AMD 5th Gen EPYC 9005 Turin vs. NVIDIA GH200 Performance Benchmarks

AMD 5th Gen EPYC "Turin" Linux benchmarks by Michael Larabel for a future article on launch day of EPYC 9005 series Linux performance.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2410298-NE-NVIDIAGRA46
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AV1 2 Tests
BLAS (Basic Linear Algebra Sub-Routine) Tests 3 Tests
C++ Boost Tests 5 Tests
Timed Code Compilation 5 Tests
C/C++ Compiler Tests 11 Tests
Compression Tests 3 Tests
CPU Massive 20 Tests
Creator Workloads 18 Tests
Cryptography 4 Tests
Database Test Suite 3 Tests
Encoding 5 Tests
Fortran Tests 6 Tests
Game Development 5 Tests
HPC - High Performance Computing 21 Tests
Imaging 3 Tests
Common Kernel Benchmarks 2 Tests
LAPACK (Linear Algebra Pack) Tests 2 Tests
Machine Learning 3 Tests
Molecular Dynamics 9 Tests
MPI Benchmarks 10 Tests
Multi-Core 33 Tests
NVIDIA GPU Compute 3 Tests
Intel oneAPI 6 Tests
OpenMPI Tests 16 Tests
Programmer / Developer System Benchmarks 8 Tests
Python 2 Tests
Raytracing 2 Tests
Renderers 4 Tests
Scientific Computing 12 Tests
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Single-Threaded 2 Tests
Video Encoding 5 Tests
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EPYC 9965
October 04
  11 Hours, 35 Minutes
EPYC 9575F
October 02
  10 Hours, 4 Minutes
NVIDIA GH200 144G HBM3e
October 28
  7 Hours, 42 Minutes
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  9 Hours, 47 Minutes

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EPYC 9965

Processor: AMD EPYC 9965 192-Core @ 2.25GHz (192 Cores / 384 Threads), Motherboard: AMD VOLCANO (RVOT1000D BIOS), Chipset: AMD Device 153a, Memory: 12 x 64GB DDR5-6000MT/s Samsung M321R8GA0PB1-CCPKC, Disk: 2 x 1920GB KIOXIA KCD8XPUG1T92, Graphics: ASPEED, Network: Broadcom NetXtreme BCM5720 PCIe

OS: Ubuntu 24.04, Kernel: 6.10.0-phx (x86_64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xb101018
Java Notes: OpenJDK Runtime Environment (build 21.0.3-ea+7-Ubuntu-1build1)
Python Notes: Python 3.12.2
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected

EPYC 9575F

Changed Processor to AMD EPYC 9575F 64-Core @ 3.30GHz (64 Cores / 128 Threads).

Processor Change: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xb002110

NVIDIA GH200 144G HBM3e

Processor: ARMv8 Neoverse-V2 @ 3.47GHz (72 Cores), Motherboard: Pegatron JIMBO P4352 (00022432 BIOS), Memory: 1 x 480GB LPDDR5-6400MT/s NVIDIA 699-2G530-0236-RC1, Disk: 1000GB CT1000T700SSD3, Graphics: NVIDIA GH200 144G HBM3e 143GB, Network: 2 x Intel X550

OS: Ubuntu 24.04, Kernel: 6.8.0-47-generic-64k (aarch64), Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.6.65, Compiler: GCC 13.2.0 + CUDA 11.8, File-System: ext4, Screen Resolution: 1920x1200

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-fix-cortex-a53-843419 --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-dIwDw0/gcc-13-13.2.0/debian/tmp-nvptx/usr --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --program-prefix=aarch64-linux-gnu- --target=aarch64-linux-gnu --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-target-system-zlib=auto --without-cuda-driver -v
Processor Notes: Scaling Governor: cppc_cpufreq performance (Boost: Disabled)
Python Notes: Python 3.12.6
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Not affected + srbds: Not affected + tsx_async_abort: Not affected

AMD 5th Gen EPYC 9005 Turin vs. NVIDIA GH200 Performance BenchmarksProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelCompilerFile-SystemScreen ResolutionDisplay DriverOpenCLEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3eAMD EPYC 9965 192-Core @ 2.25GHz (192 Cores / 384 Threads)AMD VOLCANO (RVOT1000D BIOS)AMD Device 153a12 x 64GB DDR5-6000MT/s Samsung M321R8GA0PB1-CCPKC2 x 1920GB KIOXIA KCD8XPUG1T92ASPEEDBroadcom NetXtreme BCM5720 PCIeUbuntu 24.046.10.0-phx (x86_64)GCC 13.2.0ext41920x1200AMD EPYC 9575F 64-Core @ 3.30GHz (64 Cores / 128 Threads)ARMv8 Neoverse-V2 @ 3.47GHz (72 Cores)Pegatron JIMBO P4352 (00022432 BIOS)1 x 480GB LPDDR5-6400MT/s NVIDIA 699-2G530-0236-RC11000GB CT1000T700SSD3NVIDIA GH200 144G HBM3e 143GB2 x Intel X5506.8.0-47-generic-64k (aarch64)NVIDIAOpenCL 3.0 CUDA 12.6.65GCC 13.2.0 + CUDA 11.8OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseCompiler Details- EPYC 9965: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - EPYC 9575F: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - NVIDIA GH200 144G HBM3e: --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-fix-cortex-a53-843419 --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-dIwDw0/gcc-13-13.2.0/debian/tmp-nvptx/usr --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --program-prefix=aarch64-linux-gnu- --target=aarch64-linux-gnu --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-target-system-zlib=auto --without-cuda-driver -v Processor Details- EPYC 9965: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xb101018- EPYC 9575F: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xb002110- NVIDIA GH200 144G HBM3e: Scaling Governor: cppc_cpufreq performance (Boost: Disabled)Java Details- EPYC 9965, EPYC 9575F: OpenJDK Runtime Environment (build 21.0.3-ea+7-Ubuntu-1build1)Python Details- EPYC 9965: Python 3.12.2- EPYC 9575F: Python 3.12.2- NVIDIA GH200 144G HBM3e: Python 3.12.6Security Details- EPYC 9965: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected - EPYC 9575F: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected - NVIDIA GH200 144G HBM3e: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Not affected + srbds: Not affected + tsx_async_abort: Not affected

EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3eLogarithmic Result OverviewPhoronix Test SuiteJohn The RipperminiBUDEMemcachedOpenSSLNAS Parallel BenchmarksGROMACSXmrigStockfishLiquid-DSPASKAPsrsRAN ProjectASTC Encoderlibavif avifenc7-Zip CompressionQuantLibSpeedbOpenVINOPrimesieveRocksDBParallel BZIP2 CompressionSecureMarkPennantCoremarkTimed Node.js CompilationTimed LLVM CompilationHelsingZstd CompressionWebP Image EncodeHigh Performance Conjugate GradientPyBenchTimed Godot Game Engine CompilationminiFEAlgebraic Multi-Grid BenchmarkNumpy BenchmarkClickHouseQMCPACKCloverLeafGraphicsMagickNWChemTimed Gem5 CompilationOpenFOAMLAMMPS Molecular Dynamics SimulatorXcompact3d Incompact3dTimed Linux Kernel Compilation

EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3eLogarithmic Per Watt Result OverviewPhoronix Test SuiteminiBUDEJohn The RipperNAS Parallel BenchmarksGROMACSASTC EncoderStockfishOpenSSLSpeedbLiquid-DSPASKAP7-Zip CompressionClickHouseRocksDBNumpy BenchmarkQuantLibLAMMPS Molecular Dynamics SimulatorGraphicsMagickWebP Image EncodeZstd CompressionXmrigCoremarkAlgebraic Multi-Grid BenchmarkHigh Performance Conjugate GradientMemcachedSecureMarksrsRAN ProjectminiFEP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.MP.W.G.M

AMD 5th Gen EPYC 9005 Turin vs. NVIDIA GH200 Performance Benchmarksopenssl: ChaCha20-Poly1305liquid-dsp: 128 - 256 - 512openvino: Vehicle Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUliquid-dsp: 1 - 256 - 512openvino: Face Detection Retail FP16-INT8 - CPUminibude: OpenMP - BM2minibude: OpenMP - BM2npb: LU.Copenvino: Road Segmentation ADAS FP16-INT8 - CPUnpb: SP.Copenvino: Noise Suppression Poconet-Like FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUbrl-cad: VGR Performance Metricopenssl: RSA4096john-the-ripper: bcryptjohn-the-ripper: Blowfishopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUsrsran: PUSCH Processor Benchmark, Throughput Totalmemcached: 1:100askap: tConvolve MPI - Griddingopenssl: AES-256-GCMopenssl: AES-128-GCMopenvino: Face Detection FP16-INT8 - CPUnpb: IS.Dopenvino: Machine Translation EN To DE FP16 - CPUgromacs: MPI CPU - water_GMX50_barestockfish: Chess Benchmarkopenssl: RSA4096liquid-dsp: 256 - 256 - 57askap: tConvolve MPI - Degriddingopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Road Segmentation ADAS FP16-INT8 - CPUjohn-the-ripper: WPA PSKopenvino: Person Vehicle Bike Detection FP16 - CPUsrsran: PUSCH Processor Benchmark, Throughput Threadastcenc: Very Thoroughastcenc: Exhaustiveastcenc: Thoroughavifenc: 0openssl: SHA256compress-7zip: Decompression Ratingcompress-7zip: Compression Ratingquantlib: Multi-Threadedospray-studio: 1 - 4K - 32 - Path Tracer - CPUopenfoam: drivaerFastback, Medium Mesh Size - Execution Timeospray-studio: 3 - 4K - 32 - Path Tracer - CPUopenvino: Person Detection FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUliquid-dsp: 256 - 256 - 512openvino: Face Detection Retail FP16-INT8 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Person Detection FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Noise Suppression Poconet-Like FP16 - CPUopenssl: ChaCha20primesieve: 1e13embree: Pathtracer ISPC - Asian Dragon Objembree: Pathtracer ISPC - Asian Dragonopenssl: SHA512openfoam: drivaerFastback, Medium Mesh Size - Mesh Timeprimesieve: 1e12ospray-studio: 3 - 4K - 16 - Path Tracer - CPUospray-studio: 3 - 4K - 1 - Path Tracer - CPUospray-studio: 1 - 4K - 1 - Path Tracer - CPUospray-studio: 1 - 4K - 16 - Path Tracer - CPUrocksdb: Rand Readembree: Pathtracer ISPC - Crowncompress-pbzip2: FreeBSD-13.0-RELEASE-amd64-memstick.img Compressionblender: Classroom - CPU-Onlyblender: Barbershop - CPU-Onlyspeedb: Rand Readsecuremark: SecureMark-TLSospray: gravity_spheres_volume/dim_512/pathtracer/real_timeliquid-dsp: 256 - 256 - 32blender: BMW27 - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlyopenvkl: vklBenchmarkCPU ISPCcoremark: CoreMark Size 666 - Iterations Per Seconduvg266: Bosphorus 4K - Slowblender: Junkshop - CPU-Onlyblender: Fishy Cat - CPU-Onlybuild-nodejs: Time To Compileuvg266: Bosphorus 4K - Mediumkvazaar: Bosphorus 4K - Slowbuild-llvm: Ninjaospray: particle_volume/scivis/real_timeospray: particle_volume/ao/real_timepennant: sedovbigcompress-zstd: 19 - Compression Speedkvazaar: Bosphorus 4K - Mediumcompress-zstd: 19 - Decompression Speedhelsing: 14 digitwebp: Quality 100, Lossless, Highest Compressionliquid-dsp: 1 - 256 - 32hpcg: 144 144 144 - 60compress-zstd: 19, Long Mode - Decompression Speedpybench: Total For Average Test Timesnamd: ATPase with 327,506 Atomsbuild-godot: Time To Compilegraphics-magick: Sharpenminife: Smallopenfoam: drivaerFastback, Small Mesh Size - Execution Timegraphics-magick: Enhancedamg: lammps: Rhodopsin Proteinclickhouse: 100M Rows Hits Dataset, Second Runnumpy: compress-zstd: 19, Long Mode - Compression Speedgraphics-magick: Noise-Gaussianqmcpack: Li2_STO_aeclickhouse: 100M Rows Hits Dataset, Third Runclickhouse: 100M Rows Hits Dataset, First Run / Cold Cachecloverleaf: clover_bm64_shortoidn: RTLightmap.hdr.4096x4096 - CPU-Onlyoidn: RT.hdr_alb_nrm.3840x2160 - CPU-Onlyoidn: RT.ldr_alb_nrm.3840x2160 - CPU-Onlynamd: STMV with 1,066,628 Atomsffmpeg: libx265 - Platformnwchem: C240 Buckyballbuild-gem5: Time To Compileopenradioss: Cell Phone Drop Testsvt-av1: Preset 4 - Bosphorus 4Kincompact3d: X3D-benchmarking input.i3dlammps: 20k Atomsbuild-linux-kernel: allmodconfigopenradioss: Chrysler Neon 1Msvt-av1: Preset 12 - Bosphorus 4Kuvg266: Bosphorus 4K - Very Fasttensorflow: CPU - 256 - ResNet-50svt-av1: Preset 8 - Bosphorus 4Kopenradioss: INIVOL and Fluid Structure Interaction Drop Containergpaw: Carbon Nanotubetensorflow: CPU - 512 - ResNet-50tensorflow: CPU - 256 - AlexNetopenfoam: drivaerFastback, Small Mesh Size - Mesh Timexmrig: GhostRider - 1Msvt-av1: Preset 13 - Bosphorus 4Kluxcorerender: Orange Juice - CPUluxcorerender: DLSC - CPUnpb: CG.Cnpb: MG.Cpennant: leblancbigrelion: Basic - CPUspeedb: Read While WritingEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e1053141452663228170000012591.548.382052775037263.7410701.175428.047313412.103497.17152747.8710251.078.63834839085251.038647438608222159.64208220.838940.99290017.4644845.222832805930232512760030913231.147503.0178.0723.1423620071783351713.3818580000046390.57.5227.34177466711033.61129.618.771311.5312128.756253.26522361364029010018581007150566481.417091176.3980620149640.090.7331816333334.97414.32149.811227.2310.88155100342123715.976230.9736267.698687509206173160.682871.396100676325358530958354064210.40020.89123820.3174.2496989073936553364.800080642666678.2025.6742414247085.81199350.9611.2611.02117.82255.6968.6199.43361.773761.68733.80696519.468.821520.539.7650.613989600060.70371469.064619.8993691.05344565294.525.336945495297634500055.019587.17723.819.2929694.591598.32576.7624.113.226.736.745.0318560.111594140.59322.0310.015196.31866566.773248.28592.04248.69770.48201.12104.63877.7627.560247.222085.0215447.6244.57535.2224.0251319.46112588.992.379513169.6982394001549926015231018229333337427.274.62827466721512.076101.793244.072303092.492198.48148567.257401.315.52140649045479.719907819895413533.20173795.434804.713808558.4248448.011936992449901289299058233141.437043.1841.9414.6511821359671673091.2544260000043732.64.2414.418577025717.19224.510.11976.203572.377340.851113637325283527037655241297665.135274233.115141324650.800.2518179333332.85225.8649.09761.698.3873306028605325.397117.9044137.40844556808188795.091062.123191961203101516174505733525111.28501.26340838.21138.3652667179249145135.6523526586666714.6345.7523883884226.12849728.8219.8419.12133.85732.6040.32110.52936.445536.40326.22110825.541.302081.261.8270.835380166763.90051970.848213.3351683.43530169114.425.60424336319761125042.850809.27962.2811.829172.933809.87779.3121.122.405.045.063.8031379.531225.7130.79917.6412.457217.38342354.786203.796109.72294.41182.89234.68121.05189.6629.874254.022050.6311601.9283.70222.2615.3665470.23159641.783.238866223.80710783281108427679423248263333153.4738.633458733519.021366.39554.65644257.1551.0724153.15199.4832.8915332.57474974756465.824896.131848.23049705.5410933.85156805063106091225212706.561931.21161.986.002981468581055054.5266973333315401.6117.25351.9076130547.01756.58854.066046.4120108.21386724440007424643436600261141.5362.2671232.713.6725363000034.662708.94549.25110.9890.2014925516954331.4644949067647883.9775092.6675683394631.68279756902111126689945024000002396678.996252202.937168.9696.43030415.21266.656.9720.546079033342.32251305.9724123.99642846786.337.373806369225645466759.841594.01699.308.6439769.609596.49581.5928.411214.7166.097240.28580258.143246.66016.739174015.527521.8763701.224.47377910241042OpenBenchmarking.org

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.3Algorithm: ChaCha20-Poly1305EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e200000M400000M600000M800000M1000000MSE +/- 170992251.22, N = 3SE +/- 312635540.30, N = 3SE +/- 3328662.15, N = 31053141452663499260152310108427679423-m64-m641. (CC) gcc options: -pthread -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.3Algorithm: ChaCha20-Poly1305EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e500M1000M1500M2000M2500M2506574135.731303373595.94535429561.19
MinAvgMaxNVIDIA GH200 144G HBM3e41.5202.5225.2EPYC 9575F38.3383.1400.8EPYC 996538.8420.2459.2OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.3CPU Power Consumption Monitor1202403604806001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 37464 Joules per run.2. EPYC 9575F: Approximate power consumption of 70099 Joules per run.3. EPYC 9965: Approximate power consumption of 77028 Joules per run.

Liquid-DSP

LiquidSDR's Liquid-DSP is a software-defined radio (SDR) digital signal processing library. This test profile runs a multi-threaded benchmark of this SDR/DSP library focused on embedded platform usage. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 1.6Threads: 128 - Buffer Length: 256 - Filter Length: 512EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e500M1000M1500M2000M2500MSE +/- 1855622.09, N = 3SE +/- 1785434.90, N = 3SE +/- 488683.04, N = 3228170000018229333332482633331. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 1.6Threads: 128 - Buffer Length: 256 - Filter Length: 512EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e1.4M2.8M4.2M5.6M7M6644407.925541653.371396900.82
MinAvgMaxNVIDIA GH200 144G HBM3e38.7177.7203.0EPYC 9575F38.4329.0377.4EPYC 996538.8343.4397.5OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 1.6CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 6220 Joules per run.2. EPYC 9575F: Approximate power consumption of 11294 Joules per run.3. EPYC 9965: Approximate power consumption of 11790 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Vehicle Detection FP16-INT8 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e3K6K9K12K15KSE +/- 17.14, N = 3SE +/- 12.36, N = 3SE +/- 0.10, N = 312591.547427.27153.471. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Weld Porosity Detection FP16-INT8 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e918273645SE +/- 0.00, N = 3SE +/- 0.00, N = 3SE +/- 0.07, N = 34.608.3838.63MIN: 2.36 / MAX: 11.16MIN: 2.46 / MAX: 47.2MIN: 23.06 / MAX: 413.241. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e39.8122.4132.5EPYC 9575F38.8330.6355.4EPYC 996538.7333.5358.9OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 7998 Joules per run.2. EPYC 9575F: Approximate power consumption of 21159 Joules per run.3. EPYC 9965: Approximate power consumption of 21453 Joules per run.

Liquid-DSP

LiquidSDR's Liquid-DSP is a software-defined radio (SDR) digital signal processing library. This test profile runs a multi-threaded benchmark of this SDR/DSP library focused on embedded platform usage. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 1.6Threads: 1 - Buffer Length: 256 - Filter Length: 512EPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e6M12M18M24M30MSE +/- 666.67, N = 3SE +/- 244710.59, N = 12SE +/- 33.33, N = 3282746672052775034587331. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 1.6Threads: 1 - Buffer Length: 256 - Filter Length: 512EPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e70K140K210K280K350K325399.16266440.1482973.28
MinAvgMaxNVIDIA GH200 144G HBM3e38.841.742.9EPYC 996537.977.082.5EPYC 9575F38.186.994.0OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 1.6CPU Power Consumption Monitor204060801001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 1445 Joules per run.2. EPYC 9965: Approximate power consumption of 2620 Joules per run.3. EPYC 9575F: Approximate power consumption of 2954 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Face Detection Retail FP16-INT8 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e8K16K24K32K40KSE +/- 31.19, N = 3SE +/- 45.20, N = 3SE +/- 0.10, N = 337263.7421512.07519.021. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

miniBUDE

MiniBUDE is a mini application for the the core computation of the Bristol University Docking Engine (BUDE). This test profile currently makes use of the OpenMP implementation of miniBUDE for CPU benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFInst/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM2EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e2K4K6K8K10KSE +/- 47.25, N = 4SE +/- 15.38, N = 3SE +/- 0.49, N = 310701.186101.791366.40-march=native-march=native1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -lm

OpenBenchmarking.orgBillion Interactions/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM2EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e90180270360450SE +/- 1.89, N = 4SE +/- 0.62, N = 3SE +/- 0.02, N = 3428.05244.0754.66-march=native-march=native-mcpu=native1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -lm
OpenBenchmarking.orgBillion Interactions/s Per Watt, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM2EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e0.32580.65160.97741.30321.6291.4480.7630.255
MinAvgMaxNVIDIA GH200 144G HBM3e41.1214.0244.9EPYC 996537.8295.7388.6EPYC 9575F38.3319.7382.8OpenBenchmarking.orgWatts, Fewer Is BetterminiBUDE 20210901CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 21253 Joules per run.2. EPYC 9965: Approximate power consumption of 4805 Joules per run.3. EPYC 9575F: Approximate power consumption of 8100 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: LU.CEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e70K140K210K280K350KSE +/- 3237.26, N = 5SE +/- 1780.21, N = 6SE +/- 235.16, N = 3313412.10303092.4944257.151. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.6
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: LU.CEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e300600900120015001309.071157.05204.41
MinAvgMaxNVIDIA GH200 144G HBM3e41.1216.5254.9EPYC 996538.5239.4392.4EPYC 9575F38.5262.0399.8OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 11331 Joules per run.2. EPYC 9965: Approximate power consumption of 3112 Joules per run.3. EPYC 9575F: Approximate power consumption of 3012 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Road Segmentation ADAS FP16-INT8 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e8001600240032004000SE +/- 6.65, N = 3SE +/- 1.81, N = 3SE +/- 0.02, N = 33497.172198.4851.071. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.CEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e30K60K90K120K150KSE +/- 1227.63, N = 4SE +/- 376.98, N = 5SE +/- 60.46, N = 3152747.87148567.2524153.151. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.6
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.CEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e120240360480600566.79518.54105.21
MinAvgMaxNVIDIA GH200 144G HBM3e41.3229.6257.1EPYC 996538.1269.5413.1EPYC 9575F38.2286.5399.7OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 15151 Joules per run.2. EPYC 9965: Approximate power consumption of 4447 Joules per run.3. EPYC 9575F: Approximate power consumption of 4183 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Noise Suppression Poconet-Like FP16 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e2K4K6K8K10KSE +/- 13.78, N = 3SE +/- 4.59, N = 3SE +/- 0.46, N = 310251.077401.31199.481. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Person Vehicle Bike Detection FP16 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e816243240SE +/- 0.01, N = 3SE +/- 0.02, N = 3SE +/- 0.04, N = 35.528.6332.89MIN: 3.58 / MAX: 12.72MIN: 4.74 / MAX: 37.97MIN: 19.5 / MAX: 53.281. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e41.2148.9161.7EPYC 9575F38.6297.8322.4EPYC 996538.7350.2386.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 9776 Joules per run.2. EPYC 9575F: Approximate power consumption of 19256 Joules per run.3. EPYC 9965: Approximate power consumption of 23230 Joules per run.

BRL-CAD

BRL-CAD is a cross-platform, open-source solid modeling system with built-in benchmark mode. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgVGR Performance Metric, More Is BetterBRL-CAD 7.38.2VGR Performance MetricEPYC 9965EPYC 9575F2M4M6M8M10M834839014064901. (CXX) g++ options: -std=c++17 -pipe -fvisibility=hidden -fno-strict-aliasing -fno-common -fexceptions -ftemplate-depth-128 -m64 -ggdb3 -O3 -fipa-pta -fstrength-reduce -finline-functions -flto -ltcl8.6 -lnetpbm -lregex_brl -lz_brl -lassimp -ldl -lm -ltk8.6
OpenBenchmarking.orgVGR Performance Metric Per Watt, More Is BetterBRL-CAD 7.38.2VGR Performance MetricEPYC 9965EPYC 9575F20K40K60K80K100K83197.803698.57
OpenBenchmarking.orgWatts, Fewer Is BetterBRL-CAD 7.38.2CPU Power Consumption MonitorEPYC 9965EPYC 9575F70140210280350Min: 38.41 / Avg: 100.34 / Max: 250.17Min: 38.36 / Avg: 380.28 / Max: 401.28

VGR Performance Metric

NVIDIA GH200 144G HBM3e: The test quit with a non-zero exit status. E: ERROR: Could not find the BRL-CAD raytracer

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsign/s, More Is BetterOpenSSL 3.3Algorithm: RSA4096EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e20K40K60K80K100KSE +/- 46.31, N = 3SE +/- 116.72, N = 3SE +/- 20.10, N = 385251.045479.715332.5-m64-m641. (CC) gcc options: -pthread -O3 -lssl -lcrypto -ldl

John The Ripper

This is a benchmark of John The Ripper, which is a password cracker. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgReal C/S, More Is BetterJohn The Ripper 2023.03.14Test: bcryptEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e80K160K240K320K400KSE +/- 8.33, N = 3SE +/- 77.93, N = 3SE +/- 31.26, N = 338647419907874749-m64 -lgmp -lbz2-m64 -lgmp -lbz21. (CC) gcc options: -lssl -lcrypto -fopenmp -lm -lrt -lz -ldl -lcrypt
OpenBenchmarking.orgReal C/S Per Watt, More Is BetterJohn The Ripper 2023.03.14Test: bcryptEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e20040060080010001127.52561.71517.44

OpenBenchmarking.orgReal C/S, More Is BetterJohn The Ripper 2023.03.14Test: BlowfishEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e80K160K240K320K400KSE +/- 201.40, N = 3SE +/- 41.32, N = 3SE +/- 19.86, N = 338608219895474756-m64 -lgmp -lbz2-m64 -lgmp -lbz21. (CC) gcc options: -lssl -lcrypto -fopenmp -lm -lrt -lz -ldl -lcrypt
OpenBenchmarking.orgReal C/S Per Watt, More Is BetterJohn The Ripper 2023.03.14Test: BlowfishEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e20040060080010001128.50562.77508.72
MinAvgMaxNVIDIA GH200 144G HBM3e41.0144.5164.9EPYC 996538.4341.7386.7EPYC 9575F38.4353.5400.3Xeon 6980P50.8445.3539.8Xeon 6980P - DDR5-640050.4446.6539.6OpenBenchmarking.orgWatts, Fewer Is BetterJohn The Ripper 2023.03.14CPU Power Consumption Monitor1402804205607001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 5104 Joules per run.2. EPYC 9965: Approximate power consumption of 11617 Joules per run.3. EPYC 9575F: Approximate power consumption of 12020 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Weld Porosity Detection FP16-INT8 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e5K10K15K20K25KSE +/- 6.08, N = 3SE +/- 5.04, N = 3SE +/- 0.86, N = 322159.6413533.20465.821. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e40K80K120K160K200KSE +/- 2422.71, N = 3SE +/- 689.22, N = 3SE +/- 6.97, N = 3208220.83173795.434896.131. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

srsRAN Project

srsRAN Project is a complete ORAN-native 5G RAN solution created by Software Radio Systems (SRS). The srsRAN Project radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMbps, More Is BettersrsRAN Project 23.10.1-20240325Test: PUSCH Processor Benchmark, Throughput TotalEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e2K4K6K8K10KSE +/- 0.19, N = 3SE +/- 0.56, N = 3SE +/- 0.06, N = 38940.94804.71848.2-march=native -mavx2 -mavx -msse4.1 -mfma -mavx512f -mavx512cd -mavx512bw -mavx512dq - MIN: 7191.5 / MAX: 8941.3-march=native -mavx2 -mavx -msse4.1 -mfma -mavx512f -mavx512cd -mavx512bw -mavx512dq - MIN: 3396.5 / MAX: 4805.8MIN: 1042.6 / MAX: 1848.31. (CXX) g++ options: -O3 -fno-trapping-math -fno-math-errno -ldl
OpenBenchmarking.orgMbps Per Watt, More Is BettersrsRAN Project 23.10.1-20240325Test: PUSCH Processor Benchmark, Throughput TotalEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e71421283528.6217.9316.72
MinAvgMaxNVIDIA GH200 144G HBM3e41.3110.5134.1EPYC 9575F38.6268.0361.7EPYC 996539.0312.4368.2OpenBenchmarking.orgWatts, Fewer Is BettersrsRAN Project 23.10.1-20240325CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 3758 Joules per run.2. EPYC 9575F: Approximate power consumption of 6075 Joules per run.3. EPYC 9965: Approximate power consumption of 10204 Joules per run.

Memcached

Memcached is a high performance, distributed memory object caching system. This Memcached test profiles makes use of memtier_benchmark for excuting this CPU/memory-focused server benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOps/sec, More Is BetterMemcached 1.6.19Set To Get Ratio: 1:100EPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e3M6M9M12M15MSE +/- 30489.72, N = 3SE +/- 70542.57, N = 3SE +/- 22880.28, N = 313808558.429290017.463049705.541. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
OpenBenchmarking.orgOps/sec Per Watt, More Is BetterMemcached 1.6.19Set To Get Ratio: 1:100EPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e9K18K27K36K45K39869.2735232.5927521.07
MinAvgMaxNVIDIA GH200 144G HBM3e39.5110.8124.9EPYC 996537.9263.7306.3EPYC 9575F38.3346.3402.0OpenBenchmarking.orgWatts, Fewer Is BetterMemcached 1.6.19CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 7831 Joules per run.2. EPYC 9965: Approximate power consumption of 18545 Joules per run.3. EPYC 9575F: Approximate power consumption of 24244 Joules per run.

ASKAP

ASKAP is a set of benchmarks from the Australian SKA Pathfinder. The principal ASKAP benchmarks are the Hogbom Clean Benchmark (tHogbomClean) and Convolutional Resamping Benchmark (tConvolve) as well as some previous ASKAP benchmarks being included as well for OpenCL and CUDA execution of tConvolve. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMpix/sec, More Is BetterASKAP 1.0Test: tConvolve MPI - GriddingEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e10K20K30K40K50KSE +/- 369.83, N = 3SE +/- 526.44, N = 13SE +/- 58.46, N = 348448.044845.210933.81. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp
OpenBenchmarking.orgMpix/sec Per Watt, More Is BetterASKAP 1.0Test: tConvolve MPI - GriddingEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e306090120150146.79144.3672.88
MinAvgMaxNVIDIA GH200 144G HBM3e41.4150.0265.8EPYC 996538.2310.7396.7EPYC 9575F38.3330.0400.0OpenBenchmarking.orgWatts, Fewer Is BetterASKAP 1.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 7052 Joules per run.2. EPYC 9965: Approximate power consumption of 20670 Joules per run.3. EPYC 9575F: Approximate power consumption of 8141 Joules per run.

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.3Algorithm: AES-256-GCMEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e500000M1000000M1500000M2000000M2500000MSE +/- 3767964201.90, N = 3SE +/- 947677989.11, N = 3SE +/- 82190365.25, N = 322832805930231193699244990515680506310-m64-m641. (CC) gcc options: -pthread -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.3Algorithm: AES-256-GCMEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e1300M2600M3900M5200M6500M5989469249.573325699800.961809281985.06
MinAvgMaxNVIDIA GH200 144G HBM3e43.5285.0293.6EPYC 9575F38.4358.9382.8EPYC 996538.4381.2412.5OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.3CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 52634 Joules per run.2. EPYC 9575F: Approximate power consumption of 65685 Joules per run.3. EPYC 9965: Approximate power consumption of 69763 Joules per run.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.3Algorithm: AES-128-GCMEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e500000M1000000M1500000M2000000M2500000MSE +/- 1643367333.46, N = 3SE +/- 1089753853.94, N = 3SE +/- 121548246.52, N = 325127600309131289299058233609122521270-m64-m641. (CC) gcc options: -pthread -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.3Algorithm: AES-128-GCMEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e1400M2800M4200M5600M7000M6477196397.293601051103.712139935369.45
MinAvgMaxNVIDIA GH200 144G HBM3e41.2284.6293.2EPYC 9575F38.0358.0377.9EPYC 996538.5387.9419.8OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.3CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 52564 Joules per run.2. EPYC 9575F: Approximate power consumption of 65640 Joules per run.3. EPYC 9965: Approximate power consumption of 70993 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Face Detection FP16-INT8 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e50100150200250SE +/- 0.69, N = 3SE +/- 0.09, N = 3SE +/- 0.00, N = 3231.14141.436.561. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: IS.DEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e16003200480064008000SE +/- 113.41, N = 15SE +/- 39.21, N = 6SE +/- 5.02, N = 37503.017043.181931.211. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.6
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: IS.DEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e91827364540.8330.3114.06
MinAvgMaxNVIDIA GH200 144G HBM3e42.5137.4235.8EPYC 996537.8183.8364.4EPYC 9575F38.2232.3399.8OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 3434 Joules per run.2. EPYC 9965: Approximate power consumption of 2169 Joules per run.3. EPYC 9575F: Approximate power consumption of 2556 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Machine Translation EN To DE FP16 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e4080120160200SE +/- 0.02, N = 3SE +/- 0.12, N = 3SE +/- 0.04, N = 341.9478.07161.98MIN: 20.28 / MAX: 62.17MIN: 33.39 / MAX: 161.89MIN: 141.94 / MAX: 399.191. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e41.2160.2178.5EPYC 996538.8364.4410.0EPYC 9575F38.4372.0401.3OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 10625 Joules per run.2. EPYC 9965: Approximate power consumption of 24053 Joules per run.3. EPYC 9575F: Approximate power consumption of 24059 Joules per run.

GROMACS

The GROMACS (GROningen MAchine for Chemical Simulations) molecular dynamics package testing with the water_GMX50 data. This test profile allows selecting between CPU and GPU-based GROMACS builds. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNs Per Day, More Is BetterGROMACS 2024Implementation: MPI CPU - Input: water_GMX50_bareEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e612182430SE +/- 0.050, N = 3SE +/- 0.053, N = 3SE +/- 0.005, N = 323.14214.6516.002-O3 -lm-O3 -lm1. (CXX) g++ options:
OpenBenchmarking.orgNs Per Day Per Watt, More Is BetterGROMACS 2024Implementation: MPI CPU - Input: water_GMX50_bareEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e0.0180.0360.0540.0720.090.0800.0460.028
MinAvgMaxNVIDIA GH200 144G HBM3e40.8212.1289.0EPYC 996537.7288.8417.2EPYC 9575F38.3315.2400.1OpenBenchmarking.orgWatts, Fewer Is BetterGROMACS 2024CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 8341 Joules per run.2. EPYC 9965: Approximate power consumption of 8182 Joules per run.3. EPYC 9575F: Approximate power consumption of 6829 Joules per run.

Stockfish

This is a test of Stockfish, an advanced open-source C++11 chess benchmark that can scale up to 1024 CPU threads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterStockfish 16.1Chess BenchmarkEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e80M160M240M320M400MSE +/- 2768351.23, N = 3SE +/- 2577463.33, N = 15SE +/- 1172057.21, N = 1536200717818213596798146858-m64 -msse -msse3 -mpopcnt -mavx2 -mbmi -mavx512f -mavx512bw -mavx512vnni -mavx512dq -mavx512vl -msse4.1 -mssse3 -msse2 -mbmi2-m64 -msse -msse3 -mpopcnt -mavx2 -mbmi -mavx512f -mavx512bw -mavx512vnni -mavx512dq -mavx512vl -msse4.1 -mssse3 -msse2 -mbmi21. (CXX) g++ options: -lgcov -lpthread -fno-exceptions -std=c++17 -fno-peel-loops -fno-tracer -pedantic -O3 -funroll-loops -flto -flto-partition=one -flto=jobserver
OpenBenchmarking.orgNodes Per Second Per Watt, More Is BetterStockfish 16.1Chess BenchmarkEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e200K400K600K800K1000K931347.77490782.75375426.89
MinAvgMaxNVIDIA GH200 144G HBM3e42.7261.4290.6EPYC 9575F38.7371.1401.2EPYC 996538.5388.7420.8OpenBenchmarking.orgWatts, Fewer Is BetterStockfish 16.1CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 18038 Joules per run.2. EPYC 9575F: Approximate power consumption of 24988 Joules per run.3. EPYC 9965: Approximate power consumption of 35889 Joules per run.

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgverify/s, More Is BetterOpenSSL 3.3Algorithm: RSA4096EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e700K1400K2100K2800K3500KSE +/- 310.57, N = 3SE +/- 743.25, N = 3SE +/- 1377.88, N = 33351713.31673091.21055054.5-m64-m641. (CC) gcc options: -pthread -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgverify/s Per Watt, More Is BetterOpenSSL 3.3Algorithm: RSA4096EPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F2K4K6K8K10K8963.315640.204395.32
MinAvgMaxNVIDIA GH200 144G HBM3e39.6187.1196.2EPYC 996538.3373.9396.9EPYC 9575F38.5380.7400.8OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.3CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 57864 Joules per run.2. EPYC 9965: Approximate power consumption of 116045 Joules per run.3. EPYC 9575F: Approximate power consumption of 116607 Joules per run.

Liquid-DSP

LiquidSDR's Liquid-DSP is a software-defined radio (SDR) digital signal processing library. This test profile runs a multi-threaded benchmark of this SDR/DSP library focused on embedded platform usage. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 1.6Threads: 256 - Buffer Length: 256 - Filter Length: 57EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e2000M4000M6000M8000M10000MSE +/- 21733844.57, N = 3SE +/- 9980647.94, N = 3SE +/- 1675642.50, N = 38185800000544260000026697333331. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 1.6Threads: 256 - Buffer Length: 256 - Filter Length: 57EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e5M10M15M20M25M23573051.7615772371.3111492978.86
MinAvgMaxNVIDIA GH200 144G HBM3e42.4232.3269.6EPYC 9575F38.4345.1391.3EPYC 996538.8347.3399.8OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 1.6CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 8208 Joules per run.2. EPYC 9575F: Approximate power consumption of 11732 Joules per run.3. EPYC 9965: Approximate power consumption of 11807 Joules per run.

ASKAP

ASKAP is a set of benchmarks from the Australian SKA Pathfinder. The principal ASKAP benchmarks are the Hogbom Clean Benchmark (tHogbomClean) and Convolutional Resamping Benchmark (tConvolve) as well as some previous ASKAP benchmarks being included as well for OpenCL and CUDA execution of tConvolve. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMpix/sec, More Is BetterASKAP 1.0Test: tConvolve MPI - DegriddingEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e10K20K30K40K50KSE +/- 455.47, N = 13SE +/- 0.00, N = 3SE +/- 33.40, N = 346390.543732.615401.61. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Vehicle Detection FP16-INT8 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e306090120150SE +/- 0.01, N = 3SE +/- 0.01, N = 3SE +/- 0.08, N = 34.247.52117.25MIN: 114.47 / MAX: 135.851. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e40.2154.0182.4EPYC 9575F38.4333.2357.4EPYC 996538.8373.2403.5OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 10113 Joules per run.2. EPYC 9575F: Approximate power consumption of 21326 Joules per run.3. EPYC 9965: Approximate power consumption of 24135 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Road Segmentation ADAS FP16-INT8 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e80160240320400SE +/- 0.01, N = 3SE +/- 0.05, N = 3SE +/- 0.04, N = 314.4127.34351.90MIN: 342.58 / MAX: 423.031. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e41.7147.9175.5EPYC 9575F38.5317.4342.3EPYC 996538.8333.6367.7OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 9909 Joules per run.2. EPYC 9575F: Approximate power consumption of 20420 Joules per run.3. EPYC 9965: Approximate power consumption of 21797 Joules per run.

John The Ripper

This is a benchmark of John The Ripper, which is a password cracker. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgReal C/S, More Is BetterJohn The Ripper 2023.03.14Test: WPA PSKEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e400K800K1200K1600K2000KSE +/- 2333.33, N = 3SE +/- 630.47, N = 3SE +/- 14.15, N = 3177466785770276130-m64 -lgmp -lbz2-m64 -lgmp -lbz21. (CC) gcc options: -lssl -lcrypto -fopenmp -lm -lrt -lz -ldl -lcrypt
OpenBenchmarking.orgReal C/S Per Watt, More Is BetterJohn The Ripper 2023.03.14Test: WPA PSKEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e100020003000400050004459.572539.28525.73
MinAvgMaxNVIDIA GH200 144G HBM3e41.0144.8161.9EPYC 9575F38.7337.8381.2EPYC 996538.3397.9449.6OpenBenchmarking.orgWatts, Fewer Is BetterJohn The Ripper 2023.03.14CPU Power Consumption Monitor1202403604806001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 5261 Joules per run.2. EPYC 9575F: Approximate power consumption of 11372 Joules per run.3. EPYC 9965: Approximate power consumption of 13530 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Person Vehicle Bike Detection FP16 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e2K4K6K8K10KSE +/- 20.79, N = 3SE +/- 12.01, N = 3SE +/- 0.71, N = 311033.615717.19547.011. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

srsRAN Project

srsRAN Project is a complete ORAN-native 5G RAN solution created by Software Radio Systems (SRS). The srsRAN Project radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMbps, More Is BettersrsRAN Project 23.10.1-20240325Test: PUSCH Processor Benchmark, Throughput ThreadEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e50100150200250SE +/- 0.00, N = 5SE +/- 0.00, N = 3SE +/- 0.00, N = 3224.5129.675.0-march=native -mavx2 -mavx -msse4.1 -mfma -mavx512f -mavx512cd -mavx512bw -mavx512dq - MIN: 158.7-march=native -mavx2 -mavx -msse4.1 -mfma -mavx512f -mavx512cd -mavx512bw -mavx512dq - MIN: 105.8MIN: 531. (CXX) g++ options: -O3 -fno-trapping-math -fno-math-errno -ldl
OpenBenchmarking.orgMbps Per Watt, More Is BettersrsRAN Project 23.10.1-20240325Test: PUSCH Processor Benchmark, Throughput ThreadEPYC 9575FNVIDIA GH200 144G HBM3eEPYC 99650.64421.28841.93262.57683.2212.8631.7971.751
MinAvgMaxNVIDIA GH200 144G HBM3e39.141.743.5EPYC 996538.374.083.7EPYC 9575F38.378.494.6OpenBenchmarking.orgWatts, Fewer Is BettersrsRAN Project 23.10.1-20240325CPU Power Consumption Monitor204060801001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 1516 Joules per run.2. EPYC 9965: Approximate power consumption of 1579 Joules per run.3. EPYC 9575F: Approximate power consumption of 1145 Joules per run.

ASTC Encoder

ASTC Encoder (astcenc) is for the Adaptive Scalable Texture Compression (ASTC) format commonly used with OpenGL, OpenGL ES, and Vulkan graphics APIs. This test profile does a coding test of both compression/decompression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMT/s, More Is BetterASTC Encoder 4.7Preset: Very ThoroughEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e510152025SE +/- 0.0042, N = 3SE +/- 0.0104, N = 3SE +/- 0.0008, N = 318.771310.11976.58851. (CXX) g++ options: -O3 -flto -pthread
OpenBenchmarking.orgMT/s Per Watt, More Is BetterASTC Encoder 4.7Preset: Very ThoroughEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e0.01690.03380.05070.06760.08450.0750.0320.030
MinAvgMaxNVIDIA GH200 144G HBM3e39.8218.7261.2EPYC 996538.1249.9374.6EPYC 9575F38.4314.2390.3OpenBenchmarking.orgWatts, Fewer Is BetterASTC Encoder 4.7CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 9697 Joules per run.2. EPYC 9965: Approximate power consumption of 5166 Joules per run.3. EPYC 9575F: Approximate power consumption of 9634 Joules per run.

OpenBenchmarking.orgMT/s, More Is BetterASTC Encoder 4.7Preset: ExhaustiveEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e3691215SE +/- 0.0016, N = 4SE +/- 0.0055, N = 3SE +/- 0.0036, N = 311.53126.20354.06601. (CXX) g++ options: -O3 -flto -pthread
OpenBenchmarking.orgMT/s Per Watt, More Is BetterASTC Encoder 4.7Preset: ExhaustiveEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e0.01040.02080.03120.04160.0520.0460.0200.018
MinAvgMaxNVIDIA GH200 144G HBM3e42.1223.2267.3EPYC 996538.5253.0377.8EPYC 9575F38.4312.4391.4OpenBenchmarking.orgWatts, Fewer Is BetterASTC Encoder 4.7CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 9747 Joules per run.2. EPYC 9965: Approximate power consumption of 5187 Joules per run.3. EPYC 9575F: Approximate power consumption of 9371 Joules per run.

OpenBenchmarking.orgMT/s, More Is BetterASTC Encoder 4.7Preset: ThoroughEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e306090120150SE +/- 0.07, N = 6SE +/- 0.06, N = 5SE +/- 0.06, N = 4128.7672.3846.411. (CXX) g++ options: -O3 -flto -pthread
OpenBenchmarking.orgMT/s Per Watt, More Is BetterASTC Encoder 4.7Preset: ThoroughEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e0.18180.36360.54540.72720.9090.8080.3290.286
MinAvgMaxEPYC 996538.2159.3364.0NVIDIA GH200 144G HBM3e39.1162.3263.1EPYC 9575F38.2219.9386.4OpenBenchmarking.orgWatts, Fewer Is BetterASTC Encoder 4.7CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 1885 Joules per run.2. NVIDIA GH200 144G HBM3e: Approximate power consumption of 2962 Joules per run.3. EPYC 9575F: Approximate power consumption of 3035 Joules per run.

libavif avifenc

This is a test of the AOMedia libavif library testing the encoding of a JPEG image to AV1 Image Format (AVIF). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is Betterlibavif avifenc 1.0Encoder Speed: 0EPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e20406080100SE +/- 0.03, N = 3SE +/- 0.08, N = 3SE +/- 0.08, N = 340.8553.27108.211. (CXX) g++ options: -O3 -fPIC -lm
MinAvgMaxNVIDIA GH200 144G HBM3e38.868.6236.5EPYC 996538.0147.9311.6EPYC 9575F38.2201.2399.6OpenBenchmarking.orgWatts, Fewer Is Betterlibavif avifenc 1.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 7757 Joules per run.2. EPYC 9965: Approximate power consumption of 8479 Joules per run.3. EPYC 9575F: Approximate power consumption of 9054 Joules per run.

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.3Algorithm: SHA256EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e50000M100000M150000M200000M250000MSE +/- 865484415.00, N = 3SE +/- 38504291.58, N = 3SE +/- 97000421.20, N = 322361364029011363732528386724440007-m64-m641. (CC) gcc options: -pthread -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.3Algorithm: SHA256EPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F130M260M390M520M650M591912038.17440489220.25295380807.41
MinAvgMaxNVIDIA GH200 144G HBM3e41.2196.9273.1EPYC 996538.4377.8402.0EPYC 9575F38.5384.7400.8OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.3CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 36358 Joules per run.2. EPYC 9965: Approximate power consumption of 69134 Joules per run.3. EPYC 9575F: Approximate power consumption of 70531 Joules per run.

7-Zip Compression

This is a test of 7-Zip compression/decompression with its integrated benchmark feature. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMIPS, More Is Better7-Zip Compression 22.01Test: Decompression RatingEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e200K400K600K800K1000KSE +/- 150.31, N = 3SE +/- 344.02, N = 3SE +/- 414.91, N = 310018585270374246431. (CXX) g++ options: -lpthread -ldl -O2 -fPIC
OpenBenchmarking.orgMIPS Per Watt, More Is Better7-Zip Compression 22.01Test: Decompression RatingEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F70014002100280035003214.792612.521634.38
MinAvgMaxNVIDIA GH200 144G HBM3e42.0162.5246.0EPYC 996538.9311.6408.2EPYC 9575F38.4322.5400.8OpenBenchmarking.orgWatts, Fewer Is Better7-Zip Compression 22.01CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 4009 Joules per run.2. EPYC 9965: Approximate power consumption of 14439 Joules per run.3. EPYC 9575F: Approximate power consumption of 9244 Joules per run.

OpenBenchmarking.orgMIPS, More Is Better7-Zip Compression 22.01Test: Compression RatingEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e200K400K600K800K1000KSE +/- 5680.94, N = 3SE +/- 4011.15, N = 3SE +/- 652.77, N = 310071506552414366001. (CXX) g++ options: -lpthread -ldl -O2 -fPIC

QuantLib

QuantLib is an open-source library/framework around quantitative finance for modeling, trading and risk management scenarios. QuantLib is written in C++ with Boost and its built-in benchmark used reports the QuantLib Benchmark Index benchmark score. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMFLOPS, More Is BetterQuantLib 1.32Configuration: Multi-ThreadedEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e120K240K360K480K600KSE +/- 485.45, N = 3SE +/- 661.19, N = 3SE +/- 316.93, N = 3566481.4297665.1261141.51. (CXX) g++ options: -O3 -march=native -fPIE -pie
OpenBenchmarking.orgMFLOPS Per Watt, More Is BetterQuantLib 1.32Configuration: Multi-ThreadedEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F300600900120015001606.221209.80897.44
MinAvgMaxNVIDIA GH200 144G HBM3e41.0215.9290.4EPYC 9575F38.5331.7399.4EPYC 996537.9352.7387.8OpenBenchmarking.orgWatts, Fewer Is BetterQuantLib 1.32CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 6692 Joules per run.2. EPYC 9575F: Approximate power consumption of 15479 Joules per run.3. EPYC 9965: Approximate power consumption of 25275 Joules per run.

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 1.0Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUEPYC 9965EPYC 9575F8K16K24K32K40KSE +/- 21.39, N = 3SE +/- 25.41, N = 31709135274
MinAvgMaxEPYC 9575F38.5307.8401.2EPYC 996538.3316.1391.9OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 1.0CPU Power Consumption Monitor1102203304405501. EPYC 9575F: Approximate power consumption of 13750 Joules per run.2. EPYC 9965: Approximate power consumption of 20969 Joules per run.

OpenFOAM

OpenFOAM is the leading free, open-source software for computational fluid dynamics (CFD). This test profile currently uses the drivaerFastback test case for analyzing automotive aerodynamics or alternatively the older motorBike input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Medium Mesh Size - Execution TimeEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e80160240320400176.40233.12362.27-m64 -lfiniteVolume -lmeshTools -lparallel -lregionModels-m64 -lfiniteVolume -lmeshTools -lparallel -lregionModels-mcpu=native -lfoamToVTK -ldynamicMesh -lfileFormats1. (CXX) g++ options: -std=c++14 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -llagrangian -lgenericPatchFields -lOpenFOAM -ldl -lm
MinAvgMaxNVIDIA GH200 144G HBM3e41.1200.9291.9EPYC 996538.1377.5408.6EPYC 9575F38.2388.3399.8OpenBenchmarking.orgWatts, Fewer Is BetterOpenFOAM 10CPU Power Consumption Monitor110220330440550

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 1.0Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUEPYC 9965EPYC 9575F9K18K27K36K45KSE +/- 14.95, N = 3SE +/- 11.14, N = 32014941324
MinAvgMaxEPYC 9575F38.5319.0401.2EPYC 996538.4324.3392.9OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 1.0CPU Power Consumption Monitor1102203304405501. EPYC 9575F: Approximate power consumption of 16374 Joules per run.2. EPYC 9965: Approximate power consumption of 24542 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Person Detection FP16 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e140280420560700SE +/- 0.93, N = 3SE +/- 5.55, N = 3SE +/- 0.09, N = 3650.80640.0932.711. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e0.82581.65162.47743.30324.129SE +/- 0.00, N = 3SE +/- 0.01, N = 3SE +/- 0.01, N = 30.250.733.67MIN: 0.14 / MAX: 57.97MIN: 1.08 / MAX: 40.711. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e41.0118.2127.1EPYC 9575F38.7308.2330.5EPYC 996538.7328.4358.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 7685 Joules per run.2. EPYC 9575F: Approximate power consumption of 19825 Joules per run.3. EPYC 9965: Approximate power consumption of 21344 Joules per run.

Liquid-DSP

LiquidSDR's Liquid-DSP is a software-defined radio (SDR) digital signal processing library. This test profile runs a multi-threaded benchmark of this SDR/DSP library focused on embedded platform usage. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 1.6Threads: 256 - Buffer Length: 256 - Filter Length: 512EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e700M1400M2100M2800M3500MSE +/- 1602428.71, N = 3SE +/- 1319511.69, N = 3SE +/- 165227.12, N = 3318163333318179333332536300001. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 1.6Threads: 256 - Buffer Length: 256 - Filter Length: 512EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e2M4M6M8M10M8815828.445494414.931400334.37
MinAvgMaxNVIDIA GH200 144G HBM3e42.5181.1204.9EPYC 9575F38.4330.9371.6EPYC 996538.5360.9416.7OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 1.6CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 6460 Joules per run.2. EPYC 9575F: Approximate power consumption of 11360 Joules per run.3. EPYC 9965: Approximate power consumption of 12511 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Face Detection Retail FP16-INT8 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e816243240SE +/- 0.00, N = 3SE +/- 0.00, N = 3SE +/- 0.01, N = 32.854.9734.66MIN: 1.9 / MAX: 35.75MIN: 33.13 / MAX: 44.011. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e41.3176.7194.0EPYC 9575F38.4335.2360.8EPYC 996539.2346.2374.4OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 11545 Joules per run.2. EPYC 9575F: Approximate power consumption of 21566 Joules per run.3. EPYC 9965: Approximate power consumption of 22616 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Face Detection FP16-INT8 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e6001200180024003000SE +/- 0.13, N = 3SE +/- 1.17, N = 3SE +/- 2.14, N = 3225.86414.322708.94MIN: 173.52 / MAX: 443.02MIN: 2034.06 / MAX: 5676.51. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e38.8160.7212.5EPYC 996538.5337.5378.1EPYC 9575F38.4348.4377.7OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 11572 Joules per run.2. EPYC 9965: Approximate power consumption of 23062 Joules per run.3. EPYC 9575F: Approximate power consumption of 22879 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Person Detection FP16 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e120240360480600SE +/- 0.07, N = 3SE +/- 1.29, N = 3SE +/- 1.50, N = 349.09149.81549.25MIN: 54.22 / MAX: 226.99MIN: 332.02 / MAX: 748.71. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e40.096.6136.2EPYC 996538.5358.1413.8EPYC 9575F38.7371.7400.8OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 6440 Joules per run.2. EPYC 9965: Approximate power consumption of 23872 Joules per run.3. EPYC 9575F: Approximate power consumption of 24160 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Machine Translation EN To DE FP16 - Device: CPUEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e30060090012001500SE +/- 1.87, N = 3SE +/- 0.37, N = 3SE +/- 0.03, N = 31227.23761.69110.981. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Noise Suppression Poconet-Like FP16 - Device: CPUEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e20406080100SE +/- 0.00, N = 3SE +/- 0.04, N = 3SE +/- 0.21, N = 38.3810.8890.20MIN: 6.04 / MAX: 54.33MIN: 85.88 / MAX: 141.561. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e41.0187.5212.8EPYC 9575F38.5345.0376.0EPYC 996539.1359.7403.1OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2024.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 12312 Joules per run.2. EPYC 9575F: Approximate power consumption of 22542 Joules per run.3. EPYC 9965: Approximate power consumption of 24579 Joules per run.

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.3Algorithm: ChaCha20EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e300000M600000M900000M1200000M1500000MSE +/- 312050224.99, N = 3SE +/- 24295011.71, N = 3SE +/- 3236084.95, N = 31551003421237733060286053149255169543-m64-m641. (CC) gcc options: -pthread -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.3Algorithm: ChaCha20EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e800M1600M2400M3200M4000M3660529028.051908650939.88768573496.68
MinAvgMaxNVIDIA GH200 144G HBM3e43.4194.2217.3EPYC 9575F38.6384.1401.2EPYC 996538.5423.7461.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.3CPU Power Consumption Monitor1202403604806001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 35862 Joules per run.2. EPYC 9575F: Approximate power consumption of 70413 Joules per run.3. EPYC 9965: Approximate power consumption of 77539 Joules per run.

Primesieve

Primesieve generates prime numbers using a highly optimized sieve of Eratosthenes implementation. Primesieve primarily benchmarks the CPU's L1/L2 cache performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPrimesieve 12.1Length: 1e13EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e714212835SE +/- 0.01, N = 4SE +/- 0.03, N = 3SE +/- 0.01, N = 315.9825.4031.461. (CXX) g++ options: -O3
MinAvgMaxNVIDIA GH200 144G HBM3e40.9233.0271.7EPYC 996538.5301.5387.0EPYC 9575F38.3321.0393.1OpenBenchmarking.orgWatts, Fewer Is BetterPrimesieve 12.1CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 8389 Joules per run.2. EPYC 9965: Approximate power consumption of 6030 Joules per run.3. EPYC 9575F: Approximate power consumption of 9524 Joules per run.

Embree

Intel Embree is a collection of high-performance ray-tracing kernels for execution on CPUs (and GPUs via SYCL) and supporting instruction sets such as SSE, AVX, AVX2, and AVX-512. Embree also supports making use of the Intel SPMD Program Compiler (ISPC). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian Dragon ObjEPYC 9965EPYC 9575F50100150200250SE +/- 0.14, N = 5SE +/- 0.06, N = 4230.97117.90MIN: 225.48 / MAX: 241.14MIN: 116.73 / MAX: 119.59
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian Dragon ObjEPYC 9965EPYC 9575F0.36790.73581.10371.47161.83951.6350.580
MinAvgMaxEPYC 996538.2141.3395.1EPYC 9575F38.2203.4400.4OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 4.3CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 2063 Joules per run.2. EPYC 9575F: Approximate power consumption of 3203 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian DragonEPYC 9965EPYC 9575F60120180240300SE +/- 0.09, N = 9SE +/- 0.08, N = 7267.70137.41MIN: 262.05 / MAX: 280.59MIN: 135.82 / MAX: 139.45
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian DragonEPYC 9965EPYC 9575F0.33620.67241.00861.34481.6811.4940.573
MinAvgMaxEPYC 996538.0179.2397.6EPYC 9575F38.3239.9400.8OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 4.3CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 1294 Joules per run.2. EPYC 9575F: Approximate power consumption of 2331 Joules per run.

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.3Algorithm: SHA512EPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F20000M40000M60000M80000M100000MSE +/- 148724346.57, N = 3SE +/- 557471050.78, N = 4SE +/- 40355477.92, N = 3875092061734949067647845568081887-m64-m641. (CC) gcc options: -pthread -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.3Algorithm: SHA512NVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F50M100M150M200M250M248755218.22235679194.64118475697.80
MinAvgMaxNVIDIA GH200 144G HBM3e41.1199.0243.4EPYC 996538.5371.3391.7EPYC 9575F38.6384.6400.9OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.3CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 36707 Joules per run.2. EPYC 9965: Approximate power consumption of 68073 Joules per run.3. EPYC 9575F: Approximate power consumption of 70385 Joules per run.

OpenFOAM

OpenFOAM is the leading free, open-source software for computational fluid dynamics (CFD). This test profile currently uses the drivaerFastback test case for analyzing automotive aerodynamics or alternatively the older motorBike input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Medium Mesh Size - Mesh TimeNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 9965408012016020083.9895.09160.68-mcpu=native -lfoamToVTK -ldynamicMesh -lfileFormats-m64 -lfiniteVolume -lmeshTools -lparallel -lregionModels-m64 -lfiniteVolume -lmeshTools -lparallel -lregionModels1. (CXX) g++ options: -std=c++14 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -llagrangian -lgenericPatchFields -lOpenFOAM -ldl -lm

Primesieve

Primesieve generates prime numbers using a highly optimized sieve of Eratosthenes implementation. Primesieve primarily benchmarks the CPU's L1/L2 cache performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPrimesieve 12.1Length: 1e12EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e0.60011.20021.80032.40043.0005SE +/- 0.003, N = 12SE +/- 0.003, N = 11SE +/- 0.003, N = 101.3962.1232.6671. (CXX) g++ options: -O3
MinAvgMaxNVIDIA GH200 144G HBM3e41.8111.4254.2EPYC 996538.2119.3378.8EPYC 9575F38.5146.3375.7OpenBenchmarking.orgWatts, Fewer Is BetterPrimesieve 12.1CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 769 Joules per run.2. EPYC 9965: Approximate power consumption of 636 Joules per run.3. EPYC 9575F: Approximate power consumption of 891 Joules per run.

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 1.0Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUEPYC 9965EPYC 9575F4K8K12K16K20KSE +/- 10.12, N = 3SE +/- 28.92, N = 31006719196
MinAvgMaxEPYC 996538.3302.0391.2EPYC 9575F38.3341.8401.2OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 1.0CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 16810 Joules per run.2. EPYC 9575F: Approximate power consumption of 23814 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 1.0Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUEPYC 9965EPYC 9575F30060090012001500SE +/- 0.33, N = 3SE +/- 0.33, N = 36321203
MinAvgMaxEPYC 996538.3312.8391.1EPYC 9575F38.3339.6400.8OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 1.0CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 19912 Joules per run.2. EPYC 9575F: Approximate power consumption of 22979 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 1.0Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUEPYC 9965EPYC 9575F2004006008001000SE +/- 0.33, N = 3SE +/- 2.19, N = 35351015
MinAvgMaxEPYC 996538.2311.5391.4EPYC 9575F38.5337.6401.2OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 1.0CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 19418 Joules per run.2. EPYC 9575F: Approximate power consumption of 22394 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 1.0Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUEPYC 9965EPYC 9575F3K6K9K12K15KSE +/- 7.81, N = 3SE +/- 1.76, N = 3853016174
MinAvgMaxEPYC 9575F38.5333.6401.2EPYC 996538.3335.2391.7OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 1.0CPU Power Consumption Monitor1102203304405501. EPYC 9575F: Approximate power consumption of 20241 Joules per run.2. EPYC 9965: Approximate power consumption of 30725 Joules per run.

RocksDB

This is a benchmark of Meta/Facebook's RocksDB as an embeddable persistent key-value store for fast storage based on Google's LevelDB. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterRocksDB 9.0Test: Random ReadEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F200M400M600M800M1000MSE +/- 1700402.40, N = 3SE +/- 143829.35, N = 3SE +/- 1563552.18, N = 3958354064568339463505733525-lpthread-lpthread1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti
OpenBenchmarking.orgOp/s Per Watt, More Is BetterRocksDB 9.0Test: Random ReadEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F600K1200K1800K2400K3000K2704939.662103254.001462898.91
MinAvgMaxNVIDIA GH200 144G HBM3e40.1270.2291.0EPYC 9575F38.3345.7371.1EPYC 996538.0354.3380.0OpenBenchmarking.orgWatts, Fewer Is BetterRocksDB 9.0CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 17564 Joules per run.2. EPYC 9575F: Approximate power consumption of 22125 Joules per run.3. EPYC 9965: Approximate power consumption of 22675 Joules per run.

Embree

Intel Embree is a collection of high-performance ray-tracing kernels for execution on CPUs (and GPUs via SYCL) and supporting instruction sets such as SSE, AVX, AVX2, and AVX-512. Embree also supports making use of the Intel SPMD Program Compiler (ISPC). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: CrownEPYC 9965EPYC 9575F50100150200250SE +/- 0.14, N = 8SE +/- 0.10, N = 6210.40111.29MIN: 203.25 / MAX: 221.34MIN: 109.32 / MAX: 113.75
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: CrownEPYC 9965EPYC 9575F0.23090.46180.69270.92361.15451.0260.429
MinAvgMaxEPYC 996538.0205.1399.1EPYC 9575F38.4259.5401.1OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 4.3CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 1641 Joules per run.2. EPYC 9575F: Approximate power consumption of 2855 Joules per run.

Parallel BZIP2 Compression

This test measures the time needed to compress a file (FreeBSD-13.0-RELEASE-amd64-memstick.img) using Parallel BZIP2 compression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterParallel BZIP2 Compression 1.1.13FreeBSD-13.0-RELEASE-amd64-memstick.img CompressionEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e0.37860.75721.13581.51441.893SE +/- 0.009424, N = 15SE +/- 0.009364, N = 15SE +/- 0.004252, N = 150.8912381.2634081.6827971. (CXX) g++ options: -O2 -pthread -lbz2 -lpthread
MinAvgMaxNVIDIA GH200 144G HBM3e40.068.7207.5EPYC 996538.080.5240.4EPYC 9575F38.2104.3361.5OpenBenchmarking.orgWatts, Fewer Is BetterParallel BZIP2 Compression 1.1.13CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 399 Joules per run.2. EPYC 9965: Approximate power consumption of 397 Joules per run.3. EPYC 9575F: Approximate power consumption of 556 Joules per run.

Blender

Blender is an open-source 3D creation and modeling software project. This test is of Blender's Cycles performance with various sample files. GPU computing via NVIDIA OptiX and NVIDIA CUDA is currently supported as well as HIP for AMD Radeon GPUs and Intel oneAPI for Intel Graphics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Classroom - Compute: CPU-OnlyEPYC 9965EPYC 9575F918273645SE +/- 0.04, N = 3SE +/- 0.06, N = 320.3138.21
MinAvgMaxEPYC 996538.0322.0389.8EPYC 9575F38.3349.8389.2OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.1CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 7943 Joules per run.2. EPYC 9575F: Approximate power consumption of 14927 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Barbershop - Compute: CPU-OnlyEPYC 9965EPYC 9575F306090120150SE +/- 0.08, N = 3SE +/- 0.10, N = 374.24138.36
MinAvgMaxEPYC 996538.2366.0403.4EPYC 9575F38.3378.8400.2OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.1CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 29277 Joules per run.2. EPYC 9575F: Approximate power consumption of 54175 Joules per run.

Speedb

Speedb is a next-generation key value storage engine that is RocksDB compatible and aiming for stability, efficiency, and performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Random ReadEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F200M400M600M800M1000MSE +/- 370648.73, N = 3SE +/- 126199.44, N = 3SE +/- 755684.49, N = 3969890739569021111526671792-lpthread-lpthread1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti
OpenBenchmarking.orgOp/s Per Watt, More Is BetterSpeedb 2.7Test: Random ReadEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F600K1200K1800K2400K3000K2712772.812132224.781520207.33
MinAvgMaxNVIDIA GH200 144G HBM3e42.0266.9286.5EPYC 9575F38.3346.4371.3EPYC 996538.2357.5383.6OpenBenchmarking.orgWatts, Fewer Is BetterSpeedb 2.7CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 17257 Joules per run.2. EPYC 9575F: Approximate power consumption of 22173 Joules per run.3. EPYC 9965: Approximate power consumption of 22882 Joules per run.

SecureMark

SecureMark is an objective, standardized benchmarking framework for measuring the efficiency of cryptographic processing solutions developed by EEMBC. SecureMark-TLS is benchmarking Transport Layer Security performance with a focus on IoT/edge computing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmarks, More Is BetterSecureMark 1.0.4Benchmark: SecureMark-TLSEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e110K220K330K440K550KSE +/- 602.41, N = 3SE +/- 1741.58, N = 3SE +/- 785.55, N = 34914513655332668991. (CC) gcc options: -pedantic -O3
OpenBenchmarking.orgmarks Per Watt, More Is BetterSecureMark 1.0.4Benchmark: SecureMark-TLSNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 9965140028004200560070006404.025300.984523.07
MinAvgMaxNVIDIA GH200 144G HBM3e38.941.744.4EPYC 996537.780.882.7EPYC 9575F38.192.796.6OpenBenchmarking.orgWatts, Fewer Is BetterSecureMark 1.0.4CPU Power Consumption Monitor204060801001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 12017 Joules per run.2. EPYC 9965: Approximate power consumption of 22925 Joules per run.3. EPYC 9575F: Approximate power consumption of 26422 Joules per run.

OSPRay

Intel OSPRay is a portable ray-tracing engine for high-performance, high-fidelity scientific visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgItems Per Second, More Is BetterOSPRay 3.1Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_timeEPYC 9965EPYC 9575F1428425670SE +/- 0.02, N = 3SE +/- 0.01, N = 364.8035.65
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 3.1Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_timeEPYC 9965EPYC 9575F0.04120.08240.12360.16480.2060.1830.097
MinAvgMaxEPYC 996538.1354.6403.8EPYC 9575F38.5367.4400.9OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 3.1CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 24116 Joules per run.2. EPYC 9575F: Approximate power consumption of 29635 Joules per run.

Liquid-DSP

LiquidSDR's Liquid-DSP is a software-defined radio (SDR) digital signal processing library. This test profile runs a multi-threaded benchmark of this SDR/DSP library focused on embedded platform usage. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 1.6Threads: 256 - Buffer Length: 256 - Filter Length: 32EPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e2000M4000M6000M8000M10000MSE +/- 10497036.62, N = 3SE +/- 6072982.06, N = 3SE +/- 3002221.40, N = 38064266667526586666745024000001. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 1.6Threads: 256 - Buffer Length: 256 - Filter Length: 32EPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F5M10M15M20M25M23123930.3317535441.6614781256.88
MinAvgMaxNVIDIA GH200 144G HBM3e41.2256.8292.1EPYC 996538.4348.7397.1EPYC 9575F38.4356.3401.0OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 1.6CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 9072 Joules per run.2. EPYC 9965: Approximate power consumption of 11857 Joules per run.3. EPYC 9575F: Approximate power consumption of 11994 Joules per run.

Blender

Blender is an open-source 3D creation and modeling software project. This test is of Blender's Cycles performance with various sample files. GPU computing via NVIDIA OptiX and NVIDIA CUDA is currently supported as well as HIP for AMD Radeon GPUs and Intel oneAPI for Intel Graphics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: BMW27 - Compute: CPU-OnlyEPYC 9965EPYC 9575F48121620SE +/- 0.02, N = 5SE +/- 0.06, N = 48.2014.63
MinAvgMaxEPYC 996538.3251.7384.4EPYC 9575F38.3298.8388.7OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.1CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 3222 Joules per run.2. EPYC 9575F: Approximate power consumption of 5677 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Pabellon Barcelona - Compute: CPU-OnlyEPYC 9965EPYC 9575F1020304050SE +/- 0.08, N = 3SE +/- 0.03, N = 325.6745.75
MinAvgMaxEPYC 996538.3319.9381.1EPYC 9575F38.3356.3394.4OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.1CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 9704 Joules per run.2. EPYC 9575F: Approximate power consumption of 17814 Joules per run.

OpenVKL

OpenVKL is the Intel Open Volume Kernel Library that offers high-performance volume computation kernels and part of the Intel oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 2.0.0Benchmark: vklBenchmarkCPU ISPCEPYC 9965EPYC 9575F9001800270036004500SE +/- 0.33, N = 3SE +/- 0.67, N = 342412388MIN: 349 / MAX: 46699MIN: 187 / MAX: 30690
OpenBenchmarking.orgItems / Sec Per Watt, More Is BetterOpenVKL 2.0.0Benchmark: vklBenchmarkCPU ISPCEPYC 9965EPYC 9575F369121512.5976.722
OpenBenchmarking.orgWatts, Fewer Is BetterOpenVKL 2.0.0CPU Power Consumption MonitorEPYC 9965EPYC 9575F70140210280350Min: 38.06 / Avg: 336.66 / Max: 409.43Min: 38.22 / Avg: 355.24 / Max: 401.221. EPYC 9965: Approximate power consumption of 228705 Joules per run.2. EPYC 9575F: Approximate power consumption of 240263 Joules per run.

Coremark

This is a test of EEMBC CoreMark processor benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations/Sec, More Is BetterCoremark 1.0CoreMark Size 666 - Iterations Per SecondEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e900K1800K2700K3600K4500KSE +/- 2487.95, N = 3SE +/- 4584.67, N = 3SE +/- 24238.24, N = 34247085.813884226.132396679.001. (CC) gcc options: -O2 -lrt" -lrt
OpenBenchmarking.orgIterations/Sec Per Watt, More Is BetterCoremark 1.0CoreMark Size 666 - Iterations Per SecondNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F4K8K12K16K20K20070.8416273.7512356.08
MinAvgMaxNVIDIA GH200 144G HBM3e38.8119.4159.8EPYC 996537.6261.0347.0EPYC 9575F38.0314.4399.6OpenBenchmarking.orgWatts, Fewer Is BetterCoremark 1.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 3105 Joules per run.2. EPYC 9965: Approximate power consumption of 6524 Joules per run.3. EPYC 9575F: Approximate power consumption of 8173 Joules per run.

uvg266

uvg266 is an open-source VVC/H.266 (Versatile Video Coding) encoder based on Kvazaar as part of the Ultra Video Group, Tampere University, Finland. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: SlowEPYC 9965EPYC 9575F1122334455SE +/- 0.05, N = 4SE +/- 0.00, N = 350.9628.82
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: SlowEPYC 9965EPYC 9575F0.04120.08240.12360.16480.2060.1830.090
MinAvgMaxEPYC 996538.1278.8375.6EPYC 9575F38.3319.5384.7OpenBenchmarking.orgWatts, Fewer Is Betteruvg266 0.4.1CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 4461 Joules per run.2. EPYC 9575F: Approximate power consumption of 7988 Joules per run.

Blender

Blender is an open-source 3D creation and modeling software project. This test is of Blender's Cycles performance with various sample files. GPU computing via NVIDIA OptiX and NVIDIA CUDA is currently supported as well as HIP for AMD Radeon GPUs and Intel oneAPI for Intel Graphics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Junkshop - Compute: CPU-OnlyEPYC 9965EPYC 9575F510152025SE +/- 0.06, N = 4SE +/- 0.04, N = 311.2619.84
MinAvgMaxEPYC 996538.3278.1409.7EPYC 9575F38.5323.6400.8OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.1CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 4380 Joules per run.2. EPYC 9575F: Approximate power consumption of 7767 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Fishy Cat - Compute: CPU-OnlyEPYC 9965EPYC 9575F510152025SE +/- 0.03, N = 4SE +/- 0.04, N = 311.0219.12
MinAvgMaxEPYC 996537.8271.4392.6EPYC 9575F38.4322.0398.2OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.1CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 4207 Joules per run.2. EPYC 9575F: Approximate power consumption of 7513 Joules per run.

Timed Node.js Compilation

This test profile times how long it takes to build/compile Node.js itself from source. Node.js is a JavaScript run-time built from the Chrome V8 JavaScript engine while itself is written in C/C++. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Node.js Compilation 21.7.2Time To CompileEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e4080120160200SE +/- 0.70, N = 3SE +/- 0.26, N = 3SE +/- 0.15, N = 3117.82133.86202.94
MinAvgMaxNVIDIA GH200 144G HBM3e39.2181.0279.3EPYC 996537.9257.7406.9EPYC 9575F38.6313.9401.2OpenBenchmarking.orgWatts, Fewer Is BetterTimed Node.js Compilation 21.7.2CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 38253 Joules per run.2. EPYC 9965: Approximate power consumption of 32304 Joules per run.3. EPYC 9575F: Approximate power consumption of 44045 Joules per run.

uvg266

uvg266 is an open-source VVC/H.266 (Versatile Video Coding) encoder based on Kvazaar as part of the Ultra Video Group, Tampere University, Finland. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: MediumEPYC 9965EPYC 9575F1326395265SE +/- 0.03, N = 5SE +/- 0.02, N = 355.6932.60
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: MediumEPYC 9965EPYC 9575F0.04570.09140.13710.18280.22850.2030.104
MinAvgMaxEPYC 996539.0274.3374.9EPYC 9575F38.5312.9383.4OpenBenchmarking.orgWatts, Fewer Is Betteruvg266 0.4.1CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 4114 Joules per run.2. EPYC 9575F: Approximate power consumption of 6885 Joules per run.

Kvazaar

This is a test of Kvazaar as a CPU-based H.265/HEVC video encoder written in the C programming language and optimized in Assembly. Kvazaar is the winner of the 2016 ACM Open-Source Software Competition and developed at the Ultra Video Group, Tampere University, Finland. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterKvazaar 2.2Video Input: Bosphorus 4K - Video Preset: SlowEPYC 9965EPYC 9575F1530456075SE +/- 0.16, N = 5SE +/- 0.02, N = 468.6140.321. (CC) gcc options: -pthread -ftree-vectorize -fvisibility=hidden -O2 -lpthread -lm -lrt
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterKvazaar 2.2Video Input: Bosphorus 4K - Video Preset: SlowEPYC 9965EPYC 9575F0.06350.1270.19050.2540.31750.2820.141
MinAvgMaxEPYC 996538.0243.3354.3EPYC 9575F38.4285.5368.4OpenBenchmarking.orgWatts, Fewer Is BetterKvazaar 2.2CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 3114 Joules per run.2. EPYC 9575F: Approximate power consumption of 5425 Joules per run.

Timed LLVM Compilation

This test times how long it takes to compile/build the LLVM compiler stack. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed LLVM Compilation 16.0Build System: NinjaEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e4080120160200SE +/- 0.77, N = 3SE +/- 0.08, N = 3SE +/- 0.09, N = 399.43110.53168.97
MinAvgMaxNVIDIA GH200 144G HBM3e39.3167.1239.8EPYC 996538.2254.0404.4EPYC 9575F38.4313.1401.2OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 16.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 32140 Joules per run.2. EPYC 9965: Approximate power consumption of 28707 Joules per run.3. EPYC 9575F: Approximate power consumption of 38199 Joules per run.

OSPRay

Intel OSPRay is a portable ray-tracing engine for high-performance, high-fidelity scientific visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgItems Per Second, More Is BetterOSPRay 3.1Benchmark: particle_volume/scivis/real_timeEPYC 9965EPYC 9575F1428425670SE +/- 0.05, N = 3SE +/- 0.01, N = 361.7736.45
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 3.1Benchmark: particle_volume/scivis/real_timeEPYC 9965EPYC 9575F0.04280.08560.12840.17120.2140.1900.105
MinAvgMaxEPYC 996538.2325.9379.2EPYC 9575F38.3346.8394.5OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 3.1CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 54541 Joules per run.2. EPYC 9575F: Approximate power consumption of 65436 Joules per run.

OpenBenchmarking.orgItems Per Second, More Is BetterOSPRay 3.1Benchmark: particle_volume/ao/real_timeEPYC 9965EPYC 9575F1428425670SE +/- 0.02, N = 3SE +/- 0.01, N = 361.6936.40
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 3.1Benchmark: particle_volume/ao/real_timeEPYC 9965EPYC 9575F0.03830.07660.11490.15320.19150.1700.095
MinAvgMaxEPYC 996538.4362.8379.4EPYC 9575F38.5381.7396.3OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 3.1CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 43780 Joules per run.2. EPYC 9575F: Approximate power consumption of 54580 Joules per run.

Pennant

Pennant is an application focused on hydrodynamics on general unstructured meshes in 2D. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgHydro Cycle Time - Seconds, Fewer Is BetterPennant 1.0.1Test: sedovbigEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e246810SE +/- 0.046313, N = 15SE +/- 0.034501, N = 6SE +/- 0.012489, N = 63.8069656.2211086.4303041. (CXX) g++ options: -fopenmp -lmpi_cxx -lmpi
MinAvgMaxNVIDIA GH200 144G HBM3e41.5150.9252.9EPYC 996537.9197.4381.4EPYC 9575F38.2248.3399.7OpenBenchmarking.orgWatts, Fewer Is BetterPennant 1.0.1CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 1811 Joules per run.2. EPYC 9965: Approximate power consumption of 2198 Joules per run.3. EPYC 9575F: Approximate power consumption of 2731 Joules per run.

Zstd Compression

This test measures the time needed to compress/decompress a sample file (silesia.tar) using Zstd (Zstandard) compression with options for different compression levels / settings. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.5.4Compression Level: 19 - Compression SpeedEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e612182430SE +/- 0.12, N = 3SE +/- 0.10, N = 3SE +/- 0.07, N = 325.519.415.21. (CC) gcc options: -O3 -pthread -lz -llzma

Kvazaar

This is a test of Kvazaar as a CPU-based H.265/HEVC video encoder written in the C programming language and optimized in Assembly. Kvazaar is the winner of the 2016 ACM Open-Source Software Competition and developed at the Ultra Video Group, Tampere University, Finland. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterKvazaar 2.2Video Input: Bosphorus 4K - Video Preset: MediumEPYC 9965EPYC 9575F1530456075SE +/- 0.17, N = 5SE +/- 0.02, N = 468.8241.301. (CC) gcc options: -pthread -ftree-vectorize -fvisibility=hidden -O2 -lpthread -lm -lrt
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterKvazaar 2.2Video Input: Bosphorus 4K - Video Preset: MediumEPYC 9965EPYC 9575F0.06350.1270.19050.2540.31750.2820.146
MinAvgMaxEPYC 996538.1244.3356.2EPYC 9575F38.4283.8372.6OpenBenchmarking.orgWatts, Fewer Is BetterKvazaar 2.2CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 3127 Joules per run.2. EPYC 9575F: Approximate power consumption of 5179 Joules per run.

Zstd Compression

This test measures the time needed to compress/decompress a sample file (silesia.tar) using Zstd (Zstandard) compression with options for different compression levels / settings. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.5.4Compression Level: 19 - Decompression SpeedEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e400800120016002000SE +/- 4.06, N = 3SE +/- 3.17, N = 3SE +/- 1.62, N = 32081.21520.51266.61. (CC) gcc options: -O3 -pthread -lz -llzma
OpenBenchmarking.orgMB/s Per Watt, More Is BetterZstd Compression 1.5.4Compression Level: 19 - Decompression SpeedNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 996561218243027.0518.1916.54
MinAvgMaxNVIDIA GH200 144G HBM3e39.146.854.5EPYC 996538.091.9112.9EPYC 9575F38.2114.4155.8OpenBenchmarking.orgWatts, Fewer Is BetterZstd Compression 1.5.4CPU Power Consumption Monitor40801201602001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 3714 Joules per run.2. EPYC 9965: Approximate power consumption of 6281 Joules per run.3. EPYC 9575F: Approximate power consumption of 7895 Joules per run.

Helsing

Helsing is an open-source POSIX vampire number generator. This test profile measures the time it takes to generate vampire numbers between varying numbers of digits. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterHelsing 1.0-betaDigit Range: 14 digitEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F1428425670SE +/- 0.08, N = 3SE +/- 0.02, N = 3SE +/- 0.23, N = 339.7756.9761.831. (CC) gcc options: -O2 -pthread
MinAvgMaxNVIDIA GH200 144G HBM3e40.6266.7291.3EPYC 996538.6342.9387.2EPYC 9575F38.4346.7373.1OpenBenchmarking.orgWatts, Fewer Is BetterHelsing 1.0-betaCPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 16357 Joules per run.2. EPYC 9965: Approximate power consumption of 14971 Joules per run.3. EPYC 9575F: Approximate power consumption of 22769 Joules per run.

WebP Image Encode

This is a test of Google's libwebp with the cwebp image encode utility and using a sample 6000x4000 pixel JPEG image as the input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100, Lossless, Highest CompressionEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e0.18680.37360.56040.74720.934SE +/- 0.00, N = 3SE +/- 0.00, N = 3SE +/- 0.00, N = 30.830.610.54-lpng16 -ljpeg -ltiff1. (CC) gcc options: -fvisibility=hidden -O2 -lm
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100, Lossless, Highest CompressionNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 99650.00270.00540.00810.01080.01350.0120.0090.007
MinAvgMaxNVIDIA GH200 144G HBM3e38.743.746.0EPYC 996537.883.093.8EPYC 9575F38.295.0114.3OpenBenchmarking.orgWatts, Fewer Is BetterWebP Image Encode 1.2.4CPU Power Consumption Monitor40801201602001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 2155 Joules per run.2. EPYC 9965: Approximate power consumption of 3599 Joules per run.3. EPYC 9575F: Approximate power consumption of 3134 Joules per run.

Liquid-DSP

LiquidSDR's Liquid-DSP is a software-defined radio (SDR) digital signal processing library. This test profile runs a multi-threaded benchmark of this SDR/DSP library focused on embedded platform usage. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 1.6Threads: 1 - Buffer Length: 256 - Filter Length: 32NVIDIA GH200 144G HBM3eEPYC 9575FEPYC 996513M26M39M52M65MSE +/- 32671.77, N = 3SE +/- 63828.76, N = 3SE +/- 154211.98, N = 36079033353801667398960001. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 1.6Threads: 1 - Buffer Length: 256 - Filter Length: 32NVIDIA GH200 144G HBM3eEPYC 9575FEPYC 9965300K600K900K1200K1500K1381459.61637828.74525714.32
MinAvgMaxNVIDIA GH200 144G HBM3e39.744.046.2EPYC 996538.175.983.0EPYC 9575F38.484.491.2OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 1.6CPU Power Consumption Monitor204060801001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 1525 Joules per run.2. EPYC 9965: Approximate power consumption of 2606 Joules per run.3. EPYC 9575F: Approximate power consumption of 2868 Joules per run.

High Performance Conjugate Gradient

HPCG is the High Performance Conjugate Gradient and is a new scientific benchmark from Sandia National Lans focused for super-computer testing with modern real-world workloads compared to HPCC. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOP/s, More Is BetterHigh Performance Conjugate Gradient 3.1X Y Z: 144 144 144 - RT: 60EPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e1428425670SE +/- 0.04, N = 3SE +/- 0.02, N = 3SE +/- 0.14, N = 363.9060.7042.321. (CXX) g++ options: -O3 -ffast-math -ftree-vectorize -lmpi_cxx -lmpi
OpenBenchmarking.orgGFLOP/s Per Watt, More Is BetterHigh Performance Conjugate Gradient 3.1X Y Z: 144 144 144 - RT: 60NVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F0.05380.10760.16140.21520.2690.2390.1760.163
MinAvgMaxNVIDIA GH200 144G HBM3e40.6177.3218.6EPYC 996538.2345.0387.5EPYC 9575F38.3391.5399.9OpenBenchmarking.orgWatts, Fewer Is BetterHigh Performance Conjugate Gradient 3.1CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 59263 Joules per run.2. EPYC 9965: Approximate power consumption of 215367 Joules per run.3. EPYC 9575F: Approximate power consumption of 99831 Joules per run.

Zstd Compression

This test measures the time needed to compress/decompress a sample file (silesia.tar) using Zstd (Zstandard) compression with options for different compression levels / settings. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.5.4Compression Level: 19, Long Mode - Decompression SpeedEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e400800120016002000SE +/- 9.59, N = 3SE +/- 9.80, N = 3SE +/- 5.67, N = 31970.81469.01305.91. (CC) gcc options: -O3 -pthread -lz -llzma
OpenBenchmarking.orgMB/s Per Watt, More Is BetterZstd Compression 1.5.4Compression Level: 19, Long Mode - Decompression SpeedNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 996571421283529.4819.4116.95
MinAvgMaxNVIDIA GH200 144G HBM3e39.244.349.0EPYC 996537.986.7100.8EPYC 9575F38.2101.5128.9OpenBenchmarking.orgWatts, Fewer Is BetterZstd Compression 1.5.4CPU Power Consumption Monitor40801201602001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 3810 Joules per run.2. EPYC 9965: Approximate power consumption of 7050 Joules per run.3. EPYC 9575F: Approximate power consumption of 7207 Joules per run.

PyBench

This test profile reports the total time of the different average timed test results from PyBench. PyBench reports average test times for different functions such as BuiltinFunctionCalls and NestedForLoops, with this total result providing a rough estimate as to Python's average performance on a given system. This test profile runs PyBench each time for 20 rounds. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyBench 2018-02-16Total For Average Test TimesEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e160320480640800SE +/- 1.58, N = 4SE +/- 2.21, N = 4SE +/- 1.53, N = 3482646724
MinAvgMaxNVIDIA GH200 144G HBM3e38.941.743.0EPYC 996537.873.883.6EPYC 9575F38.181.497.6OpenBenchmarking.orgWatts, Fewer Is BetterPyBench 2018-02-16CPU Power Consumption Monitor204060801001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 904 Joules per run.2. EPYC 9965: Approximate power consumption of 1458 Joules per run.3. EPYC 9575F: Approximate power consumption of 1261 Joules per run.

NAMD

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgns/day, More Is BetterNAMD 3.0b6Input: ATPase with 327,506 AtomsEPYC 9965EPYC 9575F510152025SE +/- 0.02, N = 3SE +/- 0.01, N = 319.9013.34
OpenBenchmarking.orgns/day Per Watt, More Is BetterNAMD 3.0b6Input: ATPase with 327,506 AtomsEPYC 9965EPYC 9575F0.04030.08060.12090.16120.20150.1790.056
MinAvgMaxEPYC 996537.8111.2383.0EPYC 9575F38.2236.1397.6OpenBenchmarking.orgWatts, Fewer Is BetterNAMD 3.0b6CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 6855 Joules per run.2. EPYC 9575F: Approximate power consumption of 5273 Joules per run.

Timed Godot Game Engine Compilation

This test times how long it takes to compile the Godot Game Engine. Godot is a popular, open-source, cross-platform 2D/3D game engine and is built using the SCons build system and targeting the X11 platform. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Godot Game Engine Compilation 4.0Time To CompileEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e306090120150SE +/- 0.37, N = 3SE +/- 0.19, N = 3SE +/- 0.28, N = 383.4491.05124.00
MinAvgMaxNVIDIA GH200 144G HBM3e39.3141.2272.9EPYC 996539.2177.2410.2EPYC 9575F38.5249.1401.2OpenBenchmarking.orgWatts, Fewer Is BetterTimed Godot Game Engine Compilation 4.0CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 18401 Joules per run.2. EPYC 9965: Approximate power consumption of 17067 Joules per run.3. EPYC 9575F: Approximate power consumption of 22000 Joules per run.

GraphicsMagick

This is a test of GraphicsMagick with its OpenMP implementation that performs various imaging tests on a sample high resolution (currently 15400 x 6940) JPEG image. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations Per Minute, More Is BetterGraphicsMagick 1.3.43Operation: SharpenEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F100200300400500SE +/- 0.88, N = 3SE +/- 0.00, N = 3SE +/- 0.33, N = 3445428301-lfreetype -lbz2-lfreetype -lbz21. (CC) gcc options: -fopenmp -O2 -ltiff -ljbig -lsharpyuv -lwebp -lwebpmux -ljpeg -lXext -lSM -lICE -lX11 -lxml2 -lzstd -llzma -lz -lm -lpthread -lgomp
OpenBenchmarking.orgIterations Per Minute Per Watt, More Is BetterGraphicsMagick 1.3.43Operation: SharpenNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F0.360.721.081.441.81.6001.5360.904
MinAvgMaxNVIDIA GH200 144G HBM3e42.8267.5300.5EPYC 996538.4289.7312.1EPYC 9575F38.7333.1356.2OpenBenchmarking.orgWatts, Fewer Is BetterGraphicsMagick 1.3.43CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 17299 Joules per run.2. EPYC 9965: Approximate power consumption of 18447 Joules per run.3. EPYC 9575F: Approximate power consumption of 21319 Joules per run.

miniFE

MiniFE Finite Element is an application for unstructured implicit finite element codes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgCG Mflops, More Is BetterminiFE 2.2Problem Size: SmallEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e15K30K45K60K75KSE +/- 50.38, N = 6SE +/- 21.96, N = 6SE +/- 346.24, N = 669114.465294.546786.31. (CXX) g++ options: -O3 -fopenmp -lmpi_cxx -lmpi
OpenBenchmarking.orgCG Mflops Per Watt, More Is BetterminiFE 2.2Problem Size: SmallNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F100200300400500453.14414.89372.32
MinAvgMaxNVIDIA GH200 144G HBM3e42.4103.2197.0EPYC 996537.8157.4341.3EPYC 9575F38.4185.6399.9OpenBenchmarking.orgWatts, Fewer Is BetterminiFE 2.2CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 1239 Joules per run.2. EPYC 9965: Approximate power consumption of 1810 Joules per run.3. EPYC 9575F: Approximate power consumption of 1949 Joules per run.

OpenFOAM

OpenFOAM is the leading free, open-source software for computational fluid dynamics (CFD). This test profile currently uses the drivaerFastback test case for analyzing automotive aerodynamics or alternatively the older motorBike input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Small Mesh Size - Execution TimeEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e91827364525.3425.6037.37-m64 -lfiniteVolume -lmeshTools -lparallel -lregionModels-m64 -lfiniteVolume -lmeshTools -lparallel -lregionModels-mcpu=native -lfoamToVTK -ldynamicMesh -lfileFormats1. (CXX) g++ options: -std=c++14 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -llagrangian -lgenericPatchFields -lOpenFOAM -ldl -lm
MinAvgMaxNVIDIA GH200 144G HBM3e41.1200.0287.7EPYC 996537.7322.4406.1EPYC 9575F38.8346.4399.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenFOAM 10CPU Power Consumption Monitor110220330440550

GraphicsMagick

This is a test of GraphicsMagick with its OpenMP implementation that performs various imaging tests on a sample high resolution (currently 15400 x 6940) JPEG image. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations Per Minute, More Is BetterGraphicsMagick 1.3.43Operation: EnhancedEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F110220330440550SE +/- 1.53, N = 3SE +/- 0.00, N = 3SE +/- 0.67, N = 3495369336-lfreetype -lbz2-lfreetype -lbz21. (CC) gcc options: -fopenmp -O2 -ltiff -ljbig -lsharpyuv -lwebp -lwebpmux -ljpeg -lXext -lSM -lICE -lX11 -lxml2 -lzstd -llzma -lz -lm -lpthread -lgomp
OpenBenchmarking.orgIterations Per Minute Per Watt, More Is BetterGraphicsMagick 1.3.43Operation: EnhancedEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F0.41990.83981.25971.67962.09951.8661.5420.986
MinAvgMaxNVIDIA GH200 144G HBM3e40.7239.3262.5EPYC 996538.0265.3285.1EPYC 9575F38.3340.8367.5OpenBenchmarking.orgWatts, Fewer Is BetterGraphicsMagick 1.3.43CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 15552 Joules per run.2. EPYC 9965: Approximate power consumption of 17068 Joules per run.3. EPYC 9575F: Approximate power consumption of 21695 Joules per run.

Algebraic Multi-Grid Benchmark

AMG is a parallel algebraic multigrid solver for linear systems arising from problems on unstructured grids. The driver provided with AMG builds linear systems for various 3-dimensional problems. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFigure Of Merit, More Is BetterAlgebraic Multi-Grid Benchmark 1.2EPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e700M1400M2100M2800M3500MSE +/- 7735064.17, N = 4SE +/- 2247127.13, N = 3SE +/- 3694072.61, N = 33197611250297634500022564546671. (CC) gcc options: -lparcsr_ls -lparcsr_mv -lseq_mv -lIJ_mv -lkrylov -lHYPRE_utilities -lm -fopenmp -lmpi
OpenBenchmarking.orgFigure Of Merit Per Watt, More Is BetterAlgebraic Multi-Grid Benchmark 1.2NVIDIA GH200 144G HBM3eEPYC 9575FEPYC 99653M6M9M12M15M15435851.4810869334.8310018207.52
MinAvgMaxNVIDIA GH200 144G HBM3e39.8146.2260.2EPYC 9575F38.3294.2401.4EPYC 996537.6297.1369.7OpenBenchmarking.orgWatts, Fewer Is BetterAlgebraic Multi-Grid Benchmark 1.2CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 3411 Joules per run.2. EPYC 9575F: Approximate power consumption of 5001 Joules per run.3. EPYC 9965: Approximate power consumption of 14161 Joules per run.

LAMMPS Molecular Dynamics Simulator

LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgns/day, More Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022Model: Rhodopsin ProteinNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F1326395265SE +/- 0.13, N = 11SE +/- 0.48, N = 15SE +/- 0.16, N = 1159.8455.0242.851. (CXX) g++ options: -O3 -lm -ldl
OpenBenchmarking.orgns/day Per Watt, More Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022Model: Rhodopsin ProteinNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 99650.21760.43520.65280.87041.0880.9670.4620.422
MinAvgMaxNVIDIA GH200 144G HBM3e39.961.9280.4EPYC 9575F38.192.8302.5EPYC 996520.5130.4330.9OpenBenchmarking.orgWatts, Fewer Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022CPU Power Consumption Monitor801602403204001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 360 Joules per run.2. EPYC 9575F: Approximate power consumption of 557 Joules per run.3. EPYC 9965: Approximate power consumption of 1052 Joules per run.

ClickHouse

ClickHouse is an open-source, high performance OLAP data management system. This test profile uses ClickHouse's standard benchmark recommendations per https://clickhouse.com/docs/en/operations/performance-test/ / https://github.com/ClickHouse/ClickBench/tree/main/clickhouse with the 100 million rows web analytics dataset. The reported value is the query processing time using the geometric mean of all separate queries performed as an aggregate. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgQueries Per Minute, Geo Mean, More Is BetterClickHouse 22.12.3.5100M Rows Hits Dataset, Second RunEPYC 9575FNVIDIA GH200 144G HBM3eEPYC 99652004006008001000SE +/- 3.75, N = 3SE +/- 2.22, N = 3SE +/- 4.38, N = 3809.27594.01587.17MIN: 65.65 / MAX: 8571.43MIN: 59.29 / MAX: 7500MIN: 92.02 / MAX: 6000

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy BenchmarkEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e2004006008001000SE +/- 3.50, N = 3SE +/- 0.78, N = 3SE +/- 1.78, N = 3962.28723.81699.30
OpenBenchmarking.orgScore Per Watt, More Is BetterNumpy BenchmarkNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 99654812162016.27710.2918.873
MinAvgMaxNVIDIA GH200 144G HBM3e38.743.047.7EPYC 996538.081.690.0EPYC 9575F38.193.5100.9OpenBenchmarking.orgWatts, Fewer Is BetterNumpy BenchmarkCPU Power Consumption Monitor204060801001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 5184 Joules per run.2. EPYC 9965: Approximate power consumption of 10468 Joules per run.3. EPYC 9575F: Approximate power consumption of 9070 Joules per run.

Zstd Compression

This test measures the time needed to compress/decompress a sample file (silesia.tar) using Zstd (Zstandard) compression with options for different compression levels / settings. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.5.4Compression Level: 19, Long Mode - Compression SpeedEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e3691215SE +/- 0.06, N = 3SE +/- 0.03, N = 3SE +/- 0.03, N = 311.809.298.641. (CC) gcc options: -O3 -pthread -lz -llzma

GraphicsMagick

This is a test of GraphicsMagick with its OpenMP implementation that performs various imaging tests on a sample high resolution (currently 15400 x 6940) JPEG image. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations Per Minute, More Is BetterGraphicsMagick 1.3.43Operation: Noise-GaussianNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F90180270360450SE +/- 0.00, N = 3SE +/- 1.00, N = 3SE +/- 1.00, N = 3397296291-lfreetype -lbz2-lfreetype -lbz21. (CC) gcc options: -fopenmp -O2 -ltiff -ljbig -lsharpyuv -lwebp -lwebpmux -ljpeg -lXext -lSM -lICE -lX11 -lxml2 -lzstd -llzma -lz -lm -lpthread -lgomp
OpenBenchmarking.orgIterations Per Minute Per Watt, More Is BetterGraphicsMagick 1.3.43Operation: Noise-GaussianNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F0.53661.07321.60982.14642.6832.3851.7791.295
MinAvgMaxEPYC 996538.3166.4181.2NVIDIA GH200 144G HBM3e41.9166.4283.3EPYC 9575F38.4224.7243.8OpenBenchmarking.orgWatts, Fewer Is BetterGraphicsMagick 1.3.43CPU Power Consumption Monitor701402102803501. EPYC 9965: Approximate power consumption of 10595 Joules per run.2. NVIDIA GH200 144G HBM3e: Approximate power consumption of 10708 Joules per run.3. EPYC 9575F: Approximate power consumption of 14456 Joules per run.

QMCPACK

QMCPACK is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code making use of MPI for this benchmark of the H20 example code. QMCPACK is an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids. QMCPACK is supported by the U.S. Department of Energy. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.17.1Input: Li2_STO_aeNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 996520406080100SE +/- 0.31, N = 3SE +/- 0.39, N = 3SE +/- 0.32, N = 369.6172.9394.59-mcpu=native-march=native-march=native1. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -O3 -lm -ldl
MinAvgMaxNVIDIA GH200 144G HBM3e41.5268.3298.1EPYC 996538.3360.0391.3EPYC 9575F38.7376.4399.8OpenBenchmarking.orgWatts, Fewer Is BetterQMCPACK 3.17.1CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 20299 Joules per run.2. EPYC 9965: Approximate power consumption of 36600 Joules per run.3. EPYC 9575F: Approximate power consumption of 29110 Joules per run.

ClickHouse

ClickHouse is an open-source, high performance OLAP data management system. This test profile uses ClickHouse's standard benchmark recommendations per https://clickhouse.com/docs/en/operations/performance-test/ / https://github.com/ClickHouse/ClickBench/tree/main/clickhouse with the 100 million rows web analytics dataset. The reported value is the query processing time using the geometric mean of all separate queries performed as an aggregate. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgQueries Per Minute, Geo Mean, More Is BetterClickHouse 22.12.3.5100M Rows Hits Dataset, Third RunEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e2004006008001000SE +/- 1.25, N = 3SE +/- 2.05, N = 3SE +/- 2.50, N = 3809.87598.32596.49MIN: 65.43 / MAX: 10000MIN: 91.19 / MAX: 6000MIN: 59.29 / MAX: 7500
OpenBenchmarking.orgQueries Per Minute, Geo Mean Per Watt, More Is BetterClickHouse 22.12.3.5100M Rows Hits Dataset, Third RunNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F2468107.9804.2024.181
MinAvgMaxNVIDIA GH200 144G HBM3e40.074.7282.8EPYC 996538.2142.4305.3EPYC 9575F38.3193.7368.0OpenBenchmarking.orgWatts, Fewer Is BetterClickHouse 22.12.3.5CPU Power Consumption Monitor1002003004005001. NVIDIA GH200 144G HBM3e: Approximate power consumption of 12558 Joules per run.2. EPYC 9965: Approximate power consumption of 23304 Joules per run.3. EPYC 9575F: Approximate power consumption of 24083 Joules per run.

OpenBenchmarking.orgQueries Per Minute, Geo Mean, More Is BetterClickHouse 22.12.3.5100M Rows Hits Dataset, First Run / Cold CacheEPYC 9575FNVIDIA GH200 144G HBM3eEPYC 99652004006008001000SE +/- 3.67, N = 3SE +/- 2.14, N = 3SE +/- 3.03, N = 3779.31581.59576.76MIN: 66.01 / MAX: 7500MIN: 58.94 / MAX: 6666.67MIN: 89.55 / MAX: 5454.55

CloverLeaf

CloverLeaf is a Lagrangian-Eulerian hydrodynamics benchmark. This test profile currently makes use of CloverLeaf's OpenMP version. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCloverLeaf 1.3Input: clover_bm64_shortEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e714212835SE +/- 0.00, N = 3SE +/- 0.09, N = 3SE +/- 0.06, N = 321.1224.1128.411. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp
MinAvgMaxNVIDIA GH200 144G HBM3e41.4142.4183.4EPYC 996538.1286.3339.7EPYC 9575F38.2332.1399.4OpenBenchmarking.orgWatts, Fewer Is BetterCloverLeaf 1.3CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 4747 Joules per run.2. EPYC 9965: Approximate power consumption of 8207 Joules per run.3. EPYC 9575F: Approximate power consumption of 8525 Joules per run.

Intel Open Image Denoise

Open Image Denoise is a denoising library for ray-tracing and part of the Intel oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 2.2Run: RTLightmap.hdr.4096x4096 - Device: CPU-OnlyEPYC 9965EPYC 9575F0.72451.4492.17352.8983.6225SE +/- 0.00, N = 5SE +/- 0.00, N = 43.222.40
OpenBenchmarking.orgImages / Sec Per Watt, More Is BetterIntel Open Image Denoise 2.2Run: RTLightmap.hdr.4096x4096 - Device: CPU-OnlyEPYC 9965EPYC 9575F0.00290.00580.00870.01160.01450.0130.008
MinAvgMaxEPYC 996538.0250.7355.3EPYC 9575F38.4292.0383.0OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 2.2CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 3460 Joules per run.2. EPYC 9575F: Approximate power consumption of 4965 Joules per run.

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 2.2Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-OnlyEPYC 9965EPYC 9575F246810SE +/- 0.00, N = 7SE +/- 0.00, N = 76.735.04
OpenBenchmarking.orgImages / Sec Per Watt, More Is BetterIntel Open Image Denoise 2.2Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-OnlyEPYC 9965EPYC 9575F0.00770.01540.02310.03080.03850.0340.021
MinAvgMaxEPYC 996538.0199.8360.2EPYC 9575F38.3240.2389.0OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 2.2CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 1799 Joules per run.2. EPYC 9575F: Approximate power consumption of 2505 Joules per run.

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 2.2Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-OnlyEPYC 9965EPYC 9575F246810SE +/- 0.01, N = 7SE +/- 0.00, N = 76.745.06
OpenBenchmarking.orgImages / Sec Per Watt, More Is BetterIntel Open Image Denoise 2.2Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-OnlyEPYC 9965EPYC 9575F0.00770.01540.02310.03080.03850.0340.021
MinAvgMaxEPYC 996538.2200.3356.3EPYC 9575F38.3237.7390.0OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 2.2CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 1803 Joules per run.2. EPYC 9575F: Approximate power consumption of 2411 Joules per run.

NAMD

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgns/day, More Is BetterNAMD 3.0b6Input: STMV with 1,066,628 AtomsEPYC 9965EPYC 9575F1.13222.26443.39664.52885.661SE +/- 0.01244, N = 3SE +/- 0.00773, N = 35.031853.80313
OpenBenchmarking.orgns/day Per Watt, More Is BetterNAMD 3.0b6Input: STMV with 1,066,628 AtomsEPYC 9965EPYC 9575F0.00830.01660.02490.03320.04150.0370.014
MinAvgMaxEPYC 996538.0135.3374.0EPYC 9575F38.1276.0398.0OpenBenchmarking.orgWatts, Fewer Is BetterNAMD 3.0b6CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 12580 Joules per run.2. EPYC 9575F: Approximate power consumption of 9200 Joules per run.

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 7.0Encoder: libx265 - Scenario: PlatformEPYC 9575FEPYC 996520406080100SE +/- 0.09, N = 3SE +/- 0.06, N = 379.5360.111. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma
OpenBenchmarking.orgFPS Per Watt, More Is BetterFFmpeg 7.0Encoder: libx265 - Scenario: PlatformEPYC 9965EPYC 9575F0.11430.22860.34290.45720.57150.5080.492
MinAvgMaxEPYC 996537.9118.2149.3EPYC 9575F38.3161.8226.0OpenBenchmarking.orgWatts, Fewer Is BetterFFmpeg 7.0CPU Power Consumption Monitor601201802403001. EPYC 9965: Approximate power consumption of 17383 Joules per run.2. EPYC 9575F: Approximate power consumption of 18122 Joules per run.

NWChem

NWChem is an open-source high performance computational chemistry package. Per NWChem's documentation, "NWChem aims to provide its users with computational chemistry tools that are scalable both in their ability to treat large scientific computational chemistry problems efficiently, and in their use of available parallel computing resources from high-performance parallel supercomputers to conventional workstation clusters." Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNWChem 7.0.2Input: C240 BuckyballNVIDIA GH200 144G HBM3eEPYC 9575FEPYC 9965300600900120015001214.71225.71594.0-m64-m641. (F9X) gfortran options: -lnwctask -lccsd -lmcscf -lselci -lmp2 -lmoints -lstepper -ldriver -loptim -lnwdft -lgradients -lcphf -lesp -lddscf -ldangchang -lguess -lhessian -lvib -lnwcutil -lrimp2 -lproperty -lsolvation -lnwints -lprepar -lnwmd -lnwpw -lofpw -lpaw -lpspw -lband -lnwpwlib -lcafe -lspace -lanalyze -lqhop -lpfft -ldplot -ldrdy -lvscf -lqmmm -lqmd -letrans -ltce -lbq -lmm -lcons -lperfm -ldntmc -lccca -ldimqm -lga -larmci -lpeigs -l64to32 -lopenblas -lpthread -lrt -llapack -lnwcblas -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz -lcomex -ffast-math -std=legacy -fdefault-integer-8 -finline-functions -O2
OpenBenchmarking.orgWatts, Fewer Is BetterNWChem 7.0.2CPU Power Consumption MonitorNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F70140210280350Min: 41.49 / Avg: 226.74 / Max: 281.44Min: 38.12 / Avg: 387.9 / Max: 409.94Min: 38.3 / Avg: 392.8 / Max: 400.06

Timed Gem5 Compilation

This test times how long it takes to compile Gem5. Gem5 is a simulator for computer system architecture research. Gem5 is widely used for computer architecture research within the industry, academia, and more. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Gem5 Compilation 23.0.1Time To CompileEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e4080120160200SE +/- 0.56, N = 3SE +/- 1.20, N = 3SE +/- 0.26, N = 3130.80140.59166.10
MinAvgMaxNVIDIA GH200 144G HBM3e39.4118.8239.4EPYC 996537.8163.6402.4EPYC 9575F38.5211.8401.3OpenBenchmarking.orgWatts, Fewer Is BetterTimed Gem5 Compilation 23.0.1CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 20441 Joules per run.2. EPYC 9965: Approximate power consumption of 23835 Joules per run.3. EPYC 9575F: Approximate power consumption of 28661 Joules per run.

OpenRadioss

OpenRadioss is an open-source AGPL-licensed finite element solver for dynamic event analysis OpenRadioss is based on Altair Radioss and open-sourced in 2022. This open-source finite element solver is benchmarked with various example models available from https://www.openradioss.org/models/ and https://github.com/OpenRadioss/ModelExchange/tree/main/Examples. This test is currently using a reference OpenRadioss binary build offered via GitHub. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenRadioss 2023.09.15Model: Cell Phone Drop TestEPYC 9575FEPYC 9965510152025SE +/- 0.09, N = 3SE +/- 0.06, N = 317.6422.03
MinAvgMaxEPYC 996538.6232.2389.0EPYC 9575F38.5285.2399.1OpenBenchmarking.orgWatts, Fewer Is BetterOpenRadioss 2023.09.15CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 9209 Joules per run.2. EPYC 9575F: Approximate power consumption of 7320 Joules per run.

SVT-AV1

This is a benchmark of the SVT-AV1 open-source video encoder/decoder. SVT-AV1 was originally developed by Intel as part of their Open Visual Cloud / Scalable Video Technology (SVT). Development of SVT-AV1 has since moved to the Alliance for Open Media as part of upstream AV1 development. SVT-AV1 is a CPU-based multi-threaded video encoder for the AV1 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 4 - Input: Bosphorus 4KEPYC 9575FEPYC 99653691215SE +/- 0.13, N = 4SE +/- 0.03, N = 312.4610.021. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 4 - Input: Bosphorus 4KEPYC 9965EPYC 9575F0.01670.03340.05010.06680.08350.0740.066
MinAvgMaxEPYC 996537.8134.8221.3EPYC 9575F38.3187.3334.2OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 2.0CPU Power Consumption Monitor801602403204001. EPYC 9965: Approximate power consumption of 3011 Joules per run.2. EPYC 9575F: Approximate power consumption of 3372 Joules per run.

Xcompact3d Incompact3d

Xcompact3d Incompact3d is a Fortran-MPI based, finite difference high-performance code for solving the incompressible Navier-Stokes equation and as many as you need scalar transport equations. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterXcompact3d Incompact3d 2021-03-11Input: X3D-benchmarking input.i3dEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e50100150200250SE +/- 0.30, N = 3SE +/- 1.26, N = 3SE +/- 0.29, N = 3196.32217.38240.291. (F9X) gfortran options: -cpp -O2 -funroll-loops -floop-optimize -fcray-pointer -fbacktrace -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
MinAvgMaxNVIDIA GH200 144G HBM3e42.2172.9253.2EPYC 996538.4357.8389.5EPYC 9575F38.2389.8399.8OpenBenchmarking.orgWatts, Fewer Is BetterXcompact3d Incompact3d 2021-03-11CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 43344 Joules per run.2. EPYC 9965: Approximate power consumption of 73462 Joules per run.3. EPYC 9575F: Approximate power consumption of 86804 Joules per run.

LAMMPS Molecular Dynamics Simulator

LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgns/day, More Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022Model: 20k AtomsEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F1530456075SE +/- 0.46, N = 3SE +/- 0.14, N = 3SE +/- 0.37, N = 366.7758.1454.791. (CXX) g++ options: -O3 -lm -ldl
OpenBenchmarking.orgns/day Per Watt, More Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022Model: 20k AtomsNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F0.04770.09540.14310.19080.23850.2120.1680.141
MinAvgMaxNVIDIA GH200 144G HBM3e40.1274.7292.0EPYC 9575F38.4388.8399.6EPYC 996537.8396.7422.8OpenBenchmarking.orgWatts, Fewer Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 42483 Joules per run.2. EPYC 9575F: Approximate power consumption of 62864 Joules per run.3. EPYC 9965: Approximate power consumption of 54084 Joules per run.

Timed Linux Kernel Compilation

This test times how long it takes to build the Linux kernel in a default configuration (defconfig) for the architecture being tested or alternatively an allmodconfig for building all possible kernel modules for the build. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Linux Kernel Compilation 6.8Build: allmodconfigEPYC 9575FNVIDIA GH200 144G HBM3eEPYC 996550100150200250SE +/- 0.48, N = 3SE +/- 0.59, N = 3SE +/- 1.72, N = 3203.80246.66248.29
MinAvgMaxNVIDIA GH200 144G HBM3e38.9229.8270.8EPYC 996538.2307.8388.2EPYC 9575F38.5352.3401.2OpenBenchmarking.orgWatts, Fewer Is BetterTimed Linux Kernel Compilation 6.8CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 59825 Joules per run.2. EPYC 9965: Approximate power consumption of 79520 Joules per run.3. EPYC 9575F: Approximate power consumption of 74930 Joules per run.

OpenRadioss

OpenRadioss is an open-source AGPL-licensed finite element solver for dynamic event analysis OpenRadioss is based on Altair Radioss and open-sourced in 2022. This open-source finite element solver is benchmarked with various example models available from https://www.openradioss.org/models/ and https://github.com/OpenRadioss/ModelExchange/tree/main/Examples. This test is currently using a reference OpenRadioss binary build offered via GitHub. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenRadioss 2023.09.15Model: Chrysler Neon 1MEPYC 9965EPYC 9575F20406080100SE +/- 0.41, N = 3SE +/- 0.09, N = 392.04109.72
MinAvgMaxEPYC 996538.4300.9408.4EPYC 9575F38.7350.4400.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenRadioss 2023.09.15CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 38413 Joules per run.2. EPYC 9575F: Approximate power consumption of 45079 Joules per run.

SVT-AV1

This is a benchmark of the SVT-AV1 open-source video encoder/decoder. SVT-AV1 was originally developed by Intel as part of their Open Visual Cloud / Scalable Video Technology (SVT). Development of SVT-AV1 has since moved to the Alliance for Open Media as part of upstream AV1 development. SVT-AV1 is a CPU-based multi-threaded video encoder for the AV1 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 12 - Input: Bosphorus 4KEPYC 9575FEPYC 996560120180240300SE +/- 1.47, N = 9SE +/- 1.44, N = 9294.41248.701. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 12 - Input: Bosphorus 4KEPYC 9575FEPYC 99650.58121.16241.74362.32482.9062.5832.524
MinAvgMaxEPYC 996537.998.5241.1EPYC 9575F38.2114.0322.8OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 2.0CPU Power Consumption Monitor801602403204001. EPYC 9965: Approximate power consumption of 745 Joules per run.2. EPYC 9575F: Approximate power consumption of 798 Joules per run.

uvg266

uvg266 is an open-source VVC/H.266 (Versatile Video Coding) encoder based on Kvazaar as part of the Ultra Video Group, Tampere University, Finland. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: Very FastEPYC 9575FEPYC 996520406080100SE +/- 0.12, N = 6SE +/- 0.11, N = 582.8970.48
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: Very FastEPYC 9965EPYC 9575F0.07670.15340.23010.30680.38350.3410.319
MinAvgMaxEPYC 996538.7206.8302.8EPYC 9575F38.3259.7393.5OpenBenchmarking.orgWatts, Fewer Is Betteruvg266 0.4.1CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 2605 Joules per run.2. EPYC 9575F: Approximate power consumption of 2944 Joules per run.

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 256 - Model: ResNet-50EPYC 9575FEPYC 996550100150200250SE +/- 0.13, N = 3SE +/- 0.20, N = 3234.68201.12
OpenBenchmarking.orgimages/sec Per Watt, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 256 - Model: ResNet-50EPYC 9575FEPYC 99650.17390.34780.52170.69560.86950.7730.702
MinAvgMaxEPYC 996538.0286.5307.5EPYC 9575F38.3303.7322.5OpenBenchmarking.orgWatts, Fewer Is BetterTensorFlow 2.16.1CPU Power Consumption Monitor801602403204001. EPYC 9965: Approximate power consumption of 42686 Joules per run.2. EPYC 9575F: Approximate power consumption of 38674 Joules per run.

Device: CPU - Batch Size: 256 - Model: ResNet-50

NVIDIA GH200 144G HBM3e: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'absl'

SVT-AV1

This is a benchmark of the SVT-AV1 open-source video encoder/decoder. SVT-AV1 was originally developed by Intel as part of their Open Visual Cloud / Scalable Video Technology (SVT). Development of SVT-AV1 has since moved to the Alliance for Open Media as part of upstream AV1 development. SVT-AV1 is a CPU-based multi-threaded video encoder for the AV1 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 8 - Input: Bosphorus 4KEPYC 9575FEPYC 9965306090120150SE +/- 0.58, N = 7SE +/- 1.06, N = 6121.05104.641. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 8 - Input: Bosphorus 4KEPYC 9965EPYC 9575F0.18560.37120.55680.74240.9280.8250.790
MinAvgMaxEPYC 996537.9126.8237.7EPYC 9575F38.1153.2299.1OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 2.0CPU Power Consumption Monitor801602403204001. EPYC 9965: Approximate power consumption of 1437 Joules per run.2. EPYC 9575F: Approximate power consumption of 1576 Joules per run.

OpenRadioss

OpenRadioss is an open-source AGPL-licensed finite element solver for dynamic event analysis OpenRadioss is based on Altair Radioss and open-sourced in 2022. This open-source finite element solver is benchmarked with various example models available from https://www.openradioss.org/models/ and https://github.com/OpenRadioss/ModelExchange/tree/main/Examples. This test is currently using a reference OpenRadioss binary build offered via GitHub. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenRadioss 2023.09.15Model: INIVOL and Fluid Structure Interaction Drop ContainerEPYC 9965EPYC 9575F20406080100SE +/- 0.04, N = 3SE +/- 0.36, N = 377.7689.66
MinAvgMaxEPYC 996538.9344.2406.7EPYC 9575F38.8369.6400.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenRadioss 2023.09.15CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 31209 Joules per run.2. EPYC 9575F: Approximate power consumption of 36093 Joules per run.

GPAW

GPAW is a density-functional theory (DFT) Python code based on the projector-augmented wave (PAW) method and the atomic simulation environment (ASE). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterGPAW 23.6Input: Carbon NanotubeEPYC 9965EPYC 9575F714212835SE +/- 0.02, N = 3SE +/- 0.21, N = 327.5629.871. (CC) gcc options: -shared -lxc -lblas -lmpi
MinAvgMaxEPYC 996538.1324.6416.9EPYC 9575F38.3351.8400.0OpenBenchmarking.orgWatts, Fewer Is BetterGPAW 23.6CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 11792 Joules per run.2. EPYC 9575F: Approximate power consumption of 12195 Joules per run.

Input: Carbon Nanotube

NVIDIA GH200 144G HBM3e: The test quit with a non-zero exit status. E: ImportError: numpy.core.multiarray failed to import

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 512 - Model: ResNet-50EPYC 9575FEPYC 996560120180240300SE +/- 0.17, N = 3SE +/- 0.31, N = 3254.02247.22
OpenBenchmarking.orgimages/sec Per Watt, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 512 - Model: ResNet-50EPYC 9965EPYC 9575F0.18430.36860.55290.73720.92150.8190.772
MinAvgMaxEPYC 996538.2301.7329.7EPYC 9575F38.4329.2346.8OpenBenchmarking.orgWatts, Fewer Is BetterTensorFlow 2.16.1CPU Power Consumption Monitor1002003004005001. EPYC 9965: Approximate power consumption of 71906 Joules per run.2. EPYC 9575F: Approximate power consumption of 75493 Joules per run.

Device: CPU - Batch Size: 512 - Model: ResNet-50

NVIDIA GH200 144G HBM3e: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'absl'

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 256 - Model: AlexNetEPYC 9965EPYC 9575F400800120016002000SE +/- 17.23, N = 4SE +/- 2.59, N = 42085.022050.63
OpenBenchmarking.orgimages/sec Per Watt, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 256 - Model: AlexNetEPYC 9965EPYC 9575F369121510.7788.736
MinAvgMaxEPYC 996538.3193.4260.2EPYC 9575F38.5234.7310.5OpenBenchmarking.orgWatts, Fewer Is BetterTensorFlow 2.16.1CPU Power Consumption Monitor801602403204001. EPYC 9965: Approximate power consumption of 3821 Joules per run.2. EPYC 9575F: Approximate power consumption of 4577 Joules per run.

Device: CPU - Batch Size: 256 - Model: AlexNet

NVIDIA GH200 144G HBM3e: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'absl'

Meta Performance Per Watts

OpenBenchmarking.orgPerformance Per Watts, More Is BetterMeta Performance Per WattsPerformance Per WattsNVIDIA GH200 144G HBM3e50K100K150K200K250K227401.82

OpenFOAM

OpenFOAM is the leading free, open-source software for computational fluid dynamics (CFD). This test profile currently uses the drivaerFastback test case for analyzing automotive aerodynamics or alternatively the older motorBike input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Small Mesh Size - Mesh TimeNVIDIA GH200 144G HBM3e4812162016.741. (CXX) g++ options: -std=c++14 -O3 -mcpu=native -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

CPU Power Consumption Monitor

OpenBenchmarking.orgWattsCPU Power Consumption MonitorPhoronix Test Suite System MonitoringNVIDIA GH200 144G HBM3eEPYC 9965EPYC 9575F80160240320400Min: 38.42 / Avg: 170.19 / Max: 300.47Min: 19.04 / Avg: 275.17 / Max: 461.59Min: 19.17 / Avg: 313.09 / Max: 403.08

Xmrig

MinAvgMaxNVIDIA GH200 144G HBM3e41.3140.0146.7EPYC 996538.1321.6344.6EPYC 9575F38.2380.2401.2OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.21CPU Power Consumption Monitor110220330440550

OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.21Variant: GhostRider - Hash Count: 1MEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e112233445548.0430.5228.69

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: GhostRider - Hash Count: 1MEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e3K6K9K12K15KSE +/- 53.68, N = 3SE +/- 508.75, N = 15SE +/- 0.17, N = 315447.611601.94015.5-maes-maes1. (CXX) g++ options: -fexceptions -fno-rtti -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.21Variant: GhostRider - Hash Count: 1MEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e112233445548.0430.5228.69
MinAvgMaxNVIDIA GH200 144G HBM3e41.3140.0146.7EPYC 996538.1321.6344.6EPYC 9575F38.2380.2401.2OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.21CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 35502 Joules per run.2. EPYC 9965: Approximate power consumption of 22295 Joules per run.3. EPYC 9575F: Approximate power consumption of 35486 Joules per run.

SVT-AV1

MinAvgMaxEPYC 996537.9100.0236.1EPYC 9575F38.1114.5297.7OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 2.0CPU Power Consumption Monitor70140210280350

OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 13 - Input: Bosphorus 4KEPYC 9575FEPYC 99650.55761.11521.67282.23042.7882.4782.445

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 13 - Input: Bosphorus 4KEPYC 9575FEPYC 996560120180240300SE +/- 7.85, N = 15SE +/- 5.92, N = 15283.70244.581. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 2.0Encoder Mode: Preset 13 - Input: Bosphorus 4KEPYC 9575FEPYC 99650.55761.11521.67282.23042.7882.4782.445
MinAvgMaxEPYC 996537.9100.0236.1EPYC 9575F38.1114.5297.7OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 2.0CPU Power Consumption Monitor701402102803501. EPYC 9965: Approximate power consumption of 780 Joules per run.2. EPYC 9575F: Approximate power consumption of 809 Joules per run.

LuxCoreRender

MinAvgMaxEPYC 996538.0361.2413.4EPYC 9575F38.2367.3401.1OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor110220330440550

OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: Orange Juice - Acceleration: CPUEPYC 9965EPYC 9575F0.02210.04420.06630.08840.11050.0980.061

OpenBenchmarking.orgM samples/sec, More Is BetterLuxCoreRender 2.6Scene: Orange Juice - Acceleration: CPUEPYC 9965EPYC 9575F816243240SE +/- 0.69, N = 15SE +/- 0.25, N = 435.2222.26MIN: 29.07 / MAX: 50.81MIN: 19.56 / MAX: 27.46
OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: Orange Juice - Acceleration: CPUEPYC 9965EPYC 9575F0.02210.04420.06630.08840.11050.0980.061
MinAvgMaxEPYC 996538.0361.2413.4EPYC 9575F38.2367.3401.1OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 24198 Joules per run.2. EPYC 9575F: Approximate power consumption of 24332 Joules per run.

MinAvgMaxEPYC 996538.2366.0402.7EPYC 9575F38.3374.7401.1OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor110220330440550

OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: DLSC - Acceleration: CPUEPYC 9965EPYC 9575F0.01490.02980.04470.05960.07450.0660.041

OpenBenchmarking.orgM samples/sec, More Is BetterLuxCoreRender 2.6Scene: DLSC - Acceleration: CPUEPYC 9965EPYC 9575F612182430SE +/- 0.37, N = 15SE +/- 0.19, N = 424.0215.36MIN: 21.26 / MAX: 31.63MIN: 14.74 / MAX: 17.33
OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: DLSC - Acceleration: CPUEPYC 9965EPYC 9575F0.01490.02980.04470.05960.07450.0660.041
MinAvgMaxEPYC 996538.2366.0402.7EPYC 9575F38.3374.7401.1OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor1102203304405501. EPYC 9965: Approximate power consumption of 23763 Joules per run.2. EPYC 9575F: Approximate power consumption of 24264 Joules per run.

NAS Parallel Benchmarks

MinAvgMaxNVIDIA GH200 144G HBM3e40.4156.9270.9EPYC 9575F38.1171.8399.7EPYC 996537.8182.9399.7OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor110220330440550

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: CG.CEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e80160240320400381.14280.56175.40

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: CG.CEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e14K28K42K56K70KSE +/- 728.80, N = 15SE +/- 985.07, N = 15SE +/- 17.96, N = 665470.2351319.4627521.871. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.6
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: CG.CEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e80160240320400381.14280.56175.40
MinAvgMaxNVIDIA GH200 144G HBM3e40.4156.9270.9EPYC 9575F38.1171.8399.7EPYC 996537.8182.9399.7OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 1700 Joules per run.2. EPYC 9575F: Approximate power consumption of 1225 Joules per run.3. EPYC 9965: Approximate power consumption of 1683 Joules per run.

MinAvgMaxNVIDIA GH200 144G HBM3e41.3120.4274.2EPYC 9575F38.1128.0396.0EPYC 996537.9142.2373.9OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor110220330440550

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: MG.CEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e300600900120015001247.11791.79529.28

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: MG.CEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e30K60K90K120K150KSE +/- 1509.34, N = 15SE +/- 2066.13, N = 15SE +/- 28.42, N = 8159641.78112588.9963701.221. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.6
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: MG.CEPYC 9575FEPYC 9965NVIDIA GH200 144G HBM3e300600900120015001247.11791.79529.28
MinAvgMaxNVIDIA GH200 144G HBM3e41.3120.4274.2EPYC 9575F38.1128.0396.0EPYC 996537.9142.2373.9OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 978 Joules per run.2. EPYC 9575F: Approximate power consumption of 751 Joules per run.3. EPYC 9965: Approximate power consumption of 1138 Joules per run.

Pennant

MinAvgMaxNVIDIA GH200 144G HBM3e41.7140.4265.6EPYC 996538.0172.4384.3EPYC 9575F38.2195.5397.8OpenBenchmarking.orgWatts, Fewer Is BetterPennant 1.0.1CPU Power Consumption Monitor110220330440550

OpenBenchmarking.orgHydro Cycle Time - Seconds, Fewer Is BetterPennant 1.0.1Test: leblancbigEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e1.00662.01323.01984.02645.033SE +/- 0.017533, N = 7SE +/- 0.077441, N = 15SE +/- 0.007724, N = 72.3795133.2388664.4737791. (CXX) g++ options: -fopenmp -lmpi_cxx -lmpi
MinAvgMaxNVIDIA GH200 144G HBM3e41.7140.4265.6EPYC 996538.0172.4384.3EPYC 9575F38.2195.5397.8OpenBenchmarking.orgWatts, Fewer Is BetterPennant 1.0.1CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 1404 Joules per run.2. EPYC 9965: Approximate power consumption of 1700 Joules per run.3. EPYC 9575F: Approximate power consumption of 1551 Joules per run.

RELION

OpenBenchmarking.orgWatts, Fewer Is BetterRELION 4.0.1CPU Power Consumption MonitorEPYC 9965EPYC 9575F70140210280350Min: 38.2 / Avg: 375.69 / Max: 395.47Min: 38.25 / Avg: 389.21 / Max: 399.75

OpenBenchmarking.orgSeconds, Fewer Is BetterRELION 4.0.1Test: Basic - Device: CPUEPYC 9965EPYC 9575F50100150200250SE +/- 3.21, N = 12SE +/- 3.85, N = 9169.70223.811. (CXX) g++ options: -fopenmp -std=c++11 -O3 -rdynamic -ldl -ltiff -lfftw3f -lfftw3 -lpng -ljpeg -lmpi_cxx -lmpi
OpenBenchmarking.orgWatts, Fewer Is BetterRELION 4.0.1CPU Power Consumption MonitorEPYC 9965EPYC 9575F70140210280350Min: 38.2 / Avg: 375.69 / Max: 395.47Min: 38.25 / Avg: 389.21 / Max: 399.751. EPYC 9965: Approximate power consumption of 64869 Joules per run.2. EPYC 9575F: Approximate power consumption of 88092 Joules per run.

Speedb

MinAvgMaxNVIDIA GH200 144G HBM3e43.4181.9215.8EPYC 996538.7314.8417.4EPYC 9575F38.5375.8400.8OpenBenchmarking.orgWatts, Fewer Is BetterSpeedb 2.7CPU Power Consumption Monitor110220330440550

OpenBenchmarking.orgOp/s Per Watt, More Is BetterSpeedb 2.7Test: Read While WritingEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F16K32K48K64K80K76045.3556303.8428692.82

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Read While WritingEPYC 9965EPYC 9575FNVIDIA GH200 144G HBM3e5M10M15M20M25MSE +/- 505667.36, N = 15SE +/- 91725.44, N = 15SE +/- 87939.63, N = 15239400151078328110241042-lpthread-lpthread1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti
OpenBenchmarking.orgOp/s Per Watt, More Is BetterSpeedb 2.7Test: Read While WritingEPYC 9965NVIDIA GH200 144G HBM3eEPYC 9575F16K32K48K64K80K76045.3556303.8428692.82
MinAvgMaxNVIDIA GH200 144G HBM3e43.4181.9215.8EPYC 996538.7314.8417.4EPYC 9575F38.5375.8400.8OpenBenchmarking.orgWatts, Fewer Is BetterSpeedb 2.7CPU Power Consumption Monitor1102203304405501. NVIDIA GH200 144G HBM3e: Approximate power consumption of 11702 Joules per run.2. EPYC 9965: Approximate power consumption of 20148 Joules per run.3. EPYC 9575F: Approximate power consumption of 23977 Joules per run.

164 Results Shown

OpenSSL
Liquid-DSP
OpenVINO:
  Vehicle Detection FP16-INT8 - CPU
  Weld Porosity Detection FP16-INT8 - CPU
Liquid-DSP
OpenVINO
miniBUDE:
  OpenMP - BM2:
    GFInst/s
    Billion Interactions/s
NAS Parallel Benchmarks
OpenVINO
NAS Parallel Benchmarks
OpenVINO:
  Noise Suppression Poconet-Like FP16 - CPU
  Person Vehicle Bike Detection FP16 - CPU
BRL-CAD
OpenSSL
John The Ripper:
  bcrypt
  Blowfish
OpenVINO:
  Weld Porosity Detection FP16-INT8 - CPU
  Age Gender Recognition Retail 0013 FP16-INT8 - CPU
srsRAN Project
Memcached
ASKAP
OpenSSL:
  AES-256-GCM
  AES-128-GCM
OpenVINO
NAS Parallel Benchmarks
OpenVINO
GROMACS
Stockfish
OpenSSL
Liquid-DSP
ASKAP
OpenVINO:
  Vehicle Detection FP16-INT8 - CPU
  Road Segmentation ADAS FP16-INT8 - CPU
John The Ripper
OpenVINO
srsRAN Project
ASTC Encoder:
  Very Thorough
  Exhaustive
  Thorough
libavif avifenc
OpenSSL
7-Zip Compression:
  Decompression Rating
  Compression Rating
QuantLib
OSPRay Studio
OpenFOAM
OSPRay Studio
OpenVINO:
  Person Detection FP16 - CPU
  Age Gender Recognition Retail 0013 FP16-INT8 - CPU
Liquid-DSP
OpenVINO:
  Face Detection Retail FP16-INT8 - CPU
  Face Detection FP16-INT8 - CPU
  Person Detection FP16 - CPU
  Machine Translation EN To DE FP16 - CPU
  Noise Suppression Poconet-Like FP16 - CPU
OpenSSL
Primesieve
Embree:
  Pathtracer ISPC - Asian Dragon Obj
  Pathtracer ISPC - Asian Dragon
OpenSSL
OpenFOAM
Primesieve
OSPRay Studio:
  3 - 4K - 16 - Path Tracer - CPU
  3 - 4K - 1 - Path Tracer - CPU
  1 - 4K - 1 - Path Tracer - CPU
  1 - 4K - 16 - Path Tracer - CPU
RocksDB
Embree
Parallel BZIP2 Compression
Blender:
  Classroom - CPU-Only
  Barbershop - CPU-Only
Speedb
SecureMark
OSPRay
Liquid-DSP
Blender:
  BMW27 - CPU-Only
  Pabellon Barcelona - CPU-Only
OpenVKL
Coremark
uvg266
Blender:
  Junkshop - CPU-Only
  Fishy Cat - CPU-Only
Timed Node.js Compilation
uvg266
Kvazaar
Timed LLVM Compilation
OSPRay:
  particle_volume/scivis/real_time
  particle_volume/ao/real_time
Pennant
Zstd Compression
Kvazaar
Zstd Compression
Helsing
WebP Image Encode
Liquid-DSP
High Performance Conjugate Gradient
Zstd Compression
PyBench
NAMD
Timed Godot Game Engine Compilation
GraphicsMagick
miniFE
OpenFOAM
GraphicsMagick
Algebraic Multi-Grid Benchmark
LAMMPS Molecular Dynamics Simulator
ClickHouse
Numpy Benchmark
Zstd Compression
GraphicsMagick
QMCPACK
ClickHouse:
  100M Rows Hits Dataset, Third Run
  100M Rows Hits Dataset, First Run / Cold Cache
CloverLeaf
Intel Open Image Denoise:
  RTLightmap.hdr.4096x4096 - CPU-Only
  RT.hdr_alb_nrm.3840x2160 - CPU-Only
  RT.ldr_alb_nrm.3840x2160 - CPU-Only
NAMD
FFmpeg
NWChem
Timed Gem5 Compilation
OpenRadioss
SVT-AV1
Xcompact3d Incompact3d
LAMMPS Molecular Dynamics Simulator
Timed Linux Kernel Compilation
OpenRadioss
SVT-AV1
uvg266
TensorFlow
SVT-AV1
OpenRadioss
GPAW
TensorFlow:
  CPU - 512 - ResNet-50
  CPU - 256 - AlexNet
Meta Performance Per Watts
OpenFOAM
CPU Power Consumption Monitor:
  Phoronix Test Suite System Monitoring
  CPU Power Consumption Monitor
  GhostRider - 1M
Xmrig
SVT-AV1:
  CPU Power Consumption Monitor
  Preset 13 - Bosphorus 4K
SVT-AV1
LuxCoreRender:
  CPU Power Consumption Monitor
  Orange Juice - CPU
LuxCoreRender
LuxCoreRender:
  CPU Power Consumption Monitor
  DLSC - CPU
LuxCoreRender
NAS Parallel Benchmarks:
  CPU Power Consumption Monitor
  CG.C
NAS Parallel Benchmarks
NAS Parallel Benchmarks:
  CPU Power Consumption Monitor
  MG.C
NAS Parallel Benchmarks
Pennant
Pennant
RELION
RELION
Speedb:
  CPU Power Consumption Monitor
  Read While Writing
Speedb

EPYC 9965

Processor: AMD EPYC 9965 192-Core @ 2.25GHz (192 Cores / 384 Threads), Motherboard: AMD VOLCANO (RVOT1000D BIOS), Chipset: AMD Device 153a, Memory: 12 x 64GB DDR5-6000MT/s Samsung M321R8GA0PB1-CCPKC, Disk: 2 x 1920GB KIOXIA KCD8XPUG1T92, Graphics: ASPEED, Network: Broadcom NetXtreme BCM5720 PCIe

OS: Ubuntu 24.04, Kernel: 6.10.0-phx (x86_64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xb101018
Java Notes: OpenJDK Runtime Environment (build 21.0.3-ea+7-Ubuntu-1build1)
Python Notes: Python 3.12.2
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 4 October 2024 14:32 by user phoronix.

EPYC 9575F

Processor: AMD EPYC 9575F 64-Core @ 3.30GHz (64 Cores / 128 Threads), Motherboard: AMD VOLCANO (RVOT1000D BIOS), Chipset: AMD Device 153a, Memory: 12 x 64GB DDR5-6000MT/s Samsung M321R8GA0PB1-CCPKC, Disk: 2 x 1920GB KIOXIA KCD8XPUG1T92, Graphics: ASPEED, Network: Broadcom NetXtreme BCM5720 PCIe

OS: Ubuntu 24.04, Kernel: 6.10.0-phx (x86_64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-OiuXZC/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xb002110
Java Notes: OpenJDK Runtime Environment (build 21.0.3-ea+7-Ubuntu-1build1)
Python Notes: Python 3.12.2
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 2 October 2024 15:16 by user phoronix.

NVIDIA GH200 144G HBM3e

Processor: ARMv8 Neoverse-V2 @ 3.47GHz (72 Cores), Motherboard: Pegatron JIMBO P4352 (00022432 BIOS), Memory: 1 x 480GB LPDDR5-6400MT/s NVIDIA 699-2G530-0236-RC1, Disk: 1000GB CT1000T700SSD3, Graphics: NVIDIA GH200 144G HBM3e 143GB, Network: 2 x Intel X550

OS: Ubuntu 24.04, Kernel: 6.8.0-47-generic-64k (aarch64), Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.6.65, Compiler: GCC 13.2.0 + CUDA 11.8, File-System: ext4, Screen Resolution: 1920x1200

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-fix-cortex-a53-843419 --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-dIwDw0/gcc-13-13.2.0/debian/tmp-nvptx/usr --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --program-prefix=aarch64-linux-gnu- --target=aarch64-linux-gnu --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-target-system-zlib=auto --without-cuda-driver -v
Processor Notes: Scaling Governor: cppc_cpufreq performance (Boost: Disabled)
Python Notes: Python 3.12.6
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 28 October 2024 17:33 by user x.