zen 1 epyc

2 x AMD EPYC 7601 32-Core testing with a Dell 02MJ3T (1.2.5 BIOS) and Matrox G200eW3 on Ubuntu 22.04 via the Phoronix Test Suite.

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AV1 3 Tests
C++ Boost Tests 2 Tests
Timed Code Compilation 5 Tests
C/C++ Compiler Tests 9 Tests
CPU Massive 13 Tests
Creator Workloads 17 Tests
Cryptocurrency Benchmarks, CPU Mining Tests 2 Tests
Cryptography 4 Tests
Database Test Suite 2 Tests
Encoding 5 Tests
Game Development 2 Tests
HPC - High Performance Computing 13 Tests
Imaging 6 Tests
Common Kernel Benchmarks 2 Tests
Machine Learning 8 Tests
Molecular Dynamics 2 Tests
Multi-Core 19 Tests
NVIDIA GPU Compute 2 Tests
Intel oneAPI 3 Tests
OpenMPI Tests 4 Tests
Programmer / Developer System Benchmarks 7 Tests
Python Tests 8 Tests
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Scientific Computing 2 Tests
Server 4 Tests
Server CPU Tests 6 Tests
Single-Threaded 2 Tests
Video Encoding 4 Tests
Common Workstation Benchmarks 2 Tests

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a
November 18 2022
  16 Hours, 26 Minutes
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November 19 2022
  15 Hours, 54 Minutes
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zen 1 epyc 2 x AMD EPYC 7601 32-Core testing with a Dell 02MJ3T (1.2.5 BIOS) and Matrox G200eW3 on Ubuntu 22.04 via the Phoronix Test Suite. ,,"a","b" Processor,,2 x AMD EPYC 7601 32-Core (64 Cores / 128 Threads),2 x AMD EPYC 7601 32-Core (64 Cores / 128 Threads) Motherboard,,Dell 02MJ3T (1.2.5 BIOS),Dell 02MJ3T (1.2.5 BIOS) Chipset,,AMD 17h,AMD 17h Memory,,512GB,512GB Disk,,280GB INTEL SSDPED1D280GA + 12 x 500GB Samsung SSD 860 + 120GB INTEL SSDSCKJB120G7R,280GB INTEL SSDPED1D280GA + 12 x 500GB Samsung SSD 860 + 120GB INTEL SSDSCKJB120G7R Graphics,,Matrox G200eW3,Matrox G200eW3 Monitor,,VE228,VE228 Network,,2 x Broadcom BCM57416 NetXtreme-E Dual-Media 10G RDMA + 2 x Broadcom NetXtreme BCM5720 PCIe,2 x Broadcom BCM57416 NetXtreme-E Dual-Media 10G RDMA + 2 x Broadcom NetXtreme BCM5720 PCIe OS,,Ubuntu 22.04,Ubuntu 22.04 Kernel,,5.15.0-40-generic (x86_64),5.15.0-40-generic (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server 1.21.1.3,X Server 1.21.1.3 Vulkan,,1.2.204,1.2.204 Compiler,,GCC 11.3.0,GCC 11.3.0 File-System,,ext4,ext4 Screen Resolution,,1600x1200,1600x1200 ,,"a","b" "Unpacking The Linux Kernel - linux-5.19.tar.xz (sec)",LIB,10.559,10.649 "C-Blosc - Test: blosclz shuffle (MB/s)",HIB,8493.2,8534.5 "C-Blosc - Test: blosclz bitshuffle (MB/s)",HIB,5328.2,5318.7 "miniBUDE - Implementation: OpenMP - Input Deck: BM1 (GFInst/s)",HIB,696.801,687.884 "miniBUDE - Implementation: OpenMP - Input Deck: BM1 (Billion Interactions/s)",HIB,27.872,27.515 "miniBUDE - Implementation: OpenMP - Input Deck: BM2 (GFInst/s)",HIB,753.17,742.296 "miniBUDE - Implementation: OpenMP - Input Deck: BM2 (Billion Interactions/s)",HIB,30.127,29.692 "SMHasher - Hash: wyhash (MiB/sec)",HIB,30087.23,30062.54 "SMHasher - Hash: wyhash (cycles/hash)",LIB,20.507,20.508 "SMHasher - Hash: SHA3-256 (MiB/sec)",HIB,171.09,171.13 "SMHasher - Hash: SHA3-256 (cycles/hash)",LIB,2294.234,2294.164 "SMHasher - Hash: Spooky32 (MiB/sec)",HIB,17912.16,17951.6 "SMHasher - Hash: Spooky32 (cycles/hash)",LIB,40.875,40.792 "SMHasher - Hash: fasthash32 (MiB/sec)",HIB,8316.13,8317.14 "SMHasher - Hash: fasthash32 (cycles/hash)",LIB,29.558,29.586 "SMHasher - Hash: FarmHash128 (MiB/sec)",HIB,19050.24,19028.89 "SMHasher - Hash: FarmHash128 (cycles/hash)",LIB,51.419,51.419 "SMHasher - Hash: t1ha2_atonce (MiB/sec)",HIB,18815.25,18809.49 "SMHasher - Hash: t1ha2_atonce (cycles/hash)",LIB,27.774,27.774 "SMHasher - Hash: FarmHash32 x86_64 AVX (MiB/sec)",HIB,32899.32,32884.37 "SMHasher - Hash: FarmHash32 x86_64 AVX (cycles/hash)",LIB,34.619,34.546 "SMHasher - Hash: t1ha0_aes_avx2 x86_64 (MiB/sec)",HIB,77196.31,76443.61 "SMHasher - Hash: t1ha0_aes_avx2 x86_64 (cycles/hash)",LIB,27.314,27.314 "SMHasher - Hash: MeowHash x86_64 AES-NI (MiB/sec)",HIB,41424.16,41393.48 "SMHasher - Hash: MeowHash x86_64 AES-NI (cycles/hash)",LIB,47.356,47.348 "nekRS - Input: TurboPipe Periodic (FLOP/s)",HIB,184723000000,184827000000 "OpenFOAM - Input: drivaerFastback, Small Mesh Size - Mesh Time (sec)",LIB,34.098937,34.188105 "OpenFOAM - Input: drivaerFastback, Small Mesh Size - Execution Time (sec)",LIB,61.454018,62.974805 "OpenFOAM - Input: drivaerFastback, Medium Mesh Size - Mesh Time (sec)",LIB,167.0036,167.06876 "OpenFOAM - Input: drivaerFastback, Medium Mesh Size - Execution Time (sec)",LIB,541.95263,542.44501 "OpenRadioss - Model: Bumper Beam (sec)",LIB,108.51,108.31 "OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,41.65,41.58 "OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,185.26,186.71 "OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,118,117.84 "OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,210.39,207.85 "LAMMPS Molecular Dynamics Simulator - Model: 20k Atoms (ns/day)",HIB,24.029,23.033 "LAMMPS Molecular Dynamics Simulator - Model: Rhodopsin Protein (ns/day)",HIB,24.104,24.069 "Xmrig - Variant: Monero - Hash Count: 1M (H/s)",HIB,12797.7,10914.9 "Xmrig - Variant: Wownero - Hash Count: 1M (H/s)",HIB,19243.7,19140.2 "JPEG XL libjxl - Input: PNG - Quality: 80 (MP/s)",HIB,6.37,6.38 "JPEG XL libjxl - Input: PNG - Quality: 90 (MP/s)",HIB,6.27,6.29 "JPEG XL libjxl - Input: JPEG - Quality: 80 (MP/s)",HIB,6.1,6.06 "JPEG XL libjxl - Input: JPEG - Quality: 90 (MP/s)",HIB,5.91,5.94 "JPEG XL libjxl - Input: PNG - Quality: 100 (MP/s)",HIB,0.4,0.4 "JPEG XL libjxl - Input: JPEG - Quality: 100 (MP/s)",HIB,0.4,0.4 "JPEG XL Decoding libjxl - CPU Threads: 1 (MP/s)",HIB,26.86,27.01 "JPEG XL Decoding libjxl - CPU Threads: All (MP/s)",HIB,151.09,151.66 "WebP Image Encode - Encode Settings: Default (MP/s)",HIB,12.25,12.23 "WebP Image Encode - Encode Settings: Quality 100 (MP/s)",HIB,7.73,7.71 "WebP Image Encode - Encode Settings: Quality 100, Lossless (MP/s)",HIB,1.06,1.05 "WebP Image Encode - Encode Settings: Quality 100, Highest Compression (MP/s)",HIB,2.43,2.43 "WebP Image Encode - Encode Settings: Quality 100, Lossless, Highest Compression (MP/s)",HIB,0.41,0.42 "WebP2 Image Encode - Encode Settings: Default (MP/s)",HIB,5.77,6.38 "WebP2 Image Encode - Encode Settings: Quality 75, Compression Effort 7 (MP/s)",HIB,0.38,0.37 "WebP2 Image Encode - Encode Settings: Quality 95, Compression Effort 7 (MP/s)",HIB,0.20,0.20 "WebP2 Image Encode - Encode Settings: Quality 100, Compression Effort 5 (MP/s)",HIB,7.42,7.35 "WebP2 Image Encode - Encode Settings: Quality 100, Lossless Compression (MP/s)",HIB,0.04,0.04 "srsRAN - Test: OFDM_Test (Samples / Second)",HIB,65700000,66100000 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM (eNb Mb/s)",HIB,252.8,252.3 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM (UE Mb/s)",HIB,86.2,85.9 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM (eNb Mb/s)",HIB,257.5,256.9 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM (UE Mb/s)",HIB,93.7,93.6 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM (eNb Mb/s)",HIB,274.2,273 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM (UE Mb/s)",HIB,92,91.5 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM (eNb Mb/s)",HIB,279.8,279.4 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM (UE Mb/s)",HIB,98.9,99 "srsRAN - Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM (eNb Mb/s)",HIB,69.7,69.7 "srsRAN - Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM (UE Mb/s)",HIB,38,38.1 "GraphicsMagick - Operation: Swirl (Iterations/min)",HIB,1243,1247 "GraphicsMagick - Operation: Rotate (Iterations/min)",HIB,443,448 "GraphicsMagick - Operation: Sharpen (Iterations/min)",HIB,561,555 "GraphicsMagick - Operation: Enhanced (Iterations/min)",HIB,743,740 "GraphicsMagick - Operation: Resizing (Iterations/min)",HIB,167,135 "GraphicsMagick - Operation: Noise-Gaussian (Iterations/min)",HIB,692,619 "GraphicsMagick - Operation: HWB Color Space (Iterations/min)",HIB,1107,957 "AOM AV1 - Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,0.21,0.21 "AOM AV1 - Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,5.59,5.56 "AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K (FPS)",HIB,16.41,15.89 "AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,8.95,8.71 "AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K (FPS)",HIB,18.51,17.14 "AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K (FPS)",HIB,21.01,21.39 "AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K (FPS)",HIB,21.95,21.83 "AOM AV1 - Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,0.6,0.59 "AOM AV1 - Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,11.06,10.84 "AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p (FPS)",HIB,33.12,33.44 "AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,24.96,24.36 "AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p (FPS)",HIB,41.79,38.47 "AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p (FPS)",HIB,48.34,42.57 "AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p (FPS)",HIB,45.43,42.01 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,1.314,1.321 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,27.818,28.06 "SVT-AV1 - Encoder Mode: Preset 10 - Input: Bosphorus 4K (FPS)",HIB,46.683,52.96 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,67.124,69.476 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,3.531,3.561 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,76.278,76.731 "SVT-AV1 - Encoder Mode: Preset 10 - Input: Bosphorus 1080p (FPS)",HIB,161.41,159.55 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,209.488,234.707 "libavif avifenc - Encoder Speed: 0 (sec)",LIB,136.608,136.591 "libavif avifenc - Encoder Speed: 2 (sec)",LIB,78.895,79.112 "libavif avifenc - Encoder Speed: 6 (sec)",LIB,5.268,5.268 "libavif avifenc - Encoder Speed: 6, Lossless (sec)",LIB,9.994,10.085 "libavif avifenc - Encoder Speed: 10, Lossless (sec)",LIB,7.613,7.653 "Timed Node.js Compilation - Time To Compile (sec)",LIB,217.334,215.92 "Timed PHP Compilation - Time To Compile (sec)",LIB,62.123,62.178 "Timed CPython Compilation - Build Configuration: Default (sec)",LIB,22.619,22.707 "Timed CPython Compilation - Build Configuration: Released Build, PGO + LTO Optimized (sec)",LIB,399.66,400.368 "Primesieve - Length: 1e12 (sec)",LIB,4.638,4.66 "Primesieve - Length: 1e13 (sec)",LIB,56.028,56.246 "Y-Cruncher - Pi Digits To Calculate: 1B (sec)",LIB,21.577,21.561 "Y-Cruncher - Pi Digits To Calculate: 500M (sec)",LIB,11.184,10.987 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,5.12686,5.03993 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,20.1433,20.2831 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,4.23625,3.81237 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,4.52585,4.58464 "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,21.0326,20.0581 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,19.1966,19.1791 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,6.0371,5.6399 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,28.7725,28.7918 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,4.14024,4.63109 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.51501,2.51294 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,7823,8504.29 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,5300.36,5167.81 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,7893.57,8148.11 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,5167.4,5128.18 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,9.48918,7.74858 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,8045.7,8013.88 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,5157.6,5207.64 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,29.7727,28.8968 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer (ms)",LIB,4233,4239 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer (ms)",LIB,4416,4403 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer (ms)",LIB,5127,5130 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer (ms)",LIB,78125,78264 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer (ms)",LIB,145358,145660 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer (ms)",LIB,80769,80340 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer (ms)",LIB,150980,151192 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer (ms)",LIB,91958,92675 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer (ms)",LIB,173492,173590 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer (ms)",LIB,1062,1060 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer (ms)",LIB,1096,1100 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer (ms)",LIB,1282,1277 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer (ms)",LIB,17188,17139 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer (ms)",LIB,34160,34140 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer (ms)",LIB,17838,17792 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer (ms)",LIB,35696,35397 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer (ms)",LIB,20597,20517 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer (ms)",LIB,41292,41490 "Timed Erlang/OTP Compilation - Time To Compile (sec)",LIB,124.066,124.216 "Timed Wasmer Compilation - Time To Compile (sec)",LIB,73.78,73.485 "FLAC Audio Encoding - WAV To FLAC (sec)",LIB,28.868,28.744 "FFmpeg - Encoder: libx264 - Scenario: Live (sec)",LIB,38.85,38.76 "FFmpeg - Encoder: libx264 - Scenario: Live (FPS)",HIB,129.99,130.30 "FFmpeg - Encoder: libx265 - Scenario: Live (sec)",LIB,86.74,88.58 "FFmpeg - Encoder: libx265 - Scenario: Live (FPS)",HIB,58.22,57.01 "FFmpeg - Encoder: libx264 - Scenario: Upload (sec)",LIB,314.58,315.290203876 "FFmpeg - Encoder: libx264 - Scenario: Upload (FPS)",HIB,8.03,8.01 "FFmpeg - Encoder: libx265 - Scenario: Upload (sec)",LIB,241.646159513,240.519283432 "FFmpeg - Encoder: libx265 - Scenario: Upload (FPS)",HIB,10.45,10.50 "FFmpeg - Encoder: libx264 - Scenario: Platform (sec)",LIB,249.00936878,250.99 "FFmpeg - Encoder: libx264 - Scenario: Platform (FPS)",HIB,30.42,30.18 "FFmpeg - Encoder: libx265 - Scenario: Platform (sec)",LIB,356.59243127,356.16 "FFmpeg - Encoder: libx265 - Scenario: Platform (FPS)",HIB,21.24,21.27 "FFmpeg - Encoder: libx264 - Scenario: Video On Demand (sec)",LIB,250.83,250.37 "FFmpeg - Encoder: libx264 - Scenario: Video On Demand (FPS)",HIB,30.20,30.25 "FFmpeg - Encoder: libx265 - Scenario: Video On Demand (sec)",LIB,357.150956529,358.61 "FFmpeg - Encoder: libx265 - Scenario: Video On Demand (FPS)",HIB,21.21,21.12 "Aircrack-ng - (k/s)",HIB,76103.367,74378.891 "Cpuminer-Opt - Algorithm: Magi (kH/s)",HIB,1711.45,1702.45 "Cpuminer-Opt - Algorithm: x25x (kH/s)",HIB,1458.5,1452.6 "Cpuminer-Opt - Algorithm: scrypt (kH/s)",HIB,496.52,499.97 "Cpuminer-Opt - Algorithm: Deepcoin (kH/s)",HIB,27510,27490 "Cpuminer-Opt - Algorithm: Ringcoin (kH/s)",HIB,18480,18440 "Cpuminer-Opt - Algorithm: Blake-2 S (kH/s)",HIB,1098730,1098280 "Cpuminer-Opt - Algorithm: Garlicoin (kH/s)",HIB,13970,14530 "Cpuminer-Opt - Algorithm: Skeincoin (kH/s)",HIB,222070,222040 "Cpuminer-Opt - Algorithm: Myriad-Groestl (kH/s)",HIB,154070,152420 "Cpuminer-Opt - Algorithm: LBC, LBRY Credits (kH/s)",HIB,56960,56960 "Cpuminer-Opt - Algorithm: Quad SHA-256, Pyrite (kH/s)",HIB,374460,374430 "Cpuminer-Opt - Algorithm: Triple SHA-256, Onecoin (kH/s)",HIB,718010,718140 "Node.js V8 Web Tooling Benchmark - (runs/s)",HIB,7.13,7.02 "ClickHouse - 100M Rows Web Analytics Dataset, First Run / Cold Cache (Queries/min, Geo Mean)",HIB,241.18,253.55 "ClickHouse - 100M Rows Web Analytics Dataset, Second Run (Queries/min, Geo Mean)",HIB,260.83,271.13 "ClickHouse - 100M Rows Web Analytics Dataset, Third Run (Queries/min, Geo Mean)",HIB,272.09,256.64 "ASTC Encoder - Preset: Fast (MT/s)",HIB,382.0137,376.393 "ASTC Encoder - Preset: Medium (MT/s)",HIB,151.8067,152.2417 "ASTC Encoder - Preset: Thorough (MT/s)",HIB,19.4177,19.4423 "ASTC Encoder - Preset: Exhaustive (MT/s)",HIB,2.2087,2.2093 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,42.69,42.91 "TensorFlow - Device: CPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,48.66,48.45 "TensorFlow - Device: CPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,59.55,59.76 "TensorFlow - Device: CPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,69.6,69.33 "TensorFlow - Device: CPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,69.82,69.69 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,24.77,24.47 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,8.13,8.12 "TensorFlow - Device: CPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,29.09,28.44 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,9.6,9.69 "TensorFlow - Device: CPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,30.13,30.12 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,10.11,10.17 "TensorFlow - Device: CPU - Batch Size: 256 - Model: GoogLeNet (images/sec)",HIB,30.11,29.87 "TensorFlow - Device: CPU - Batch Size: 256 - Model: ResNet-50 (images/sec)",HIB,10.92,10.86 "TensorFlow - Device: CPU - Batch Size: 512 - Model: GoogLeNet (images/sec)",HIB,30.61,30.42 "TensorFlow - Device: CPU - Batch Size: 512 - Model: ResNet-50 (images/sec)",HIB,11.24,11.32 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,22.6007,22.568 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,1387.0925,1398.9258 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,8.3625,8.2919 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,119.5632,120.5776 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,87.6373,87.3202 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,362.7795,364.9851 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,17.7786,19.1958 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,56.2277,52.0728 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,121.842,121.8565 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,261.4175,261.4233 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,39.942,40.071 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,25.014,24.9281 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,249.2856,250.8978 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,128.0038,127.2104 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,73.5683,73.866 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,13.5743,13.5155 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,192.2873,192.6373 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,166.1119,165.7009 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,51.2365,51.6311 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,19.4996,19.3504 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,96.0986,96.5473 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,331.234,330.5267 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,26.3211,26.046 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,37.9743,38.3752 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,22.6456,22.674 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,1387.2953,1390.6109 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,8.1225,8.2228 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,123.0974,121.5902 "Stress-NG - Test: MMAP (Bogo Ops/s)",HIB,2418.27,2342.6 "Stress-NG - Test: NUMA (Bogo Ops/s)",HIB,140.8,140.65 "Stress-NG - Test: Futex (Bogo Ops/s)",HIB,802471.3,796565.52 "Stress-NG - Test: MEMFD (Bogo Ops/s)",HIB,1751.42,1743.95 "Stress-NG - Test: Mutex (Bogo Ops/s)",HIB,17481256.34,17702491.79 "Stress-NG - Test: Atomic (Bogo Ops/s)",HIB,156802.93,172559.2 "Stress-NG - Test: Crypto (Bogo Ops/s)",HIB,67640.35,67622.54 "Stress-NG - Test: Malloc (Bogo Ops/s)",HIB,146984386.17,148029623.74 "Stress-NG - Test: Forking (Bogo Ops/s)",HIB,19768.12,19540.99 "Stress-NG - Test: IO_uring (Bogo Ops/s)",HIB,45731.6,45863.96 "Stress-NG - Test: SENDFILE (Bogo Ops/s)",HIB,678380.72,678714.22 "Stress-NG - Test: CPU Cache (Bogo Ops/s)",HIB,199.02,259.54 "Stress-NG - Test: CPU Stress (Bogo Ops/s)",HIB,106647.2,107074.79 "Stress-NG - Test: Semaphores (Bogo Ops/s)",HIB,7247266.69,7261328.18 "Stress-NG - Test: Matrix Math (Bogo Ops/s)",HIB,215006.68,215110.79 "Stress-NG - Test: Vector Math (Bogo Ops/s)",HIB,266411.19,266535.54 "Stress-NG - Test: x86_64 RdRand ()",,, "Stress-NG - Test: Memory Copying (Bogo Ops/s)",HIB,2328.14,2485.61 "Stress-NG - Test: Socket Activity (Bogo Ops/s)",HIB,25316.76,25937.03 "Stress-NG - Test: Context Switching (Bogo Ops/s)",HIB,14423511.17,15090386.73 "Stress-NG - Test: Glibc C String Functions (Bogo Ops/s)",HIB,5250106.25,5386266.71 "Stress-NG - Test: Glibc Qsort Data Sorting (Bogo Ops/s)",HIB,673.06,667.4 "Stress-NG - Test: System V Message Passing (Bogo Ops/s)",HIB,7616183.78,7408445.43 "spaCy - Model: en_core_web_lg (tokens/sec)",HIB,6798,6795 "spaCy - Model: en_core_web_trf (tokens/sec)",HIB,701,685 "Mobile Neural Network - Model: nasnet (ms)",LIB,42.874,40.336 "Mobile Neural Network - Model: mobilenetV3 (ms)",LIB,5.475,5.35 "Mobile Neural Network - Model: squeezenetv1.1 (ms)",LIB,10.633,10.152 "Mobile Neural Network - Model: resnet-v2-50 (ms)",LIB,50.815,47.115 "Mobile Neural Network - Model: SqueezeNetV1.0 (ms)",LIB,15.675,15.008 "Mobile Neural Network - Model: MobileNetV2_224 (ms)",LIB,9.862,9.957 "Mobile Neural Network - Model: mobilenet-v1-1.0 (ms)",LIB,7.677,7.386 "Mobile Neural Network - Model: inception-v3 (ms)",LIB,56.231,56.096 "NCNN - Target: CPU - Model: mobilenet (ms)",LIB,70.65,82.64 "NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,74.59,48.21 "NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,45.3,58.67 "NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,64,43.74 "NCNN - Target: CPU - Model: mnasnet (ms)",LIB,51.66,41.96 "NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,55.09,56.92 "NCNN - Target: CPU - Model: blazeface (ms)",LIB,42.63,31.18 "NCNN - Target: CPU - Model: googlenet (ms)",LIB,111.2,100.95 "NCNN - Target: CPU - Model: vgg16 (ms)",LIB,139.33,123.73 "NCNN - Target: CPU - Model: resnet18 (ms)",LIB,81.24,63.2 "NCNN - Target: CPU - Model: alexnet (ms)",LIB,65.6,53.71 "NCNN - Target: CPU - Model: resnet50 (ms)",LIB,178.31,110.59 "NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,75.13,78.03 "NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,100.38,94.37 "NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,286.78,208.14 "NCNN - Target: CPU - Model: vision_transformer (ms)",LIB,319.23,298.72 "NCNN - Target: CPU - Model: FastestDet (ms)",LIB,76.99,51.75 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,35.5,35.62 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,87.25,87.77 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,51.46,51.66 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,409.74,408.93 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,126.81,126.75 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,5.85,5.82 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,5452.5,5492 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,3.9,3.89 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,8087.11,8150.2 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,3.85,3.89 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,8245.26,8119.63 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,352.62,351.6 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,90.64,90.91 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,7.61,7.61 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,4182.46,4185.2 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,614.56,613.31 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,52.03,52.14 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,626.61,626.42 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,51.03,51.04 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,59.76,61.27 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,533.65,520.79 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,751.84,751.3 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,85.05,85.09 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,513.24,508.7 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,62.29,62.84 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,16068.58,16149.43 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,3.94,3.92 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,19469.8,19580.12 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,3.26,3.24 "Facebook RocksDB - Test: Random Read (Op/s)",HIB,198794977,196826975 "Facebook RocksDB - Test: Update Random (Op/s)",HIB,199394,198198 "Facebook RocksDB - Test: Read While Writing (Op/s)",HIB,5807440,6220606 "Facebook RocksDB - Test: Read Random Write Random (Op/s)",HIB,1551427,1558868 "nginx - Connections: 1 (Reqs/sec)",HIB,, "nginx - Connections: 20 (Reqs/sec)",HIB,, "nginx - Connections: 100 (Reqs/sec)",HIB,, "nginx - Connections: 200 (Reqs/sec)",HIB,100112.06,101709.29 "nginx - Connections: 500 (Reqs/sec)",HIB,99535.87,98639.62 "nginx - Connections: 1000 (Reqs/sec)",HIB,96265.52,95346.38 "nginx - Connections: 4000 (Reqs/sec)",HIB,, "Natron - Input: Spaceship (FPS)",HIB,3.5,3.6 "AI Benchmark Alpha - Device Inference Score (Score)",HIB,1245,1232 "AI Benchmark Alpha - Device Training Score (Score)",HIB,764,760 "AI Benchmark Alpha - Device AI Score (Score)",HIB,2009,1992 "EnCodec - Target Bandwidth: 3 kbps (sec)",LIB,66.471,68.291 "EnCodec - Target Bandwidth: 6 kbps (sec)",LIB,74.538,74.885 "EnCodec - Target Bandwidth: 24 kbps (sec)",LIB,78.457,81.167 "EnCodec - Target Bandwidth: 1.5 kbps (sec)",LIB,67.588,75.036 "BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,413004,406201