Benchmarks for a future article. Intel Core i9-11900K testing with a ASUS ROG MAXIMUS XIII HERO (1402 BIOS) and ASUS Intel RKL GT1 31GB on Ubuntu 22.10 via the Phoronix Test Suite.
i9-11900K: AVX-512 On Kernel Notes: Transparent Huge Pages: madviseEnvironment Notes: CXXFLAGS="-O3 -march=native" CFLAGS="-O3 -march=native"Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0x54 - Thermald 2.5.1Python Notes: Python 3.10.7Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
i9-11900K: AVX-512 Off Kernel Notes: Transparent Huge Pages: madviseEnvironment Notes: CXXFLAGS="-O3 -march=native -mno-avx512f" CFLAGS="-O3 -march=native -mno-avx512f"Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0x54 - Thermald 2.5.1Python Notes: Python 3.10.7Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
i9-11900K: AVX-512 On 512 Processor: Intel Core i9-11900K @ 5.10GHz (8 Cores / 16 Threads), Motherboard: ASUS ROG MAXIMUS XIII HERO (1402 BIOS), Chipset: Intel Tiger Lake-H, Memory: 32GB, Disk: 2000GB Corsair Force MP600 + 32GB Flash Drive, Graphics: ASUS Intel RKL GT1 31GB (1300MHz), Audio: Intel Tiger Lake-H HD Audio, Monitor: ASUS MG28U, Network: 2 x Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411
OS: Ubuntu 22.10, Kernel: 5.19.0-21-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.2.1, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 3840x2160
Kernel Notes: Transparent Huge Pages: madviseEnvironment Notes: CXXFLAGS="-O3 -march=native -mprefer-vector-width=512" CFLAGS="-O3 -march=native -mprefer-vector-width=512"Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0x54 - Thermald 2.5.1Python Notes: Python 3.10.7Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
Neural Magic DeepSparse OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 8 16 24 32 40 SE +/- 0.11, N = 3 SE +/- 0.02, N = 3 SE +/- 0.07, N = 3 35.42 28.97 35.38
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 9 18 27 36 45 SE +/- 0.22, N = 3 SE +/- 0.02, N = 3 SE +/- 0.12, N = 3 40.80 35.52 40.87
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 15 30 45 60 75 SE +/- 0.03, N = 3 SE +/- 0.03, N = 3 SE +/- 0.05, N = 3 68.77 56.59 68.76
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 20 40 60 80 100 SE +/- 0.06, N = 3 SE +/- 0.03, N = 3 SE +/- 0.12, N = 3 81.02 69.10 80.67
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 30 60 90 120 150 SE +/- 0.12, N = 3 SE +/- 0.04, N = 3 SE +/- 0.48, N = 3 133.74 85.98 132.91
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 30 60 90 120 150 SE +/- 0.68, N = 3 SE +/- 0.14, N = 3 SE +/- 0.92, N = 3 156.81 101.64 155.39
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 3 6 9 12 15 SE +/- 0.0301, N = 3 SE +/- 0.0071, N = 3 SE +/- 0.0785, N = 3 8.9905 7.8089 8.8759
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 3 6 9 12 15 SE +/- 0.0140, N = 3 SE +/- 0.0060, N = 3 SE +/- 0.0648, N = 3 9.0886 8.1213 8.9516
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 10 20 30 40 50 SE +/- 0.19, N = 3 SE +/- 0.05, N = 3 SE +/- 0.20, N = 3 43.44 25.54 43.68
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 8 16 24 32 40 SE +/- 0.02, N = 3 SE +/- 0.15, N = 3 SE +/- 0.03, N = 3 36.86 26.53 36.60
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 11 22 33 44 55 SE +/- 0.04, N = 3 SE +/- 0.01, N = 3 SE +/- 0.15, N = 3 49.99 43.39 49.67
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 12 24 36 48 60 SE +/- 0.37, N = 3 SE +/- 0.08, N = 3 SE +/- 0.15, N = 3 51.36 43.23 50.77
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 3 6 9 12 15 SE +/- 0.0265, N = 3 SE +/- 0.0041, N = 3 SE +/- 0.0463, N = 3 9.0118 7.8337 8.9135
OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.1 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream AVX-512 On 512 AVX-512 Off AVX-512 On 3 6 9 12 15 SE +/- 0.0265, N = 3 SE +/- 0.0056, N = 3 SE +/- 0.0383, N = 3 9.0792 8.1425 8.9588
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/. This test is currently using a reference OpenRadioss binary build offered via GitHub. Learn more via the OpenBenchmarking.org test page.
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.org Seconds, Fewer Is Better OpenFOAM 10 Input: drivaerFastback, Small Mesh Size - Mesh Time AVX-512 On 512 AVX-512 Off AVX-512 On 11 22 33 44 55 45.33 46.53 45.49 1. (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
simdjson This is a benchmark of SIMDJSON, a high performance JSON parser. SIMDJSON aims to be the fastest JSON parser and is used by projects like Microsoft FishStore, Yandex ClickHouse, Shopify, and others. Learn more via the OpenBenchmarking.org test page.
Embree Intel Embree is a collection of high-performance ray-tracing kernels for execution on CPUs 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.
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.
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.
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
i9-11900K: AVX-512 Off: The test run did not produce a result.
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
i9-11900K: AVX-512 Off: The test run did not produce a result.
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
i9-11900K: AVX-512 Off: The test run did not produce a result.
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
i9-11900K: AVX-512 Off: The test run did not produce a result.
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
i9-11900K: AVX-512 Off: The test run did not produce a result.
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
i9-11900K: AVX-512 Off: The test run did not produce a result.
Cpuminer-Opt Cpuminer-Opt is a fork of cpuminer-multi that carries a wide range of CPU performance optimizations for measuring the potential cryptocurrency mining performance of the CPU/processor with a wide variety of cryptocurrencies. The benchmark reports the hash speed for the CPU mining performance for the selected cryptocurrency. Learn more via the OpenBenchmarking.org test page.
Result
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.18 Algorithm: Triple SHA-256, Onecoin AVX-512 On 512 AVX-512 Off AVX-512 On 50K 100K 150K 200K 250K SE +/- 1461.35, N = 3 SE +/- 627.84, N = 3 SE +/- 1895.81, N = 3 230687 106137 231700 -mno-avx512f 1. (CXX) g++ options: -O3 -march=native -lcurl -lz -lpthread -lssl -lcrypto -lgmp
kH/s Per Watt
OpenBenchmarking.org kH/s Per Watt, More Is Better Cpuminer-Opt 3.18 Algorithm: Triple SHA-256, Onecoin AVX-512 On 512 AVX-512 Off AVX-512 On 300 600 900 1200 1500 1361.66 820.32 1366.28
CPU Peak Freq (Highest CPU Core Frequency
OpenBenchmarking.org Megahertz, More Is Better Cpuminer-Opt 3.18 CPU Peak Freq (Highest CPU Core Frequency) Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 900 1800 2700 3600 4500 Min: 4700 / Avg: 4753.44 / Max: 5300 Min: 4700 / Avg: 4834.78 / Max: 5300 Min: 4700 / Avg: 4759.14 / Max: 5300
CPU Power Consumption
OpenBenchmarking.org Watts, Fewer Is Better Cpuminer-Opt 3.18 CPU Power Consumption Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 50 100 150 200 250 Min: 12.41 / Avg: 169.42 / Max: 278.89 Min: 12.4 / Avg: 129.38 / Max: 169.44 Min: 12.5 / Avg: 169.58 / Max: 237.02
CPU Temp
OpenBenchmarking.org Celsius, Fewer Is Better Cpuminer-Opt 3.18 CPU Temperature Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 20 40 60 80 100 Min: 42 / Avg: 75.38 / Max: 94 Min: 39 / Avg: 64.76 / Max: 73 Min: 40 / Avg: 74.89 / Max: 92
Result
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.18 Algorithm: Quad SHA-256, Pyrite AVX-512 On 512 AVX-512 Off AVX-512 On 40K 80K 120K 160K 200K SE +/- 346.55, N = 3 SE +/- 42.56, N = 3 SE +/- 280.42, N = 3 164790 64663 167500 -mno-avx512f 1. (CXX) g++ options: -O3 -march=native -lcurl -lz -lpthread -lssl -lcrypto -lgmp
kH/s Per Watt
OpenBenchmarking.org kH/s Per Watt, More Is Better Cpuminer-Opt 3.18 Algorithm: Quad SHA-256, Pyrite AVX-512 On 512 AVX-512 Off AVX-512 On 200 400 600 800 1000 930.90 460.38 953.89
CPU Peak Freq (Highest CPU Core Frequency
OpenBenchmarking.org Megahertz, More Is Better Cpuminer-Opt 3.18 CPU Peak Freq (Highest CPU Core Frequency) Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 900 1800 2700 3600 4500 Min: 4700 / Avg: 4753.87 / Max: 5290 Min: 4700 / Avg: 4831.54 / Max: 5312 Min: 4700 / Avg: 4750.65 / Max: 5300
CPU Power Consumption
OpenBenchmarking.org Watts, Fewer Is Better Cpuminer-Opt 3.18 CPU Power Consumption Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 50 100 150 200 250 Min: 12.25 / Avg: 177.02 / Max: 245.52 Min: 12.24 / Avg: 140.46 / Max: 186.51 Min: 12.43 / Avg: 175.6 / Max: 252.11
CPU Temp
OpenBenchmarking.org Celsius, Fewer Is Better Cpuminer-Opt 3.18 CPU Temperature Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 20 40 60 80 100 Min: 39 / Avg: 75.84 / Max: 93 Min: 36 / Avg: 65.33 / Max: 73 Min: 38 / Avg: 73.74 / Max: 92
Result
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.18 Algorithm: LBC, LBRY Credits AVX-512 On 512 AVX-512 Off AVX-512 On 17K 34K 51K 68K 85K SE +/- 116.81, N = 3 SE +/- 20.00, N = 3 SE +/- 84.13, N = 3 76393 27270 77083 -mno-avx512f 1. (CXX) g++ options: -O3 -march=native -lcurl -lz -lpthread -lssl -lcrypto -lgmp
kH/s Per Watt
OpenBenchmarking.org kH/s Per Watt, More Is Better Cpuminer-Opt 3.18 Algorithm: LBC, LBRY Credits AVX-512 On 512 AVX-512 Off AVX-512 On 90 180 270 360 450 393.49 165.49 397.33
CPU Peak Freq (Highest CPU Core Frequency
OpenBenchmarking.org Megahertz, More Is Better Cpuminer-Opt 3.18 CPU Peak Freq (Highest CPU Core Frequency) Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 900 1800 2700 3600 4500 Min: 4700 / Avg: 4753.32 / Max: 5289 Min: 4700 / Avg: 4769.7 / Max: 5300 Min: 4700 / Avg: 4754.62 / Max: 5300
CPU Power Consumption
OpenBenchmarking.org Watts, Fewer Is Better Cpuminer-Opt 3.18 CPU Power Consumption Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 50 100 150 200 250 Min: 12.24 / Avg: 194.14 / Max: 277.39 Min: 12.49 / Avg: 164.79 / Max: 203.1 Min: 12.52 / Avg: 194 / Max: 268.78
CPU Temp
OpenBenchmarking.org Celsius, Fewer Is Better Cpuminer-Opt 3.18 CPU Temperature Monitor AVX-512 On 512 AVX-512 Off AVX-512 On 20 40 60 80 100 Min: 40 / Avg: 77.84 / Max: 95 Min: 37 / Avg: 68.15 / Max: 76 Min: 39 / Avg: 77.28 / Max: 95
Mobile Neural Network MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. This MNN test profile is building the OpenMP / CPU threaded version for processor benchmarking and not any GPU-accelerated test. MNN does allow making use of AVX-512 extensions. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.1 Model: nasnet AVX-512 On 512 AVX-512 Off AVX-512 On 2 4 6 8 10 SE +/- 0.087, N = 15 SE +/- 0.056, N = 3 SE +/- 0.077, N = 15 7.527 7.139 7.468 MIN: 6.72 / MAX: 17.92 -mno-avx512f - MIN: 6.92 / MAX: 7.99 MIN: 6.73 / MAX: 13.9 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl
OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.1 Model: mobilenetV3 AVX-512 On 512 AVX-512 Off AVX-512 On 0.2167 0.4334 0.6501 0.8668 1.0835 SE +/- 0.012, N = 15 SE +/- 0.003, N = 3 SE +/- 0.005, N = 15 0.963 0.942 0.938 MIN: 0.89 / MAX: 10.54 -mno-avx512f - MIN: 0.92 / MAX: 1.64 MIN: 0.89 / MAX: 3.62 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl
OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.1 Model: squeezenetv1.1 AVX-512 On 512 AVX-512 Off AVX-512 On 0.4552 0.9104 1.3656 1.8208 2.276 SE +/- 0.014, N = 15 SE +/- 0.014, N = 3 SE +/- 0.013, N = 15 1.675 2.023 1.670 MIN: 1.55 / MAX: 7.76 -mno-avx512f - MIN: 1.97 / MAX: 2.72 MIN: 1.54 / MAX: 11.33 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl
OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.1 Model: resnet-v2-50 AVX-512 On 512 AVX-512 Off AVX-512 On 4 8 12 16 20 SE +/- 0.04, N = 15 SE +/- 0.08, N = 3 SE +/- 0.05, N = 15 10.37 18.04 10.30 MIN: 10.05 / MAX: 22.64 -mno-avx512f - MIN: 17.67 / MAX: 24.46 MIN: 9.74 / MAX: 33.88 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl
OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.1 Model: SqueezeNetV1.0 AVX-512 On 512 AVX-512 Off AVX-512 On 0.8312 1.6624 2.4936 3.3248 4.156 SE +/- 0.022, N = 15 SE +/- 0.032, N = 3 SE +/- 0.020, N = 15 3.114 3.694 3.073 MIN: 2.9 / MAX: 9.42 -mno-avx512f - MIN: 3.57 / MAX: 4.37 MIN: 2.89 / MAX: 8.65 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl
OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.1 Model: MobileNetV2_224 AVX-512 On 512 AVX-512 Off AVX-512 On 0.4795 0.959 1.4385 1.918 2.3975 SE +/- 0.014, N = 15 SE +/- 0.019, N = 3 SE +/- 0.014, N = 15 2.125 1.934 2.131 MIN: 2.03 / MAX: 8.62 -mno-avx512f - MIN: 1.84 / MAX: 11.59 MIN: 2.03 / MAX: 8.46 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl
OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.1 Model: mobilenet-v1-1.0 AVX-512 On 512 AVX-512 Off AVX-512 On 0.4475 0.895 1.3425 1.79 2.2375 SE +/- 0.007, N = 15 SE +/- 0.007, N = 3 SE +/- 0.005, N = 15 1.989 1.798 1.972 MIN: 1.9 / MAX: 8.1 -mno-avx512f - MIN: 1.74 / MAX: 2.69 MIN: 1.8 / MAX: 8.03 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl
NCNN NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: mobilenet AVX-512 On 512 AVX-512 Off AVX-512 On 3 6 9 12 15 SE +/- 0.13, N = 3 SE +/- 0.02, N = 3 SE +/- 0.15, N = 3 12.28 11.43 12.28 MIN: 11.87 / MAX: 18.86 -mno-avx512f - MIN: 11.21 / MAX: 16.87 MIN: 11.79 / MAX: 13.67 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU-v2-v2 - Model: mobilenet-v2 AVX-512 On 512 AVX-512 Off AVX-512 On 0.8483 1.6966 2.5449 3.3932 4.2415 SE +/- 0.05, N = 3 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 3.77 3.34 3.73 MIN: 3.53 / MAX: 4.4 -mno-avx512f - MIN: 3.16 / MAX: 5.05 MIN: 3.56 / MAX: 5.05 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU-v3-v3 - Model: mobilenet-v3 AVX-512 On 512 AVX-512 Off AVX-512 On 0.6098 1.2196 1.8294 2.4392 3.049 SE +/- 0.05, N = 3 SE +/- 0.01, N = 3 SE +/- 0.03, N = 3 2.71 2.49 2.68 MIN: 2.55 / MAX: 3.5 -mno-avx512f - MIN: 2.4 / MAX: 3.36 MIN: 2.56 / MAX: 3.63 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: shufflenet-v2 AVX-512 On 512 AVX-512 Off AVX-512 On 0.5243 1.0486 1.5729 2.0972 2.6215 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 2.33 2.33 2.33 MIN: 2.27 / MAX: 2.81 -mno-avx512f - MIN: 2.27 / MAX: 3.18 MIN: 2.26 / MAX: 3.21 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: mnasnet AVX-512 On 512 AVX-512 Off AVX-512 On 0.5895 1.179 1.7685 2.358 2.9475 SE +/- 0.03, N = 3 SE +/- 0.03, N = 3 SE +/- 0.03, N = 3 2.58 2.37 2.62 MIN: 2.48 / MAX: 3.14 -mno-avx512f - MIN: 2.28 / MAX: 3.27 MIN: 2.48 / MAX: 3.81 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: efficientnet-b0 AVX-512 On 512 AVX-512 Off AVX-512 On 1.0935 2.187 3.2805 4.374 5.4675 SE +/- 0.05, N = 3 SE +/- 0.05, N = 3 SE +/- 0.06, N = 3 4.79 4.35 4.86 MIN: 4.61 / MAX: 9.9 -mno-avx512f - MIN: 4.2 / MAX: 5.22 MIN: 4.6 / MAX: 6.13 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: blazeface AVX-512 On 512 AVX-512 Off AVX-512 On 0.18 0.36 0.54 0.72 0.9 SE +/- 0.01, N = 3 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 0.80 0.76 0.80 MIN: 0.77 / MAX: 1.28 -mno-avx512f - MIN: 0.73 / MAX: 1.52 MIN: 0.77 / MAX: 1.52 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: googlenet AVX-512 On 512 AVX-512 Off AVX-512 On 3 6 9 12 15 SE +/- 0.13, N = 3 SE +/- 0.03, N = 3 SE +/- 0.00, N = 3 9.96 9.55 10.16 MIN: 9.52 / MAX: 10.98 -mno-avx512f - MIN: 9.33 / MAX: 10.86 MIN: 9.92 / MAX: 11.36 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: vgg16 AVX-512 On 512 AVX-512 Off AVX-512 On 10 20 30 40 50 SE +/- 0.23, N = 3 SE +/- 0.13, N = 3 SE +/- 0.13, N = 3 44.59 45.68 44.74 MIN: 43.87 / MAX: 50.43 -mno-avx512f - MIN: 44.98 / MAX: 50.56 MIN: 44.1 / MAX: 49.06 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: resnet18 AVX-512 On 512 AVX-512 Off AVX-512 On 2 4 6 8 10 SE +/- 0.02, N = 3 SE +/- 0.00, N = 3 SE +/- 0.02, N = 3 8.64 8.24 8.69 MIN: 8.45 / MAX: 9.39 -mno-avx512f - MIN: 8.06 / MAX: 9.19 MIN: 8.51 / MAX: 14.15 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: alexnet AVX-512 On 512 AVX-512 Off AVX-512 On 2 4 6 8 10 SE +/- 0.19, N = 3 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 6.69 6.33 6.98 MIN: 6.2 / MAX: 7.35 -mno-avx512f - MIN: 6.2 / MAX: 7.75 MIN: 6.82 / MAX: 7.88 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: resnet50 AVX-512 On 512 AVX-512 Off AVX-512 On 4 8 12 16 20 SE +/- 0.42, N = 3 SE +/- 0.02, N = 3 SE +/- 0.42, N = 3 16.24 15.40 15.99 MIN: 15.19 / MAX: 17.46 -mno-avx512f - MIN: 15.07 / MAX: 24.51 MIN: 15.25 / MAX: 18.48 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: yolov4-tiny AVX-512 On 512 AVX-512 Off AVX-512 On 5 10 15 20 25 SE +/- 0.44, N = 3 SE +/- 0.06, N = 3 SE +/- 0.41, N = 3 22.01 18.80 20.99 MIN: 21.29 / MAX: 26.71 -mno-avx512f - MIN: 18.51 / MAX: 19.88 MIN: 20.36 / MAX: 37.15 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: squeezenet_ssd AVX-512 On 512 AVX-512 Off AVX-512 On 4 8 12 16 20 SE +/- 0.05, N = 3 SE +/- 0.04, N = 3 SE +/- 0.18, N = 3 17.34 13.89 17.19 MIN: 17.03 / MAX: 18.98 -mno-avx512f - MIN: 13.58 / MAX: 15.32 MIN: 16.54 / MAX: 25.09 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: regnety_400m AVX-512 On 512 AVX-512 Off AVX-512 On 2 4 6 8 10 SE +/- 0.03, N = 3 SE +/- 0.02, N = 3 SE +/- 0.02, N = 3 7.10 6.70 7.05 MIN: 6.91 / MAX: 11.12 -mno-avx512f - MIN: 6.54 / MAX: 7.93 MIN: 6.87 / MAX: 8.31 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20220729 Target: CPU - Model: vision_transformer AVX-512 On 512 AVX-512 Off AVX-512 On 20 40 60 80 100 SE +/- 0.12, N = 3 SE +/- 0.39, N = 3 SE +/- 0.11, N = 3 70.39 110.38 87.29 MIN: 69.62 / MAX: 71.72 -mno-avx512f - MIN: 109.43 / MAX: 116.1 MIN: 86.63 / MAX: 92.92 1. (CXX) g++ options: -O3 -march=native -rdynamic -lgomp -lpthread
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 too. Learn more via the OpenBenchmarking.org test page.
Device: CPU - Batch Size: 512 - Model: VGG-16
i9-11900K: AVX-512 On: The test quit with a non-zero exit status.
i9-11900K: AVX-512 Off: The test quit with a non-zero exit status. E: Fatal Python error: Aborted
i9-11900K: AVX-512 On 512: The test quit with a non-zero exit status. E: Fatal Python error: Segmentation fault
Device: CPU - Batch Size: 512 - Model: ResNet-50
i9-11900K: AVX-512 On: The test quit with a non-zero exit status.
i9-11900K: AVX-512 Off: The test quit with a non-zero exit status.
i9-11900K: AVX-512 On 512: The test quit with a non-zero exit status.
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.org FPS, More Is Better OpenVINO 2022.2.dev Model: Face Detection FP16 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 0.6683 1.3366 2.0049 2.6732 3.3415 SE +/- 0.00, N = 3 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 2.95 2.60 2.97 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Face Detection FP16-INT8 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 3 6 9 12 15 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 SE +/- 0.01, N = 3 12.17 4.88 12.23 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 2K 4K 6K 8K 10K SE +/- 54.72, N = 3 SE +/- 34.03, N = 3 SE +/- 94.72, N = 3 9460.45 7956.78 9379.27 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 5K 10K 15K 20K 25K SE +/- 387.31, N = 15 SE +/- 47.04, N = 3 SE +/- 411.52, N = 15 22962.10 14973.88 23074.82 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Person Detection FP16 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 0.4005 0.801 1.2015 1.602 2.0025 SE +/- 0.00, N = 3 SE +/- 0.01, N = 3 SE +/- 0.00, N = 3 1.78 1.54 1.78 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Person Detection FP32 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 0.3983 0.7966 1.1949 1.5932 1.9915 SE +/- 0.00, N = 3 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 1.77 1.54 1.76 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Weld Porosity Detection FP16-INT8 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 300 600 900 1200 1500 SE +/- 4.58, N = 3 SE +/- 2.18, N = 3 SE +/- 5.38, N = 3 1242.45 533.79 1240.56 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Weld Porosity Detection FP16 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 70 140 210 280 350 SE +/- 1.00, N = 3 SE +/- 0.05, N = 3 SE +/- 0.40, N = 3 321.51 252.69 320.80 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Vehicle Detection FP16-INT8 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 140 280 420 560 700 SE +/- 1.77, N = 3 SE +/- 1.58, N = 3 SE +/- 3.77, N = 3 630.58 310.38 623.17 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Vehicle Detection FP16 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 30 60 90 120 150 SE +/- 0.25, N = 3 SE +/- 1.44, N = 3 SE +/- 0.12, N = 3 109.09 141.30 108.61 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Person Vehicle Bike Detection FP16 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 70 140 210 280 350 SE +/- 2.59, N = 7 SE +/- 3.32, N = 6 SE +/- 2.42, N = 3 285.75 325.95 286.43 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.2.dev Model: Machine Translation EN To DE FP16 - Device: CPU AVX-512 On 512 AVX-512 Off AVX-512 On 8 16 24 32 40 SE +/- 0.18, N = 3 SE +/- 0.04, N = 3 SE +/- 0.27, N = 3 35.55 31.70 35.62 -ldl -mno-avx512f -ldl 1. (CXX) g++ options: -O3 -march=native -fsigned-char -ffunction-sections -fdata-sections -fno-strict-overflow -fwrapv -pie
ONNX Runtime ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Zoo. Learn more via the OpenBenchmarking.org test page.
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.
Meta Performance Per Watts OpenBenchmarking.org Performance Per Watts, More Is Better Meta Performance Per Watts Performance Per Watts AVX-512 On 512 AVX-512 Off AVX-512 On 40 80 120 160 200 170.21 133.30 170.75
CPU Peak Freq (Highest CPU Core Frequency) Monitor OpenBenchmarking.org Megahertz CPU Peak Freq (Highest CPU Core Frequency) Monitor Phoronix Test Suite System Monitoring AVX-512 On 512 AVX-512 Off AVX-512 On 1000 2000 3000 4000 5000 Min: 2700 / Avg: 4722.22 / Max: 5323 Min: 3500 / Avg: 4781.84 / Max: 5621 Min: 3350 / Avg: 4733.96 / Max: 5541
CPU Power Consumption Monitor OpenBenchmarking.org Watts CPU Power Consumption Monitor Phoronix Test Suite System Monitoring AVX-512 On 512 AVX-512 Off AVX-512 On 50 100 150 200 250 Min: 6.3 / Avg: 192.29 / Max: 280.23 Min: 6.36 / Avg: 173.58 / Max: 251.93 Min: 6.37 / Avg: 188.45 / Max: 283.94
CPU Temperature Monitor OpenBenchmarking.org Celsius CPU Temperature Monitor Phoronix Test Suite System Monitoring AVX-512 On 512 AVX-512 Off AVX-512 On 20 40 60 80 100 Min: 31 / Avg: 79.47 / Max: 100 Min: 27 / Avg: 72.98 / Max: 95 Min: 29 / Avg: 78.17 / Max: 100
i9-11900K: AVX-512 On Kernel Notes: Transparent Huge Pages: madviseEnvironment Notes: CXXFLAGS="-O3 -march=native" CFLAGS="-O3 -march=native"Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0x54 - Thermald 2.5.1Python Notes: Python 3.10.7Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 18 October 2022 10:35 by user phoronix.
i9-11900K: AVX-512 Off Kernel Notes: Transparent Huge Pages: madviseEnvironment Notes: CXXFLAGS="-O3 -march=native -mno-avx512f" CFLAGS="-O3 -march=native -mno-avx512f"Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0x54 - Thermald 2.5.1Python Notes: Python 3.10.7Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 19 October 2022 09:56 by user phoronix.
i9-11900K: AVX-512 On 512 Processor: Intel Core i9-11900K @ 5.10GHz (8 Cores / 16 Threads), Motherboard: ASUS ROG MAXIMUS XIII HERO (1402 BIOS), Chipset: Intel Tiger Lake-H, Memory: 32GB, Disk: 2000GB Corsair Force MP600 + 32GB Flash Drive, Graphics: ASUS Intel RKL GT1 31GB (1300MHz), Audio: Intel Tiger Lake-H HD Audio, Monitor: ASUS MG28U, Network: 2 x Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411
OS: Ubuntu 22.10, Kernel: 5.19.0-21-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.2.1, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 3840x2160
Kernel Notes: Transparent Huge Pages: madviseEnvironment Notes: CXXFLAGS="-O3 -march=native -mprefer-vector-width=512" CFLAGS="-O3 -march=native -mprefer-vector-width=512"Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0x54 - Thermald 2.5.1Python Notes: Python 3.10.7Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 20 October 2022 10:52 by user phoronix.