AMD EPYC Zen 1

AMD EPYC 7601 32-Core testing with a TYAN B8026T70AE24HR (V1.02.B10 BIOS) and llvmpipe on Ubuntu 23.10 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2401063-NE-AMDEPYCZE11
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EPYC 7601
January 05
  1 Day, 4 Hours, 15 Minutes
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EPYC 7601

Processor: AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads), Motherboard: TYAN B8026T70AE24HR (V1.02.B10 BIOS), Chipset: AMD 17h, Memory: 128GB, Disk: 1000GB INTEL SSDPE2KX010T8 + 280GB INTEL SSDPE21D280GA, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Broadcom NetXtreme BCM5720 PCIe

OS: Ubuntu 23.10, Kernel: 6.6.9-060609-generic (x86_64), Desktop: GNOME Shell 45.0, Display Server: X Server 1.21.1.7, OpenGL: 4.5 Mesa 23.2.1-1ubuntu3.1 (LLVM 15.0.7 256 bits), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/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-build-config=bootstrap-lto-lean --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: 0x800126e
Java Notes: OpenJDK Runtime Environment (build 11.0.21+9-post-Ubuntu-0ubuntu123.10)
Python Notes: Python 3.11.6
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 + retbleed: Mitigation of untrained return thunk; SMT vulnerable + spec_rstack_overflow: Mitigation of Safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: disabled RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

AMD EPYC Zen 1OpenBenchmarking.orgPhoronix Test SuiteAMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads)TYAN B8026T70AE24HR (V1.02.B10 BIOS)AMD 17h128GB1000GB INTEL SSDPE2KX010T8 + 280GB INTEL SSDPE21D280GAllvmpipeVE2282 x Broadcom NetXtreme BCM5720 PCIeUbuntu 23.106.6.9-060609-generic (x86_64)GNOME Shell 45.0X Server 1.21.1.74.5 Mesa 23.2.1-1ubuntu3.1 (LLVM 15.0.7 256 bits)GCC 13.2.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsMonitorNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionAMD EPYC Zen 1 BenchmarksSystem Logs- Transparent Huge Pages: madvise- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/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-build-config=bootstrap-lto-lean --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 - Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0x800126e - OpenJDK Runtime Environment (build 11.0.21+9-post-Ubuntu-0ubuntu123.10)- Python 3.11.6- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT vulnerable + spec_rstack_overflow: Mitigation of Safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: disabled RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

AMD EPYC Zen 1nginx: 500nginx: 1000openssl: RSA4096openssl: RSA4096openssl: SHA256openssl: SHA512openssl: AES-128-GCMopenssl: AES-256-GCMopenssl: ChaCha20openssl: ChaCha20-Poly1305apache: 1000apache-iotdb: 500 - 100 - 200 - 100apache-iotdb: 500 - 100 - 200 - 100apache-iotdb: 500 - 100 - 200 - 400apache-iotdb: 500 - 100 - 200 - 400apache-iotdb: 500 - 100 - 500 - 100apache-iotdb: 500 - 100 - 500 - 100apache-iotdb: 500 - 100 - 500 - 400apache-iotdb: 500 - 100 - 500 - 400apache-iotdb: 500 - 100 - 800 - 100apache-iotdb: 500 - 100 - 800 - 100apache-iotdb: 500 - 100 - 800 - 400apache-iotdb: 500 - 100 - 800 - 400apache-iotdb: 800 - 100 - 200 - 100apache-iotdb: 800 - 100 - 200 - 100apache-iotdb: 800 - 100 - 200 - 400apache-iotdb: 800 - 100 - 200 - 400apache-iotdb: 800 - 100 - 500 - 100apache-iotdb: 800 - 100 - 500 - 100apache-iotdb: 800 - 100 - 500 - 400apache-iotdb: 800 - 100 - 500 - 400apache-iotdb: 800 - 100 - 800 - 100apache-iotdb: 800 - 100 - 800 - 100apache-iotdb: 800 - 100 - 800 - 400apache-iotdb: 800 - 100 - 800 - 400duckdb: IMDBduckdb: TPC-H Parquetcassandra: Writesspeedb: Rand Readspeedb: Read While Writingspeedb: Read Rand Write Randspeedb: Update Randrocksdb: Rand Readrocksdb: Read While Writingrocksdb: Read Rand Write Randrocksdb: Update Randmemtier-benchmark: Redis - 100 - 1:10memtier-benchmark: Redis - 100 - 1:5compress-7zip: Compression Ratingcompress-7zip: Decompression Ratingbuild-linux-kernel: defconfigbuild-linux-kernel: allmodconfigbuild-llvm: Ninjabuild-llvm: Unix Makefilesbuild-nodejs: Time To Compilebuild-gem5: Time To Compilebuild-ffmpeg: Time To Compilequantlib: Multi-Threadedspecfem3d: Layered Halfspacespecfem3d: Water-layered Halfspacespecfem3d: Homogeneous Halfspacespecfem3d: Mount St. Helensspecfem3d: Tomographic Modelopenfoam: drivaerFastback, Small Mesh Size - Mesh Timeopenfoam: drivaerFastback, Small Mesh Size - Execution Timeopenfoam: drivaerFastback, Medium Mesh Size - Mesh Timeopenfoam: drivaerFastback, Medium Mesh Size - Execution Timeopenradioss: Chrysler Neon 1Mminibude: OpenMP - BM1minibude: OpenMP - BM1minibude: OpenMP - BM2minibude: OpenMP - BM2easywave: e2Asean Grid + BengkuluSept2007 Source - 1200easywave: e2Asean Grid + BengkuluSept2007 Source - 2400gpaw: Carbon Nanotubelammps: 20k Atomscloverleaf: clover_bm64_shortcloverleaf: clover_bm16kripke: incompact3d: input.i3d 193 Cells Per Directionincompact3d: X3D-benchmarking input.i3dnamd: ATPase Simulation - 327,506 Atomsgromacs: MPI CPU - water_GMX50_baremt-dgemm: Sustained Floating-Point Ratemrbayes: Primate Phylogeny Analysisblender: BMW27 - CPU-Onlyblender: Classroom - CPU-Onlyblender: Fishy Cat - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlyblender: Barbershop - CPU-Onlyembree: Pathtracer ISPC - Asian Dragonembree: Pathtracer ISPC - Crownoidn: RT.ldr_alb_nrm.3840x2160 - CPU-Onlyospray-studio: 1 - 4K - 1 - Path Tracer - CPUospray-studio: 1 - 4K - 16 - Path Tracer - CPUospray-studio: 1 - 4K - 32 - Path Tracer - CPUospray-studio: 3 - 4K - 1 - Path Tracer - CPUospray-studio: 3 - 4K - 16 - Path Tracer - CPUospray-studio: 3 - 4K - 32 - Path Tracer - CPUospray: gravity_spheres_volume/dim_512/ao/real_timeospray: gravity_spheres_volume/dim_512/scivis/real_timeospray: gravity_spheres_volume/dim_512/pathtracer/real_timeospray: particle_volume/ao/real_timeospray: particle_volume/scivis/real_timeospray: particle_volume/pathtracer/real_timev-ray: CPUindigobench: CPU - Supercarindigobench: CPU - Bedroomffmpeg: libx265 - Liveffmpeg: libx265 - Uploadffmpeg: libx265 - Platformffmpeg: libx265 - Video On Demandsvt-av1: Preset 13 - Bosphorus 4Ksvt-av1: Preset 12 - Bosphorus 4Ksvt-av1: Preset 8 - Bosphorus 4Ksvt-av1: Preset 4 - Bosphorus 4Krav1e: 10rav1e: 6rav1e: 5rav1e: 1x265: Bosphorus 4Kuvg266: Bosphorus 4K - Mediumuvg266: Bosphorus 4K - Very Fastuvg266: Bosphorus 4K - Super Fastuvg266: Bosphorus 4K - Ultra Fastvvenc: Bosphorus 4K - Fastvvenc: Bosphorus 4K - Fasteropenvino: Face Detection FP16-INT8 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP16 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Face Detection Retail FP16-INT8 - CPUopenvino: Face Detection Retail FP16-INT8 - CPUopenvino: Handwritten English Recognition FP16-INT8 - CPUopenvino: Handwritten English Recognition FP16-INT8 - CPUopenvino: Road Segmentation ADAS FP16-INT8 - CPUopenvino: Road Segmentation ADAS FP16-INT8 - CPUdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamtensorflow: CPU - 16 - ResNet-50pytorch: CPU - 1 - ResNet-50pytorch: CPU - 256 - ResNet-50xmrig: Monero - 1Mxmrig: Wownero - 1Mxmrig: CryptoNight-Femto UPX2 - 1Mxmrig: CryptoNight-Heavy - 1Mxmrig: KawPow - 1Mxmrig: GhostRider - 1MEPYC 7601102247.1498966.934520.9293636.72729540345383439633739780898875089867483183478735817833030447845788228.723855674946.4838853944162.645572244881.8155039355279.3460408895122.2261779270401.414718978738.2347189863145.625918093779.0959498447302.9161643882123.2160202628493.23199.322250.440153313862102866114827143475520141083065366338979115119322905671247481.471159703.1012608213602376.325755.324437.561532.477388.767394.21739.87064508.578.30224842174.73885889438.68835285330.01922020630.28310627541.846127123.21923226.935851180.3543498.82357.45614.298355.94614.238152.991355.129143.42413.749105.80978.1818435226737.55226141279.592350.974031.9963.762393193.36873.03193.36101.52247.45770.0021.820218.40020.488477146133281119100701709143318492.597422.467134.156035.278375.2349798.2406202238.6074.00658.3410.3821.0020.9968.38071.05626.5133.0396.9682.9482.2450.56614.578.6622.2023.3627.273.316.4783.822094.3812968.702.4443.79182.44377.3984.70296.7126.93470.4216.9841.47192.59877.959.09188.68169.37149.8253.35260.163661.411193.0253171.8486143.8266111.049810.91181448.312671.0645224.520971.7820222.238210.97211444.59309.29581695.4008145.5189109.7383155.2477102.960913.29411200.0420134.5223118.71309.9026.3621.406890.310393.07092.56932.66984.41104.3OpenBenchmarking.org

nginx

This is a benchmark of the lightweight Nginx HTTP(S) web-server. This Nginx web server benchmark test profile makes use of the wrk program for facilitating the HTTP requests over a fixed period time with a configurable number of concurrent clients/connections. HTTPS with a self-signed OpenSSL certificate is used by this test for local benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRequests Per Second, More Is Betternginx 1.23.2Connections: 500EPYC 760120K40K60K80K100KSE +/- 281.84, N = 3102247.141. (CC) gcc options: -lluajit-5.1 -lm -lssl -lcrypto -lpthread -ldl -std=c99 -O2
OpenBenchmarking.orgRequests Per Second Per Watt, More Is Betternginx 1.23.2Connections: 500EPYC 7601306090120150151.50
MinAvgMaxEPYC 7601241675712OpenBenchmarking.orgWatts, Fewer Is Betternginx 1.23.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 65242 Joules per run.

OpenBenchmarking.orgRequests Per Second, More Is Betternginx 1.23.2Connections: 1000EPYC 760120K40K60K80K100KSE +/- 581.17, N = 398966.931. (CC) gcc options: -lluajit-5.1 -lm -lssl -lcrypto -lpthread -ldl -std=c99 -O2
OpenBenchmarking.orgRequests Per Second Per Watt, More Is Betternginx 1.23.2Connections: 1000EPYC 7601306090120150146.51
MinAvgMaxEPYC 7601260675714OpenBenchmarking.orgWatts, Fewer Is Betternginx 1.23.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 65748 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.orgsign/s, More Is BetterOpenSSL 3.1Algorithm: RSA4096EPYC 760110002000300040005000SE +/- 20.79, N = 34520.91. (CC) gcc options: -pthread -m64 -O3 -ldl

OpenBenchmarking.orgverify/s, More Is BetterOpenSSL 3.1Algorithm: RSA4096EPYC 760160K120K180K240K300KSE +/- 943.05, N = 3293636.71. (CC) gcc options: -pthread -m64 -O3 -ldl
OpenBenchmarking.orgverify/s Per Watt, More Is BetterOpenSSL 3.1Algorithm: RSA4096EPYC 760190180270360450432.33
MinAvgMaxEPYC 7601259679713OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43469 Joules per run.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.1Algorithm: SHA256EPYC 76016000M12000M18000M24000M30000MSE +/- 33884690.29, N = 3272954034531. (CC) gcc options: -pthread -m64 -O3 -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.1Algorithm: SHA256EPYC 76018M16M24M32M40M39173939.34
MinAvgMaxEPYC 7601265697715OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 127974 Joules per run.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.1Algorithm: SHA512EPYC 76012000M4000M6000M8000M10000MSE +/- 14339737.74, N = 383439633731. (CC) gcc options: -pthread -m64 -O3 -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.1Algorithm: SHA512EPYC 76013M6M9M12M15M11949440.21
MinAvgMaxEPYC 7601264698712OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 128249 Joules per run.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.1Algorithm: AES-128-GCMEPYC 760120000M40000M60000M80000M100000MSE +/- 326361512.63, N = 3978089887501. (CC) gcc options: -pthread -m64 -O3 -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.1Algorithm: AES-128-GCMEPYC 760130M60M90M120M150M139960512.85
MinAvgMaxEPYC 7601264699713OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 128352 Joules per run.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.1Algorithm: AES-256-GCMEPYC 760120000M40000M60000M80000M100000MSE +/- 261314502.58, N = 3898674831831. (CC) gcc options: -pthread -m64 -O3 -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.1Algorithm: AES-256-GCMEPYC 760130M60M90M120M150M128779763.99
MinAvgMaxEPYC 7601263698714OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 127937 Joules per run.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.1Algorithm: ChaCha20EPYC 760110000M20000M30000M40000M50000MSE +/- 19889425.87, N = 3478735817831. (CC) gcc options: -pthread -m64 -O3 -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.1Algorithm: ChaCha20EPYC 760115M30M45M60M75M69981779.21
MinAvgMaxEPYC 7601263684710OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 125872 Joules per run.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.1Algorithm: ChaCha20-Poly1305EPYC 76016000M12000M18000M24000M30000MSE +/- 33396892.07, N = 3303044784571. (CC) gcc options: -pthread -m64 -O3 -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.1Algorithm: ChaCha20-Poly1305EPYC 76019M18M27M36M45M44232442.1
MinAvgMaxEPYC 7601265685713OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 125605 Joules per run.

Apache HTTP Server

This is a test of the Apache HTTPD web server. This Apache HTTPD web server benchmark test profile makes use of the wrk program for facilitating the HTTP requests over a fixed period time with a configurable number of concurrent clients. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRequests Per Second, More Is BetterApache HTTP Server 2.4.56Concurrent Requests: 1000EPYC 760120K40K60K80K100KSE +/- 364.41, N = 388228.721. (CC) gcc options: -lluajit-5.1 -lm -lssl -lcrypto -lpthread -ldl -std=c99 -O2
OpenBenchmarking.orgRequests Per Second Per Watt, More Is BetterApache HTTP Server 2.4.56Concurrent Requests: 1000EPYC 7601306090120150133.43
MinAvgMaxEPYC 7601262661707OpenBenchmarking.orgWatts, Fewer Is BetterApache HTTP Server 2.4.56CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 64141 Joules per run.

Apache IoTDB

Apache IotDB is a time series database and this benchmark is facilitated using the IoT Benchmaark [https://github.com/thulab/iot-benchmark/]. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100EPYC 76018M16M24M32M40MSE +/- 250473.81, N = 338556749

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100EPYC 76011122334455SE +/- 0.60, N = 346.48MAX: 13899.69
MinAvgMaxEPYC 7601261386705OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 45477 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400EPYC 76018M16M24M32M40MSE +/- 379243.48, N = 338853944

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400EPYC 76014080120160200SE +/- 2.77, N = 3162.64MAX: 27310.19
MinAvgMaxEPYC 7601265388688OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 47864 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100EPYC 760112M24M36M48M60MSE +/- 91826.95, N = 355722448

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100EPYC 760120406080100SE +/- 0.47, N = 381.81MAX: 12685.84
MinAvgMaxEPYC 7601253453696OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 78838 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400EPYC 760112M24M36M48M60MSE +/- 725985.88, N = 355039355

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400EPYC 760160120180240300SE +/- 5.14, N = 3279.34MAX: 29130.29
MinAvgMaxEPYC 7601253454699OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 81479 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100EPYC 760113M26M39M52M65MSE +/- 301284.23, N = 360408895

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100EPYC 7601306090120150SE +/- 0.52, N = 3122.22MAX: 11435.4
MinAvgMaxEPYC 7601252498698OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 114151 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400EPYC 760113M26M39M52M65MSE +/- 497201.21, N = 361779270

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400EPYC 760190180270360450SE +/- 7.91, N = 3401.41MAX: 27863.73
MinAvgMaxEPYC 7601252497696OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 114083 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100EPYC 760110M20M30M40M50MSE +/- 185384.87, N = 347189787

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100EPYC 7601918273645SE +/- 0.06, N = 338.23MAX: 23968.07
MinAvgMaxEPYC 7601253419706OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 59529 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400EPYC 760110M20M30M40M50MSE +/- 137590.10, N = 347189863

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400EPYC 7601306090120150SE +/- 1.72, N = 3145.62MAX: 28352.35
MinAvgMaxEPYC 7601253424700OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 61151 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100EPYC 760113M26M39M52M65MSE +/- 203912.87, N = 359180937

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100EPYC 760120406080100SE +/- 0.22, N = 379.09MAX: 23926.25
MinAvgMaxEPYC 7601252499690OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 116190 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400EPYC 760113M26M39M52M65MSE +/- 789480.85, N = 359498447

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400EPYC 760170140210280350SE +/- 7.59, N = 3302.91MAX: 28097.51
MinAvgMaxEPYC 7601253494693OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 117726 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100EPYC 760113M26M39M52M65MSE +/- 444016.97, N = 361643882

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100EPYC 7601306090120150SE +/- 0.87, N = 3123.21MAX: 24077.59
MinAvgMaxEPYC 7601252536711OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 175937 Joules per run.

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400EPYC 760113M26M39M52M65MSE +/- 476066.06, N = 360202628

OpenBenchmarking.orgAverage Latency, Fewer Is BetterApache IoTDB 1.2Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400EPYC 7601110220330440550SE +/- 3.92, N = 3493.23MAX: 28692.29
MinAvgMaxEPYC 7601254527710OpenBenchmarking.orgWatts, Fewer Is BetterApache IoTDB 1.2CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 178689 Joules per run.

DuckDB

DuckDB is an in-progress SQL OLAP database management system optimized for analytics and features a vectorized and parallel engine. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterDuckDB 0.9.1Benchmark: IMDBEPYC 76014080120160200SE +/- 0.58, N = 3199.321. (CXX) g++ options: -O3 -rdynamic -lssl -lcrypto -ldl
MinAvgMaxEPYC 7601204.9399.4590.7OpenBenchmarking.orgWatts, Fewer Is BetterDuckDB 0.9.1CPU Power Consumption Monitor1603204806408001. EPYC 7601: Approximate power consumption of 122742 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterDuckDB 0.9.1Benchmark: TPC-H ParquetEPYC 760150100150200250SE +/- 0.51, N = 3250.441. (CXX) g++ options: -O3 -rdynamic -lssl -lcrypto -ldl
MinAvgMaxEPYC 7601258.0315.2588.8OpenBenchmarking.orgWatts, Fewer Is BetterDuckDB 0.9.1CPU Power Consumption Monitor1603204806408001. EPYC 7601: Approximate power consumption of 109056 Joules per run.

Apache Cassandra

This is a benchmark of the Apache Cassandra NoSQL database management system making use of cassandra-stress. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterApache Cassandra 4.1.3Test: WritesEPYC 760130K60K90K120K150KSE +/- 878.30, N = 3153313
OpenBenchmarking.orgOp/s Per Watt, More Is BetterApache Cassandra 4.1.3Test: WritesEPYC 760160120180240300257.31
MinAvgMaxEPYC 7601255596719OpenBenchmarking.orgWatts, Fewer Is BetterApache Cassandra 4.1.3CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 88185 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 760120M40M60M80M100MSE +/- 884018.85, N = 3862102861. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterSpeedb 2.7Test: Random ReadEPYC 760130K60K90K120K150K126723.28
MinAvgMaxEPYC 7601263680714OpenBenchmarking.orgWatts, Fewer Is BetterSpeedb 2.7CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43766 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Read While WritingEPYC 76011.3M2.6M3.9M5.2M6.5MSE +/- 64578.15, N = 561148271. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterSpeedb 2.7Test: Read While WritingEPYC 76012K4K6K8K10K9032.65
MinAvgMaxEPYC 7601260677714OpenBenchmarking.orgWatts, Fewer Is BetterSpeedb 2.7CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43597 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Read Random Write RandomEPYC 7601300K600K900K1200K1500KSE +/- 2860.74, N = 314347551. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterSpeedb 2.7Test: Read Random Write RandomEPYC 760150010001500200025002257.83
MinAvgMaxEPYC 7601269635710OpenBenchmarking.orgWatts, Fewer Is BetterSpeedb 2.7CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 41305 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Update RandomEPYC 760140K80K120K160K200KSE +/- 300.37, N = 32014101. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterSpeedb 2.7Test: Update RandomEPYC 760190180270360450395.38
MinAvgMaxEPYC 7601261.3509.4579.9OpenBenchmarking.orgWatts, Fewer Is BetterSpeedb 2.7CPU Power Consumption Monitor1603204806408001. EPYC 7601: Approximate power consumption of 32942 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 8.0Test: Random ReadEPYC 760120M40M60M80M100MSE +/- 381556.63, N = 3830653661. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterRocksDB 8.0Test: Random ReadEPYC 760130K60K90K120K150K122252.45
MinAvgMaxEPYC 7601260679712OpenBenchmarking.orgWatts, Fewer Is BetterRocksDB 8.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43485 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterRocksDB 8.0Test: Read While WritingEPYC 7601700K1400K2100K2800K3500KSE +/- 24594.27, N = 333897911. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterRocksDB 8.0Test: Read While WritingEPYC 7601110022003300440055004987.78
MinAvgMaxEPYC 7601263680712OpenBenchmarking.orgWatts, Fewer Is BetterRocksDB 8.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43722 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterRocksDB 8.0Test: Read Random Write RandomEPYC 7601300K600K900K1200K1500KSE +/- 3730.48, N = 315119321. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterRocksDB 8.0Test: Read Random Write RandomEPYC 760150010001500200025002251.90
MinAvgMaxEPYC 7601263671712OpenBenchmarking.orgWatts, Fewer Is BetterRocksDB 8.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 42970 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterRocksDB 8.0Test: Update RandomEPYC 760160K120K180K240K300KSE +/- 255.89, N = 32905671. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterRocksDB 8.0Test: Update RandomEPYC 7601110220330440550501.43
MinAvgMaxEPYC 7601263579638OpenBenchmarking.orgWatts, Fewer Is BetterRocksDB 8.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 37279 Joules per run.

Redis 7.0.12 + memtier_benchmark

Memtier_benchmark is a NoSQL Redis/Memcache traffic generation plus benchmarking tool developed by Redis Labs. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOps/sec, More Is BetterRedis 7.0.12 + memtier_benchmark 2.0Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10EPYC 7601300K600K900K1200K1500KSE +/- 10426.11, N = 31247481.471. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
OpenBenchmarking.orgOps/sec Per Watt, More Is BetterRedis 7.0.12 + memtier_benchmark 2.0Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10EPYC 76014008001200160020001933.83
MinAvgMaxEPYC 7601260645714OpenBenchmarking.orgWatts, Fewer Is BetterRedis 7.0.12 + memtier_benchmark 2.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 45586 Joules per run.

OpenBenchmarking.orgOps/sec, More Is BetterRedis 7.0.12 + memtier_benchmark 2.0Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5EPYC 7601200K400K600K800K1000KSE +/- 8378.54, N = 31159703.101. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
OpenBenchmarking.orgOps/sec Per Watt, More Is BetterRedis 7.0.12 + memtier_benchmark 2.0Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5EPYC 76014008001200160020001802.37
MinAvgMaxEPYC 7601272643715OpenBenchmarking.orgWatts, Fewer Is BetterRedis 7.0.12 + memtier_benchmark 2.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 45684 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: Compression RatingEPYC 760130K60K90K120K150KSE +/- 597.93, N = 31260821. (CXX) g++ options: -lpthread -ldl -O2 -fPIC

OpenBenchmarking.orgMIPS, More Is Better7-Zip Compression 22.01Test: Decompression RatingEPYC 760130K60K90K120K150KSE +/- 1266.83, N = 31360231. (CXX) g++ options: -lpthread -ldl -O2 -fPIC
OpenBenchmarking.orgMIPS Per Watt, More Is Better7-Zip Compression 22.01Test: Decompression RatingEPYC 760150100150200250215.04
MinAvgMaxEPYC 7601253633714OpenBenchmarking.orgWatts, Fewer Is Better7-Zip Compression 22.01CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 36266 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 6.1Build: defconfigEPYC 760120406080100SE +/- 0.90, N = 376.33
MinAvgMaxEPYC 7601128527718OpenBenchmarking.orgWatts, Fewer Is BetterTimed Linux Kernel Compilation 6.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 47605 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Linux Kernel Compilation 6.1Build: allmodconfigEPYC 7601160320480640800SE +/- 0.82, N = 3755.32
OpenBenchmarking.orgWatts, Fewer Is BetterTimed Linux Kernel Compilation 6.1CPU Power Consumption MonitorEPYC 7601130260390520650Min: 252.98 / Avg: 676.51 / Max: 718.811. EPYC 7601: Approximate power consumption of 522721 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 760190180270360450SE +/- 2.68, N = 3437.56
OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 16.0CPU Power Consumption MonitorEPYC 7601130260390520650Min: 255.68 / Avg: 644.25 / Max: 716.841. EPYC 7601: Approximate power consumption of 297431 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed LLVM Compilation 16.0Build System: Unix MakefilesEPYC 7601120240360480600SE +/- 1.64, N = 3532.48
OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 16.0CPU Power Consumption MonitorEPYC 7601130260390520650Min: 157.96 / Avg: 592.01 / Max: 716.841. EPYC 7601: Approximate power consumption of 334089 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 19.8.1Time To CompileEPYC 760180160240320400SE +/- 0.70, N = 3388.77
MinAvgMaxEPYC 7601174624717OpenBenchmarking.orgWatts, Fewer Is BetterTimed Node.js Compilation 19.8.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 248198 Joules per run.

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 760190180270360450SE +/- 4.34, N = 9394.22
OpenBenchmarking.orgWatts, Fewer Is BetterTimed Gem5 Compilation 23.0.1CPU Power Consumption MonitorEPYC 7601130260390520650Min: 141.91 / Avg: 529.47 / Max: 719.641. EPYC 7601: Approximate power consumption of 211669 Joules per run.

Timed FFmpeg Compilation

This test times how long it takes to build the FFmpeg multimedia library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed FFmpeg Compilation 6.1Time To CompileEPYC 7601918273645SE +/- 0.07, N = 339.87
MinAvgMaxEPYC 7601255523718OpenBenchmarking.orgWatts, Fewer Is BetterTimed FFmpeg Compilation 6.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 25300 Joules per run.

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 760114K28K42K56K70KSE +/- 155.83, N = 364508.51. (CXX) g++ options: -O3 -march=native -fPIE -pie
OpenBenchmarking.orgMFLOPS Per Watt, More Is BetterQuantLib 1.32Configuration: Multi-ThreadedEPYC 76012040608010096.81
MinAvgMaxEPYC 7601258666715OpenBenchmarking.orgWatts, Fewer Is BetterQuantLib 1.32CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 69521 Joules per run.

SPECFEM3D

simulates acoustic (fluid), elastic (solid), coupled acoustic/elastic, poroelastic or seismic wave propagation in any type of conforming mesh of hexahedra. This test profile currently relies on CPU-based execution for SPECFEM3D and using a variety of their built-in examples/models for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Layered HalfspaceEPYC 760120406080100SE +/- 0.22, N = 378.301. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
MinAvgMaxEPYC 7601260668710OpenBenchmarking.orgWatts, Fewer Is BetterSPECFEM3D 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 57265 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Water-layered HalfspaceEPYC 760120406080100SE +/- 0.26, N = 374.741. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
MinAvgMaxEPYC 7601266651706OpenBenchmarking.orgWatts, Fewer Is BetterSPECFEM3D 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 54459 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Homogeneous HalfspaceEPYC 7601918273645SE +/- 0.55, N = 338.691. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
MinAvgMaxEPYC 7601264650715OpenBenchmarking.orgWatts, Fewer Is BetterSPECFEM3D 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 30104 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Mount St. HelensEPYC 7601714212835SE +/- 0.22, N = 330.021. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
MinAvgMaxEPYC 7601263636711OpenBenchmarking.orgWatts, Fewer Is BetterSPECFEM3D 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 23334 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Tomographic ModelEPYC 7601714212835SE +/- 0.10, N = 330.281. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
MinAvgMaxEPYC 7601262640715OpenBenchmarking.orgWatts, Fewer Is BetterSPECFEM3D 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 23664 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 - Mesh TimeEPYC 7601102030405041.851. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Small Mesh Size - Execution TimeEPYC 7601306090120150123.221. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm
MinAvgMaxEPYC 7601262657697OpenBenchmarking.orgWatts, Fewer Is BetterOpenFOAM 10CPU Power Consumption Monitor2004006008001000

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Medium Mesh Size - Mesh TimeEPYC 760150100150200250226.941. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Medium Mesh Size - Execution TimeEPYC 7601300600900120015001180.351. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm
OpenBenchmarking.orgWatts, Fewer Is BetterOpenFOAM 10CPU Power Consumption MonitorEPYC 7601120240360480600Min: 264.52 / Avg: 640.81 / Max: 706.65

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 7601110220330440550SE +/- 1.47, N = 3498.82
OpenBenchmarking.orgWatts, Fewer Is BetterOpenRadioss 2023.09.15CPU Power Consumption MonitorEPYC 7601120240360480600Min: 266.18 / Avg: 627.09 / Max: 692.761. EPYC 7601: Approximate power consumption of 344065 Joules per run.

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: BM1EPYC 760180160240320400SE +/- 0.39, N = 3357.461. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm

OpenBenchmarking.orgBillion Interactions/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM1EPYC 760148121620SE +/- 0.02, N = 314.301. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
OpenBenchmarking.orgBillion Interactions/s Per Watt, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM1EPYC 76010.0050.010.0150.020.0250.022
MinAvgMaxEPYC 7601266659704OpenBenchmarking.orgWatts, Fewer Is BetterminiBUDE 20210901CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 39743 Joules per run.

OpenBenchmarking.orgGFInst/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM2EPYC 760180160240320400SE +/- 0.42, N = 3355.951. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm

OpenBenchmarking.orgBillion Interactions/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM2EPYC 760148121620SE +/- 0.02, N = 314.241. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
OpenBenchmarking.orgBillion Interactions/s Per Watt, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM2EPYC 76010.00470.00940.01410.01880.02350.021
MinAvgMaxEPYC 7601264676704OpenBenchmarking.orgWatts, Fewer Is BetterminiBUDE 20210901CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 246288 Joules per run.

easyWave

The easyWave software allows simulating tsunami generation and propagation in the context of early warning systems. EasyWave supports making use of OpenMP for CPU multi-threading and there are also GPU ports available but not currently incorporated as part of this test profile. The easyWave tsunami generation software is run with one of the example/reference input files for measuring the CPU execution time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BettereasyWave r34Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200EPYC 7601306090120150SE +/- 2.54, N = 9152.991. (CXX) g++ options: -O3 -fopenmp
OpenBenchmarking.orgWatts, Fewer Is BettereasyWave r34CPU Power Consumption MonitorEPYC 7601110220330440550Min: 249.6 / Avg: 522.07 / Max: 595.641. EPYC 7601: Approximate power consumption of 83415 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BettereasyWave r34Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400EPYC 760180160240320400SE +/- 4.29, N = 9355.131. (CXX) g++ options: -O3 -fopenmp
OpenBenchmarking.orgWatts, Fewer Is BettereasyWave r34CPU Power Consumption MonitorEPYC 7601110220330440550Min: 258.39 / Avg: 525.08 / Max: 599.481. EPYC 7601: Approximate power consumption of 190195 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 7601306090120150SE +/- 1.55, N = 5143.421. (CC) gcc options: -shared -fwrapv -O2 -lxc -lblas -lmpi
MinAvgMaxEPYC 7601263666715OpenBenchmarking.orgWatts, Fewer Is BetterGPAW 23.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 99038 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 760148121620SE +/- 0.05, N = 313.751. (CXX) g++ options: -O3 -lm -ldl
OpenBenchmarking.orgns/day Per Watt, More Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022Model: 20k AtomsEPYC 76010.00450.0090.01350.0180.02250.02
OpenBenchmarking.orgWatts, Fewer Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022CPU Power Consumption MonitorEPYC 7601120240360480600Min: 264.67 / Avg: 689.74 / Max: 702.541. EPYC 7601: Approximate power consumption of 435455 Joules per run.

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 760120406080100SE +/- 0.56, N = 3105.801. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp
MinAvgMaxEPYC 7601267587646OpenBenchmarking.orgWatts, Fewer Is BetterCloverLeaf 1.3CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 65364 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterCloverLeaf 1.3Input: clover_bm16EPYC 76012004006008001000SE +/- 2.36, N = 3978.181. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp
OpenBenchmarking.orgWatts, Fewer Is BetterCloverLeaf 1.3CPU Power Consumption MonitorEPYC 7601110220330440550Min: 263.01 / Avg: 591.37 / Max: 637.791. EPYC 7601: Approximate power consumption of 578165 Joules per run.

Kripke

Kripke is a simple, scalable, 3D Sn deterministic particle transport code. Its primary purpose is to research how data layout, programming paradigms and architectures effect the implementation and performance of Sn transport. Kripke is developed by LLNL. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgThroughput FoM, More Is BetterKripke 1.2.6EPYC 760140M80M120M160M200MSE +/- 2458810.58, N = 31843522671. (CXX) g++ options: -O3 -fopenmp -ldl
OpenBenchmarking.orgThroughput FoM Per Watt, More Is BetterKripke 1.2.6EPYC 760160K120K180K240K300K287085.20
MinAvgMaxEPYC 7601266642710OpenBenchmarking.orgWatts, Fewer Is BetterKripke 1.2.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 31465 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: input.i3d 193 Cells Per DirectionEPYC 7601918273645SE +/- 0.14, N = 337.551. (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
MinAvgMaxEPYC 7601263577642OpenBenchmarking.orgWatts, Fewer Is BetterXcompact3d Incompact3d 2021-03-11CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 24611 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterXcompact3d Incompact3d 2021-03-11Input: X3D-benchmarking input.i3dEPYC 760130060090012001500SE +/- 15.06, N = 91279.591. (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
OpenBenchmarking.orgWatts, Fewer Is BetterXcompact3d Incompact3d 2021-03-11CPU Power Consumption MonitorEPYC 7601120240360480600Min: 241.38 / Avg: 584.29 / Max: 692.591. EPYC 7601: Approximate power consumption of 749321 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.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsEPYC 76010.21920.43840.65760.87681.096SE +/- 0.00114, N = 30.97403
MinAvgMaxEPYC 7601264663713OpenBenchmarking.orgWatts, Fewer Is BetterNAMD 2.14CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43738 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 2023Implementation: MPI CPU - Input: water_GMX50_bareEPYC 76010.44910.89821.34731.79642.2455SE +/- 0.021, N = 31.9961. (CXX) g++ options: -O3
OpenBenchmarking.orgNs Per Day Per Watt, More Is BetterGROMACS 2023Implementation: MPI CPU - Input: water_GMX50_bareEPYC 76010.00070.00140.00210.00280.00350.003
MinAvgMaxEPYC 7601263668709OpenBenchmarking.orgWatts, Fewer Is BetterGROMACS 2023CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 67924 Joules per run.

ACES DGEMM

This is a multi-threaded DGEMM benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOP/s, More Is BetterACES DGEMM 1.0Sustained Floating-Point RateEPYC 76010.84651.6932.53953.3864.2325SE +/- 0.067646, N = 153.7623931. (CC) gcc options: -O3 -march=native -fopenmp
OpenBenchmarking.orgGFLOP/s Per Watt, More Is BetterACES DGEMM 1.0Sustained Floating-Point RateEPYC 76010.00140.00280.00420.00560.0070.006
MinAvgMaxEPYC 7601263615677OpenBenchmarking.orgWatts, Fewer Is BetterACES DGEMM 1.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 40441 Joules per run.

Timed MrBayes Analysis

This test performs a bayesian analysis of a set of primate genome sequences in order to estimate their phylogeny. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed MrBayes Analysis 3.2.7Primate Phylogeny AnalysisEPYC 76014080120160200SE +/- 0.03, N = 3193.371. (CC) gcc options: -mmmx -msse -msse2 -msse3 -mssse3 -msse4.1 -msse4.2 -msse4a -msha -maes -mavx -mfma -mavx2 -mrdrnd -mbmi -mbmi2 -madx -mabm -O3 -std=c99 -pedantic -lm -lreadline
MinAvgMaxEPYC 7601267687711OpenBenchmarking.orgWatts, Fewer Is BetterTimed MrBayes Analysis 3.2.7CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 135597 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 4.0Blend File: BMW27 - Compute: CPU-OnlyEPYC 76011632486480SE +/- 0.24, N = 373.03
MinAvgMaxEPYC 7601263675715OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 52662 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Classroom - Compute: CPU-OnlyEPYC 76014080120160200SE +/- 0.51, N = 3193.36
MinAvgMaxEPYC 7601263696713OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 137354 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Fishy Cat - Compute: CPU-OnlyEPYC 760120406080100SE +/- 0.40, N = 3101.52
MinAvgMaxEPYC 7601263682716OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 72980 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Pabellon Barcelona - Compute: CPU-OnlyEPYC 760150100150200250SE +/- 0.16, N = 3247.45
MinAvgMaxEPYC 7601263699714OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.0CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 175815 Joules per run.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Barbershop - Compute: CPU-OnlyEPYC 7601170340510680850SE +/- 0.16, N = 3770.00
OpenBenchmarking.orgWatts, Fewer Is BetterBlender 4.0CPU Power Consumption MonitorEPYC 7601130260390520650Min: 263.43 / Avg: 701.62 / Max: 714.731. EPYC 7601: Approximate power consumption of 544461 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 DragonEPYC 7601510152025SE +/- 0.06, N = 321.82MIN: 21.6 / MAX: 22.27
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian DragonEPYC 76010.00770.01540.02310.03080.03850.034
MinAvgMaxEPYC 7601265649712OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 4.3CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 25729 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: CrownEPYC 7601510152025SE +/- 0.02, N = 318.40MIN: 18.17 / MAX: 18.71
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: CrownEPYC 76010.00630.01260.01890.02520.03150.028
MinAvgMaxEPYC 7601265660711OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 4.3CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 29912 Joules per run.

Intel Open Image Denoise

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 2.1Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-OnlyEPYC 76010.1080.2160.3240.4320.54SE +/- 0.00, N = 30.48
OpenBenchmarking.orgImages / Sec Per Watt, More Is BetterIntel Open Image Denoise 2.1Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-OnlyEPYC 76010.00020.00040.00060.00080.0010.001
MinAvgMaxEPYC 7601265659700OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 2.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 44399 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 0.13Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUEPYC 76012K4K6K8K10KSE +/- 7.00, N = 38477
MinAvgMaxEPYC 7601263578714OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.13CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 56992 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUEPYC 760130K60K90K120K150KSE +/- 162.97, N = 3146133
MinAvgMaxEPYC 7601264629715OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.13CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 104567 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUEPYC 760160K120K180K240K300KSE +/- 171.33, N = 3281119
MinAvgMaxEPYC 7601261664715OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.13CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 199540 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUEPYC 76012K4K6K8K10KSE +/- 41.15, N = 310070
MinAvgMaxEPYC 7601264581715OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.13CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 58679 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUEPYC 760140K80K120K160K200KSE +/- 353.87, N = 3170914
MinAvgMaxEPYC 7601262639714OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.13CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 122140 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUEPYC 760170K140K210K280K350KSE +/- 39.68, N = 3331849
MinAvgMaxEPYC 7601262669715OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.13CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 234904 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 2.12Benchmark: gravity_spheres_volume/dim_512/ao/real_timeEPYC 76010.58441.16881.75322.33762.922SE +/- 0.00623, N = 32.59742
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 2.12Benchmark: gravity_spheres_volume/dim_512/ao/real_timeEPYC 76010.00090.00180.00270.00360.00450.004
MinAvgMaxEPYC 7601264623711OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 2.12CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 36337 Joules per run.

OpenBenchmarking.orgItems Per Second, More Is BetterOSPRay 2.12Benchmark: gravity_spheres_volume/dim_512/scivis/real_timeEPYC 76010.55511.11021.66532.22042.7755SE +/- 0.01201, N = 32.46713
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 2.12Benchmark: gravity_spheres_volume/dim_512/scivis/real_timeEPYC 76010.00090.00180.00270.00360.00450.004
MinAvgMaxEPYC 7601262617710OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 2.12CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 36410 Joules per run.

OpenBenchmarking.orgItems Per Second, More Is BetterOSPRay 2.12Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_timeEPYC 76010.93511.87022.80533.74044.6755SE +/- 0.00653, N = 34.15603
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 2.12Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_timeEPYC 76010.00160.00320.00480.00640.0080.007
MinAvgMaxEPYC 7601261632708OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 2.12CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 53063 Joules per run.

OpenBenchmarking.orgItems Per Second, More Is BetterOSPRay 2.12Benchmark: particle_volume/ao/real_timeEPYC 76011.18762.37523.56284.75045.938SE +/- 0.00806, N = 35.27837
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 2.12Benchmark: particle_volume/ao/real_timeEPYC 76010.00180.00360.00540.00720.0090.008
MinAvgMaxEPYC 7601262680701OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 2.12CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 85916 Joules per run.

OpenBenchmarking.orgItems Per Second, More Is BetterOSPRay 2.12Benchmark: particle_volume/scivis/real_timeEPYC 76011.17792.35583.53374.71165.8895SE +/- 0.00820, N = 35.23497
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 2.12Benchmark: particle_volume/scivis/real_timeEPYC 76010.00180.00360.00540.00720.0090.008
MinAvgMaxEPYC 7601263623703OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 2.12CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 112067 Joules per run.

OpenBenchmarking.orgItems Per Second, More Is BetterOSPRay 2.12Benchmark: particle_volume/pathtracer/real_timeEPYC 760120406080100SE +/- 0.37, N = 398.24
OpenBenchmarking.orgItems Per Second Per Watt, More Is BetterOSPRay 2.12Benchmark: particle_volume/pathtracer/real_timeEPYC 76010.03650.0730.10950.1460.18250.162
MinAvgMaxEPYC 7601264608699OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay 2.12CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 94595 Joules per run.

Chaos Group V-RAY

This is a test of Chaos Group's V-RAY benchmark. V-RAY is a commercial renderer that can integrate with various creator software products like SketchUp and 3ds Max. The V-RAY benchmark is standalone and supports CPU and NVIDIA CUDA/RTX based rendering. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgvsamples, More Is BetterChaos Group V-RAY 5.02Mode: CPUEPYC 76014K8K12K16K20KSE +/- 36.50, N = 320223
OpenBenchmarking.orgvsamples Per Watt, More Is BetterChaos Group V-RAY 5.02Mode: CPUEPYC 760181624324034.60
MinAvgMaxEPYC 7601260585717OpenBenchmarking.orgWatts, Fewer Is BetterChaos Group V-RAY 5.02CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 51438 Joules per run.

IndigoBench

This is a test of Indigo Renderer's IndigoBench benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgM samples/s, More Is BetterIndigoBench 4.4Acceleration: CPU - Scene: SupercarEPYC 7601246810SE +/- 0.015, N = 38.607
OpenBenchmarking.orgM samples/s Per Watt, More Is BetterIndigoBench 4.4Acceleration: CPU - Scene: SupercarEPYC 76010.00290.00580.00870.01160.01450.013
MinAvgMaxEPYC 7601261667718OpenBenchmarking.orgWatts, Fewer Is BetterIndigoBench 4.4CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 44434 Joules per run.

OpenBenchmarking.orgM samples/s, More Is BetterIndigoBench 4.4Acceleration: CPU - Scene: BedroomEPYC 76010.90141.80282.70423.60564.507SE +/- 0.007, N = 34.006
OpenBenchmarking.orgM samples/s Per Watt, More Is BetterIndigoBench 4.4Acceleration: CPU - Scene: BedroomEPYC 76010.00140.00280.00420.00560.0070.006
MinAvgMaxEPYC 7601262663717OpenBenchmarking.orgWatts, Fewer Is BetterIndigoBench 4.4CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 45309 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 6.1Encoder: libx265 - Scenario: LiveEPYC 76011326395265SE +/- 0.07, N = 358.341. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma
OpenBenchmarking.orgFPS Per Watt, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: LiveEPYC 76010.03960.07920.11880.15840.1980.176
MinAvgMaxEPYC 7601256.6331.4522.3OpenBenchmarking.orgWatts, Fewer Is BetterFFmpeg 6.1CPU Power Consumption Monitor1302603905206501. EPYC 7601: Approximate power consumption of 40099 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: UploadEPYC 76013691215SE +/- 0.01, N = 310.381. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma
OpenBenchmarking.orgFPS Per Watt, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: UploadEPYC 76010.0070.0140.0210.0280.0350.031
MinAvgMaxEPYC 7601252.8340.3554.4OpenBenchmarking.orgWatts, Fewer Is BetterFFmpeg 6.1CPU Power Consumption Monitor1402804205607001. EPYC 7601: Approximate power consumption of 117390 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: PlatformEPYC 7601510152025SE +/- 0.01, N = 321.001. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma
OpenBenchmarking.orgFPS Per Watt, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: PlatformEPYC 76010.01350.0270.04050.0540.06750.06
MinAvgMaxEPYC 7601179.7349.1507.6OpenBenchmarking.orgWatts, Fewer Is BetterFFmpeg 6.1CPU Power Consumption Monitor1302603905206501. EPYC 7601: Approximate power consumption of 137896 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: Video On DemandEPYC 7601510152025SE +/- 0.01, N = 320.991. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma
OpenBenchmarking.orgFPS Per Watt, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: Video On DemandEPYC 76010.01350.0270.04050.0540.06750.06
MinAvgMaxEPYC 7601201.1349.5507.7OpenBenchmarking.orgWatts, Fewer Is BetterFFmpeg 6.1CPU Power Consumption Monitor1302603905206501. EPYC 7601: Approximate power consumption of 138304 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 1.8Encoder Mode: Preset 13 - Input: Bosphorus 4KEPYC 76011530456075SE +/- 3.17, N = 1268.381. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 13 - Input: Bosphorus 4KEPYC 76010.04010.08020.12030.16040.20050.178
MinAvgMaxEPYC 7601249384645OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 1.8CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 8228 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 12 - Input: Bosphorus 4KEPYC 76011632486480SE +/- 0.94, N = 1571.061. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 12 - Input: Bosphorus 4KEPYC 76010.04030.08060.12090.16120.20150.179
MinAvgMaxEPYC 7601263397653OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 1.8CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 8517 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 8 - Input: Bosphorus 4KEPYC 7601612182430SE +/- 0.36, N = 326.511. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 8 - Input: Bosphorus 4KEPYC 76010.01220.02440.03660.04880.0610.054
MinAvgMaxEPYC 7601266493682OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 1.8CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 17243 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 4 - Input: Bosphorus 4KEPYC 76010.68381.36762.05142.73523.419SE +/- 0.012, N = 33.0391. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 4 - Input: Bosphorus 4KEPYC 76010.00140.00280.00420.00560.0070.006
MinAvgMaxEPYC 7601255496685OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 1.8CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 32540 Joules per run.

rav1e

Xiph rav1e is a Rust-written AV1 video encoder that claims to be the fastest and safest AV1 encoder. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is Betterrav1e 0.7Speed: 10EPYC 7601246810SE +/- 0.051, N = 36.968
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betterrav1e 0.7Speed: 10EPYC 76010.00450.0090.01350.0180.02250.02
MinAvgMaxEPYC 7601253.6349.3376.3OpenBenchmarking.orgWatts, Fewer Is Betterrav1e 0.7CPU Power Consumption Monitor1002003004005001. EPYC 7601: Approximate power consumption of 31667 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is Betterrav1e 0.7Speed: 6EPYC 76010.66331.32661.98992.65323.3165SE +/- 0.023, N = 32.948
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betterrav1e 0.7Speed: 6EPYC 76010.00180.00360.00540.00720.0090.008
MinAvgMaxEPYC 7601187.9370.1417.3OpenBenchmarking.orgWatts, Fewer Is Betterrav1e 0.7CPU Power Consumption Monitor1102203304405501. EPYC 7601: Approximate power consumption of 26520 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is Betterrav1e 0.7Speed: 5EPYC 76010.50511.01021.51532.02042.5255SE +/- 0.010, N = 32.245
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betterrav1e 0.7Speed: 5EPYC 76010.00140.00280.00420.00560.0070.006
MinAvgMaxEPYC 7601265.8372.0500.9OpenBenchmarking.orgWatts, Fewer Is Betterrav1e 0.7CPU Power Consumption Monitor1302603905206501. EPYC 7601: Approximate power consumption of 34719 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is Betterrav1e 0.7Speed: 1EPYC 76010.12740.25480.38220.50960.637SE +/- 0.001, N = 30.566
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betterrav1e 0.7Speed: 1EPYC 76010.00020.00040.00060.00080.0010.001
MinAvgMaxEPYC 7601260.9423.5543.9OpenBenchmarking.orgWatts, Fewer Is Betterrav1e 0.7CPU Power Consumption Monitor1402804205607001. EPYC 7601: Approximate power consumption of 61691 Joules per run.

x265

This is a simple test of the x265 encoder run on the CPU with 1080p and 4K options for H.265 video encode performance with x265. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is Betterx265 3.4Video Input: Bosphorus 4KEPYC 760148121620SE +/- 0.10, N = 1514.571. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betterx265 3.4Video Input: Bosphorus 4KEPYC 76010.00630.01260.01890.02520.03150.028
MinAvgMaxEPYC 7601259.0515.5599.5OpenBenchmarking.orgWatts, Fewer Is Betterx265 3.4CPU Power Consumption Monitor1603204806408001. EPYC 7601: Approximate power consumption of 23437 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 7601246810SE +/- 0.03, N = 38.66
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: MediumEPYC 76010.00290.00580.00870.01160.01450.013
MinAvgMaxEPYC 7601260659703OpenBenchmarking.orgWatts, Fewer Is Betteruvg266 0.4.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 48344 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: Very FastEPYC 7601510152025SE +/- 0.08, N = 322.20
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: Very FastEPYC 76010.00810.01620.02430.03240.04050.036
MinAvgMaxEPYC 7601261610681OpenBenchmarking.orgWatts, Fewer Is Betteruvg266 0.4.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 19102 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: Super FastEPYC 7601612182430SE +/- 0.08, N = 323.36
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: Super FastEPYC 76010.00860.01720.02580.03440.0430.038
MinAvgMaxEPYC 7601260617690OpenBenchmarking.orgWatts, Fewer Is Betteruvg266 0.4.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 18510 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: Ultra FastEPYC 7601612182430SE +/- 0.02, N = 327.27
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betteruvg266 0.4.1Video Input: Bosphorus 4K - Video Preset: Ultra FastEPYC 76010.01060.02120.03180.04240.0530.047
MinAvgMaxEPYC 7601260586668OpenBenchmarking.orgWatts, Fewer Is Betteruvg266 0.4.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 15421 Joules per run.

VVenC

VVenC is the Fraunhofer Versatile Video Encoder as a fast/efficient H.266/VVC encoder. The vvenc encoder makes use of SIMD Everywhere (SIMDe). The vvenc software is published under the Clear BSD License. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 4K - Video Preset: FastEPYC 76010.74481.48962.23442.97923.724SE +/- 0.02, N = 33.311. (CXX) g++ options: -O3 -flto=auto -fno-fat-lto-objects
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterVVenC 1.9Video Input: Bosphorus 4K - Video Preset: FastEPYC 76010.00140.00280.00420.00560.0070.006
MinAvgMaxEPYC 7601259512711OpenBenchmarking.orgWatts, Fewer Is BetterVVenC 1.9CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 96326 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 4K - Video Preset: FasterEPYC 7601246810SE +/- 0.027, N = 36.4781. (CXX) g++ options: -O3 -flto=auto -fno-fat-lto-objects
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterVVenC 1.9Video Input: Bosphorus 4K - Video Preset: FasterEPYC 76010.00270.00540.00810.01080.01350.012
MinAvgMaxEPYC 7601265523658OpenBenchmarking.orgWatts, Fewer Is BetterVVenC 1.9CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 51952 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 2023.2.devModel: Face Detection FP16-INT8 - Device: CPUEPYC 76010.85951.7192.57853.4384.2975SE +/- 0.00, N = 33.821. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Face Detection FP16-INT8 - Device: CPUEPYC 7601400800120016002000SE +/- 0.19, N = 32094.38MIN: 2092.56 / MAX: 2107.431. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601263598661OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43028 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUEPYC 76013K6K9K12K15KSE +/- 41.63, N = 312968.701. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUEPYC 76010.5491.0981.6472.1962.745SE +/- 0.01, N = 32.44MIN: 2.4 / MAX: 10.41. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601260630668OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 40921 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Person Detection FP16 - Device: CPUEPYC 76011020304050SE +/- 0.14, N = 343.791. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Person Detection FP16 - Device: CPUEPYC 76014080120160200SE +/- 0.54, N = 3182.44MIN: 171.2 / MAX: 204.21. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601262669709OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43945 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Weld Porosity Detection FP16-INT8 - Device: CPUEPYC 760180160240320400SE +/- 0.04, N = 3377.391. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Weld Porosity Detection FP16-INT8 - Device: CPUEPYC 760120406080100SE +/- 0.01, N = 384.70MIN: 84.34 / MAX: 93.361. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601261607648OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 39439 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Vehicle Detection FP16-INT8 - Device: CPUEPYC 760160120180240300SE +/- 0.08, N = 3296.711. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Vehicle Detection FP16-INT8 - Device: CPUEPYC 7601612182430SE +/- 0.01, N = 326.93MIN: 26.82 / MAX: 37.141. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601261616658OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 39851 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Person Vehicle Bike Detection FP16 - Device: CPUEPYC 7601100200300400500SE +/- 1.69, N = 3470.421. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Person Vehicle Bike Detection FP16 - Device: CPUEPYC 760148121620SE +/- 0.06, N = 316.98MIN: 16.32 / MAX: 31.761. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601262662707OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43456 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Machine Translation EN To DE FP16 - Device: CPUEPYC 7601918273645SE +/- 0.20, N = 341.471. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Machine Translation EN To DE FP16 - Device: CPUEPYC 76014080120160200SE +/- 0.95, N = 3192.59MIN: 169.23 / MAX: 247.321. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601263665707OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43911 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Face Detection Retail FP16-INT8 - Device: CPUEPYC 76012004006008001000SE +/- 0.47, N = 3877.951. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Face Detection Retail FP16-INT8 - Device: CPUEPYC 76013691215SE +/- 0.00, N = 39.09MIN: 9.03 / MAX: 17.891. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601263604643OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 39278 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Handwritten English Recognition FP16-INT8 - Device: CPUEPYC 76014080120160200SE +/- 0.20, N = 3188.681. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Handwritten English Recognition FP16-INT8 - Device: CPUEPYC 76014080120160200SE +/- 0.18, N = 3169.37MIN: 138.88 / MAX: 192.11. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601263672710OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 43872 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Road Segmentation ADAS FP16-INT8 - Device: CPUEPYC 7601306090120150SE +/- 0.12, N = 3149.821. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Road Segmentation ADAS FP16-INT8 - Device: CPUEPYC 76011224364860SE +/- 0.04, N = 353.35MIN: 52.66 / MAX: 66.861. (CXX) g++ options: -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -pie
MinAvgMaxEPYC 7601262616670OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2023.2.devCPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 40443 Joules per run.

Neural Magic DeepSparse

This is a benchmark of Neural Magic's DeepSparse using its built-in deepsparse.benchmark utility and various models from their SparseZoo (https://sparsezoo.neuralmagic.com/). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-StreamEPYC 760160120180240300SE +/- 1.67, N = 3260.16

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-StreamEPYC 76011428425670SE +/- 0.40, N = 361.41
MinAvgMaxEPYC 7601260556711OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 33525 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-StreamEPYC 760120406080100SE +/- 0.41, N = 393.03

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-StreamEPYC 76014080120160200SE +/- 0.69, N = 3171.85
MinAvgMaxEPYC 7601260595710OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 28745 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-StreamEPYC 7601306090120150SE +/- 1.14, N = 3143.83

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-StreamEPYC 760120406080100SE +/- 0.90, N = 3111.05
MinAvgMaxEPYC 7601268634713OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 28089 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-StreamEPYC 76013691215SE +/- 0.10, N = 310.91

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-StreamEPYC 760130060090012001500SE +/- 12.42, N = 31448.31
MinAvgMaxEPYC 7601261564712OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 32548 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-StreamEPYC 76011632486480SE +/- 0.58, N = 371.06

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-StreamEPYC 760150100150200250SE +/- 1.65, N = 3224.52
MinAvgMaxEPYC 7601258630711OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 27524 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-StreamEPYC 76011632486480SE +/- 0.17, N = 371.78

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-StreamEPYC 760150100150200250SE +/- 0.44, N = 3222.24
MinAvgMaxEPYC 7601260631709OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 27769 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-StreamEPYC 76013691215SE +/- 0.02, N = 310.97

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-StreamEPYC 760130060090012001500SE +/- 5.15, N = 31444.59
MinAvgMaxEPYC 7601262563712OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 32489 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-StreamEPYC 76013691215SE +/- 0.0505, N = 39.2958

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-StreamEPYC 7601400800120016002000SE +/- 7.60, N = 31695.40
MinAvgMaxEPYC 7601258582700OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 28311 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-StreamEPYC 7601306090120150SE +/- 0.39, N = 3145.52

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-StreamEPYC 760120406080100SE +/- 0.26, N = 3109.74

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-StreamEPYC 7601306090120150SE +/- 0.33, N = 3155.25

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-StreamEPYC 760120406080100SE +/- 0.23, N = 3102.96
MinAvgMaxEPYC 7601263635709OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 28170 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-StreamEPYC 76013691215SE +/- 0.06, N = 313.29

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-StreamEPYC 760130060090012001500SE +/- 3.77, N = 31200.04
MinAvgMaxEPYC 7601260440713OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 48382 Joules per run.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-StreamEPYC 7601306090120150SE +/- 0.35, N = 3134.52

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-StreamEPYC 7601306090120150SE +/- 0.34, N = 3118.71
MinAvgMaxEPYC 7601261564712OpenBenchmarking.orgWatts, Fewer Is BetterNeural Magic DeepSparse 1.6CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 33467 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 2.12Device: CPU - Batch Size: 16 - Model: ResNet-50EPYC 76013691215SE +/- 0.08, N = 39.90
OpenBenchmarking.orgimages/sec Per Watt, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: ResNet-50EPYC 76010.00430.00860.01290.01720.02150.019
MinAvgMaxEPYC 7601259.2526.4584.0OpenBenchmarking.orgWatts, Fewer Is BetterTensorFlow 2.12CPU Power Consumption Monitor1603204806408001. EPYC 7601: Approximate power consumption of 100023 Joules per run.

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50EPYC 7601612182430SE +/- 0.24, N = 326.36MIN: 12.3 / MAX: 27.98
OpenBenchmarking.orgbatches/sec Per Watt, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50EPYC 76010.00970.01940.02910.03880.04850.043
MinAvgMaxEPYC 7601259617697OpenBenchmarking.orgWatts, Fewer Is BetterPyTorch 2.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 31670 Joules per run.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50EPYC 7601510152025SE +/- 0.26, N = 321.40MIN: 12 / MAX: 22.24
OpenBenchmarking.orgbatches/sec Per Watt, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50EPYC 76010.00740.01480.02220.02960.0370.033
MinAvgMaxEPYC 7601260652698OpenBenchmarking.orgWatts, Fewer Is BetterPyTorch 2.1CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 66911 Joules per run.

Xmrig

Xmrig is an open-source cross-platform CPU/GPU miner for RandomX, KawPow, CryptoNight and AstroBWT. This test profile is setup to measure the Xmrig CPU mining performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: Monero - Hash Count: 1MEPYC 760115003000450060007500SE +/- 77.42, N = 126890.31. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.21Variant: Monero - Hash Count: 1MEPYC 7601369121510.71
OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.21CPU Power Consumption MonitorEPYC 7601120240360480600Min: 230.88 / Avg: 643.11 / Max: 685.61. EPYC 7601: Approximate power consumption of 98557 Joules per run.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: Wownero - Hash Count: 1MEPYC 76012K4K6K8K10KSE +/- 18.58, N = 310393.01. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.21Variant: Wownero - Hash Count: 1MEPYC 76014812162015.76
MinAvgMaxEPYC 7601266660708OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.21CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 69047 Joules per run.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: CryptoNight-Femto UPX2 - Hash Count: 1MEPYC 760115003000450060007500SE +/- 66.27, N = 37092.51. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.21Variant: CryptoNight-Femto UPX2 - Hash Count: 1MEPYC 7601369121511.01
MinAvgMaxEPYC 7601264644678OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.21CPU Power Consumption Monitor20040060080010001. EPYC 7601: Approximate power consumption of 96227 Joules per run.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: CryptoNight-Heavy - Hash Count: 1MEPYC 760115003000450060007500SE +/- 57.05, N = 126932.61. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.21Variant: CryptoNight-Heavy - Hash Count: 1MEPYC 7601369121510.75
OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.21CPU Power Consumption MonitorEPYC 7601120240360480600Min: 264 / Avg: 644.79 / Max: 676.591. EPYC 7601: Approximate power consumption of 98170 Joules per run.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: KawPow - Hash Count: 1MEPYC 760115003000450060007500SE +/- 72.58, N = 126984.41. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.21Variant: KawPow - Hash Count: 1MEPYC 7601369121510.85
OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.21CPU Power Consumption MonitorEPYC 7601120240360480600Min: 266.42 / Avg: 643.61 / Max: 674.471. EPYC 7601: Approximate power consumption of 97400 Joules per run.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: GhostRider - Hash Count: 1MEPYC 76012004006008001000SE +/- 0.39, N = 31104.31. (CXX) g++ options: -fexceptions -fno-rtti -maes -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 76010.4460.8921.3381.7842.231.982
OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.21CPU Power Consumption MonitorEPYC 7601100200300400500Min: 265.12 / Avg: 557.27 / Max: 583.071. EPYC 7601: Approximate power consumption of 505258 Joules per run.

Meta Performance Per Watts

OpenBenchmarking.orgPerformance Per Watts, More Is BetterMeta Performance Per WattsPerformance Per WattsEPYC 760160012001800240030002564.49

CPU Power Consumption Monitor

OpenBenchmarking.orgWattsCPU Power Consumption MonitorPhoronix Test Suite System MonitoringEPYC 7601130260390520650Min: 121.04 / Avg: 559.03 / Max: 719.64

182 Results Shown

nginx:
  500
  1000
OpenSSL:
  RSA4096:
    sign/s
    verify/s
  SHA256:
    byte/s
  SHA512:
    byte/s
  AES-128-GCM:
    byte/s
  AES-256-GCM:
    byte/s
  ChaCha20:
    byte/s
  ChaCha20-Poly1305:
    byte/s
Apache HTTP Server
Apache IoTDB:
  500 - 100 - 200 - 100:
    point/sec
    Average Latency
  500 - 100 - 200 - 400:
    point/sec
    Average Latency
  500 - 100 - 500 - 100:
    point/sec
    Average Latency
  500 - 100 - 500 - 400:
    point/sec
    Average Latency
  500 - 100 - 800 - 100:
    point/sec
    Average Latency
  500 - 100 - 800 - 400:
    point/sec
    Average Latency
  800 - 100 - 200 - 100:
    point/sec
    Average Latency
  800 - 100 - 200 - 400:
    point/sec
    Average Latency
  800 - 100 - 500 - 100:
    point/sec
    Average Latency
  800 - 100 - 500 - 400:
    point/sec
    Average Latency
  800 - 100 - 800 - 100:
    point/sec
    Average Latency
  800 - 100 - 800 - 400:
    point/sec
    Average Latency
DuckDB:
  IMDB
  TPC-H Parquet
Apache Cassandra
Speedb:
  Rand Read
  Read While Writing
  Read Rand Write Rand
  Update Rand
RocksDB:
  Rand Read
  Read While Writing
  Read Rand Write Rand
  Update Rand
Redis 7.0.12 + memtier_benchmark:
  Redis - 100 - 1:10
  Redis - 100 - 1:5
7-Zip Compression:
  Compression Rating
  Decompression Rating
Timed Linux Kernel Compilation:
  defconfig
  allmodconfig
Timed LLVM Compilation:
  Ninja
  Unix Makefiles
Timed Node.js Compilation
Timed Gem5 Compilation
Timed FFmpeg Compilation
QuantLib
SPECFEM3D:
  Layered Halfspace
  Water-layered Halfspace
  Homogeneous Halfspace
  Mount St. Helens
  Tomographic Model
OpenFOAM:
  drivaerFastback, Small Mesh Size - Mesh Time
  drivaerFastback, Small Mesh Size - Execution Time
  drivaerFastback, Medium Mesh Size - Mesh Time
  drivaerFastback, Medium Mesh Size - Execution Time
OpenRadioss
miniBUDE:
  OpenMP - BM1:
    GFInst/s
    Billion Interactions/s
  OpenMP - BM2:
    GFInst/s
    Billion Interactions/s
easyWave:
  e2Asean Grid + BengkuluSept2007 Source - 1200
  e2Asean Grid + BengkuluSept2007 Source - 2400
GPAW
LAMMPS Molecular Dynamics Simulator
CloverLeaf:
  clover_bm64_short
  clover_bm16
Kripke
Xcompact3d Incompact3d:
  input.i3d 193 Cells Per Direction
  X3D-benchmarking input.i3d
NAMD
GROMACS
ACES DGEMM
Timed MrBayes Analysis
Blender:
  BMW27 - CPU-Only
  Classroom - CPU-Only
  Fishy Cat - CPU-Only
  Pabellon Barcelona - CPU-Only
  Barbershop - CPU-Only
Embree:
  Pathtracer ISPC - Asian Dragon
  Pathtracer ISPC - Crown
Intel Open Image Denoise
OSPRay Studio:
  1 - 4K - 1 - Path Tracer - CPU
  1 - 4K - 16 - Path Tracer - CPU
  1 - 4K - 32 - Path Tracer - CPU
  3 - 4K - 1 - Path Tracer - CPU
  3 - 4K - 16 - Path Tracer - CPU
  3 - 4K - 32 - Path Tracer - CPU
OSPRay:
  gravity_spheres_volume/dim_512/ao/real_time
  gravity_spheres_volume/dim_512/scivis/real_time
  gravity_spheres_volume/dim_512/pathtracer/real_time
  particle_volume/ao/real_time
  particle_volume/scivis/real_time
  particle_volume/pathtracer/real_time
Chaos Group V-RAY
IndigoBench:
  CPU - Supercar
  CPU - Bedroom
FFmpeg:
  libx265 - Live
  libx265 - Upload
  libx265 - Platform
  libx265 - Video On Demand
SVT-AV1:
  Preset 13 - Bosphorus 4K
  Preset 12 - Bosphorus 4K
  Preset 8 - Bosphorus 4K
  Preset 4 - Bosphorus 4K
rav1e:
  10
  6
  5
  1
x265
uvg266:
  Bosphorus 4K - Medium
  Bosphorus 4K - Very Fast
  Bosphorus 4K - Super Fast
  Bosphorus 4K - Ultra Fast
VVenC:
  Bosphorus 4K - Fast
  Bosphorus 4K - Faster
OpenVINO:
  Face Detection FP16-INT8 - CPU:
    FPS
    ms
  Age Gender Recognition Retail 0013 FP16-INT8 - CPU:
    FPS
    ms
  Person Detection FP16 - CPU:
    FPS
    ms
  Weld Porosity Detection FP16-INT8 - CPU:
    FPS
    ms
  Vehicle Detection FP16-INT8 - CPU:
    FPS
    ms
  Person Vehicle Bike Detection FP16 - CPU:
    FPS
    ms
  Machine Translation EN To DE FP16 - CPU:
    FPS
    ms
  Face Detection Retail FP16-INT8 - CPU:
    FPS
    ms
  Handwritten English Recognition FP16-INT8 - CPU:
    FPS
    ms
  Road Segmentation ADAS FP16-INT8 - CPU:
    FPS
    ms
Neural Magic DeepSparse:
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  ResNet-50, Baseline - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  ResNet-50, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
TensorFlow
PyTorch:
  CPU - 1 - ResNet-50
  CPU - 256 - ResNet-50
Xmrig:
  Monero - 1M
  Wownero - 1M
  CryptoNight-Femto UPX2 - 1M
  CryptoNight-Heavy - 1M
  KawPow - 1M
  GhostRider - 1M
Meta Performance Per Watts:
  Performance Per Watts
  Phoronix Test Suite System Monitoring

EPYC 7601

Processor: AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads), Motherboard: TYAN B8026T70AE24HR (V1.02.B10 BIOS), Chipset: AMD 17h, Memory: 128GB, Disk: 1000GB INTEL SSDPE2KX010T8 + 280GB INTEL SSDPE21D280GA, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Broadcom NetXtreme BCM5720 PCIe

OS: Ubuntu 23.10, Kernel: 6.6.9-060609-generic (x86_64), Desktop: GNOME Shell 45.0, Display Server: X Server 1.21.1.7, OpenGL: 4.5 Mesa 23.2.1-1ubuntu3.1 (LLVM 15.0.7 256 bits), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/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-build-config=bootstrap-lto-lean --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: 0x800126e
Java Notes: OpenJDK Runtime Environment (build 11.0.21+9-post-Ubuntu-0ubuntu123.10)
Python Notes: Python 3.11.6
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 + retbleed: Mitigation of untrained return thunk; SMT vulnerable + spec_rstack_overflow: Mitigation of Safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: disabled RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 5 January 2024 18:51 by user phoronix.