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.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2211191-NE-ZEN1EPYC673
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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
Renderers 3 Tests
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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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 Suite 1.0.0 System Test suite extracted from zen 1 epyc. pts/unpack-linux-1.2.0 linux-5.19.tar.xz pts/blosc-1.2.0 blosclz shuffle Test: blosclz shuffle pts/blosc-1.2.0 blosclz bitshuffle Test: blosclz bitshuffle pts/minibude-1.0.0 --deck ../data/bm1 --iterations 500 Implementation: OpenMP - Input Deck: BM1 pts/minibude-1.0.0 --deck ../data/bm2 --iterations 10 Implementation: OpenMP - Input Deck: BM2 pts/smhasher-1.1.0 --test=Speed wyhash Hash: wyhash pts/smhasher-1.1.0 --test=Speed sha3-256 Hash: SHA3-256 pts/smhasher-1.1.0 --test=Speed Spooky32 Hash: Spooky32 pts/smhasher-1.1.0 --test=Speed fasthash32 Hash: fasthash32 pts/smhasher-1.1.0 --test=Speed FarmHash128 Hash: FarmHash128 pts/smhasher-1.1.0 --test=Speed t1ha2_atonce Hash: t1ha2_atonce pts/smhasher-1.1.0 --test=Speed FarmHash32 Hash: FarmHash32 x86_64 AVX pts/smhasher-1.1.0 --test=Speed t1ha0_aes_avx2 Hash: t1ha0_aes_avx2 x86_64 pts/smhasher-1.1.0 --test=Speed MeowHash Hash: MeowHash x86_64 AES-NI pts/nekrs-1.0.0 turbPipePeriodic turbPipe.par Input: TurboPipe Periodic pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Mesh Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Execution Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Mesh Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Execution Time pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/openradioss-1.0.0 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test pts/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/openradioss-1.0.0 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation pts/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container pts/lammps-1.4.0 benchmark_20k_atoms.in Model: 20k Atoms pts/lammps-1.4.0 in.rhodo Model: Rhodopsin Protein pts/xmrig-1.1.0 --bench=1M Variant: Monero - Hash Count: 1M pts/xmrig-1.1.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/jpegxl-1.5.0 sample-4.png out.jxl -q 80 --num_reps 50 Input: PNG - Quality: 80 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 90 --num_reps 40 Input: PNG - Quality: 90 pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 50 Input: JPEG - Quality: 80 pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 40 Input: JPEG - Quality: 90 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 100 --num_reps 10 Input: PNG - Quality: 100 pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 100 --num_reps 10 Input: JPEG - Quality: 100 pts/jpegxl-decode-1.5.0 --num_threads=1 --num_reps=100 CPU Threads: 1 pts/jpegxl-decode-1.5.0 --num_reps=200 CPU Threads: All pts/webp-1.2.0 Encode Settings: Default pts/webp-1.2.0 -q 100 Encode Settings: Quality 100 pts/webp-1.2.0 -q 100 -lossless Encode Settings: Quality 100, Lossless pts/webp-1.2.0 -q 100 -m 6 Encode Settings: Quality 100, Highest Compression pts/webp-1.2.0 -q 100 -lossless -m 6 Encode Settings: Quality 100, Lossless, Highest Compression pts/webp2-1.2.0 Encode Settings: Default pts/webp2-1.2.0 -q 75 -effort 7 Encode Settings: Quality 75, Compression Effort 7 pts/webp2-1.2.0 -q 95 -effort 7 Encode Settings: Quality 95, Compression Effort 7 pts/webp2-1.2.0 -q 100 -effort 5 Encode Settings: Quality 100, Compression Effort 5 pts/webp2-1.2.0 -q 100 -effort 9 Encode Settings: Quality 100, Lossless Compression pts/srsran-1.2.0 lib/src/phy/dft/test/ofdm_test -N 2048 -n 100 -r 500000 Test: OFDM_Test pts/srsran-1.2.0 lib/test/phy/phy_dl_test -p 100 -s 20000 -m 28 -t 4 Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM pts/srsran-1.2.0 lib/test/phy/phy_dl_test -p 100 -s 20000 -m 28 -t 1 Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM pts/srsran-1.2.0 lib/test/phy/phy_dl_test -p 100 -s 20000 -m 27 -t 4 -q Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM pts/srsran-1.2.0 lib/test/phy/phy_dl_test -p 100 -s 20000 -m 27 -t 1 -q Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM pts/srsran-1.2.0 lib/test/phy/phy_dl_nr_test -P 52 -p 52 -m 28 -n 20000 Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM pts/graphics-magick-2.1.0 -swirl 90 Operation: Swirl pts/graphics-magick-2.1.0 -rotate 90 Operation: Rotate pts/graphics-magick-2.1.0 -sharpen 0x2.0 Operation: Sharpen pts/graphics-magick-2.1.0 -enhance Operation: Enhanced pts/graphics-magick-2.1.0 -resize 50% Operation: Resizing pts/graphics-magick-2.1.0 -operator all Noise-Gaussian 30% Operation: Noise-Gaussian pts/graphics-magick-2.1.0 -colorspace HWB Operation: HWB Color Space pts/aom-av1-3.5.0 --cpu-used=0 --limit=20 Bosphorus_3840x2160.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=4 Bosphorus_3840x2160.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=6 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=6 Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=8 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=9 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=10 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=0 --limit=20 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p pts/aom-av1-3.5.0 --cpu-used=4 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p pts/aom-av1-3.5.0 --cpu-used=6 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p pts/aom-av1-3.5.0 --cpu-used=6 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p pts/aom-av1-3.5.0 --cpu-used=8 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p pts/aom-av1-3.5.0 --cpu-used=9 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p pts/aom-av1-3.5.0 --cpu-used=10 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p pts/svt-av1-2.6.0 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/svt-av1-2.6.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/svt-av1-2.6.0 --preset 10 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 10 - Input: Bosphorus 4K pts/svt-av1-2.6.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/svt-av1-2.6.0 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/svt-av1-2.6.0 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/svt-av1-2.6.0 --preset 10 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 10 - Input: Bosphorus 1080p pts/svt-av1-2.6.0 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/avifenc-1.3.0 -s 0 Encoder Speed: 0 pts/avifenc-1.3.0 -s 2 Encoder Speed: 2 pts/avifenc-1.3.0 -s 6 Encoder Speed: 6 pts/avifenc-1.3.0 -s 6 -l Encoder Speed: 6, Lossless pts/avifenc-1.3.0 -s 10 -l Encoder Speed: 10, Lossless pts/build-nodejs-1.2.0 Time To Compile pts/build-php-1.6.0 Time To Compile pts/build-python-1.0.0 Build Configuration: Default pts/build-python-1.0.0 --enable-optimizations --with-lto Build Configuration: Released Build, PGO + LTO Optimized pts/primesieve-1.9.0 1e12 Length: 1e12 pts/primesieve-1.9.0 1e13 Length: 1e13 pts/y-cruncher-1.2.0 1b Pi Digits To Calculate: 1B pts/y-cruncher-1.2.0 500m Pi Digits To Calculate: 500M pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=bf16bf16bf16 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer pts/build-erlang-1.2.0 Time To Compile pts/build-wasmer-1.2.0 Time To Compile pts/encode-flac-1.8.1 WAV To FLAC pts/ffmpeg-3.0.0 --encoder=libx264 live Encoder: libx264 - Scenario: Live pts/ffmpeg-3.0.0 --encoder=libx265 live Encoder: libx265 - Scenario: Live pts/ffmpeg-3.0.0 --encoder=libx264 upload Encoder: libx264 - Scenario: Upload pts/ffmpeg-3.0.0 --encoder=libx265 upload Encoder: libx265 - Scenario: Upload pts/ffmpeg-3.0.0 --encoder=libx264 platform Encoder: libx264 - Scenario: Platform pts/ffmpeg-3.0.0 --encoder=libx265 platform Encoder: libx265 - Scenario: Platform pts/ffmpeg-3.0.0 --encoder=libx264 vod Encoder: libx264 - Scenario: Video On Demand pts/ffmpeg-3.0.0 --encoder=libx265 vod Encoder: libx265 - Scenario: Video On Demand pts/aircrack-ng-1.3.0 pts/cpuminer-opt-1.6.0 -a m7m Algorithm: Magi pts/cpuminer-opt-1.6.0 -a x25x Algorithm: x25x pts/cpuminer-opt-1.6.0 -a scrypt Algorithm: scrypt pts/cpuminer-opt-1.6.0 -a deep Algorithm: Deepcoin pts/cpuminer-opt-1.6.0 -a minotaur Algorithm: Ringcoin pts/cpuminer-opt-1.6.0 -a blake2s Algorithm: Blake-2 S pts/cpuminer-opt-1.6.0 -a allium Algorithm: Garlicoin pts/cpuminer-opt-1.6.0 -a skein Algorithm: Skeincoin pts/cpuminer-opt-1.6.0 -a myr-gr Algorithm: Myriad-Groestl pts/cpuminer-opt-1.6.0 -a lbry Algorithm: LBC, LBRY Credits pts/cpuminer-opt-1.6.0 -a sha256q Algorithm: Quad SHA-256, Pyrite pts/cpuminer-opt-1.6.0 -a sha256t Algorithm: Triple SHA-256, Onecoin pts/node-web-tooling-1.0.1 pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, First Run / Cold Cache pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, Second Run pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, Third Run pts/astcenc-1.4.0 -fast -repeats 120 Preset: Fast pts/astcenc-1.4.0 -medium -repeats 20 Preset: Medium pts/astcenc-1.4.0 -thorough -repeats 10 Preset: Thorough pts/astcenc-1.4.0 -exhaustive -repeats 2 Preset: Exhaustive pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=alexnet Device: CPU - Batch Size: 512 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=googlenet Device: CPU - Batch Size: 512 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=resnet50 Device: CPU - Batch Size: 512 - Model: ResNet-50 pts/deepsparse-1.0.1 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario sync Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario async Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario sync Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario sync Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream pts/stress-ng-1.6.0 --mmap -1 Test: MMAP pts/stress-ng-1.6.0 --numa -1 Test: NUMA pts/stress-ng-1.6.0 --futex -1 Test: Futex pts/stress-ng-1.6.0 --memfd -1 Test: MEMFD pts/stress-ng-1.6.0 --mutex -1 Test: Mutex pts/stress-ng-1.6.0 --atomic -1 Test: Atomic pts/stress-ng-1.6.0 --crypt -1 Test: Crypto pts/stress-ng-1.6.0 --malloc -1 Test: Malloc pts/stress-ng-1.6.0 --fork -1 Test: Forking pts/stress-ng-1.6.0 --io-uring -1 Test: IO_uring pts/stress-ng-1.6.0 --sendfile -1 Test: SENDFILE pts/stress-ng-1.6.0 --cache -1 Test: CPU Cache pts/stress-ng-1.6.0 --cpu -1 --cpu-method all Test: CPU Stress pts/stress-ng-1.6.0 --sem -1 Test: Semaphores pts/stress-ng-1.6.0 --matrix -1 Test: Matrix Math pts/stress-ng-1.6.0 --vecmath -1 Test: Vector Math pts/stress-ng-1.6.0 --rdrand -1 Test: x86_64 RdRand pts/stress-ng-1.6.0 --memcpy -1 Test: Memory Copying pts/stress-ng-1.6.0 --sock -1 Test: Socket Activity pts/stress-ng-1.6.0 --switch -1 Test: Context Switching pts/stress-ng-1.6.0 --str -1 Test: Glibc C String Functions pts/stress-ng-1.6.0 --qsort -1 Test: Glibc Qsort Data Sorting pts/stress-ng-1.6.0 --msg -1 Test: System V Message Passing pts/spacy-1.0.0 Model: en_core_web_lg pts/spacy-1.0.0 Model: en_core_web_trf pts/mnn-2.1.0 Model: nasnet pts/mnn-2.1.0 Model: mobilenetV3 pts/mnn-2.1.0 Model: squeezenetv1.1 pts/mnn-2.1.0 Model: resnet-v2-50 pts/mnn-2.1.0 Model: SqueezeNetV1.0 pts/mnn-2.1.0 Model: MobileNetV2_224 pts/mnn-2.1.0 Model: mobilenet-v1-1.0 pts/mnn-2.1.0 Model: inception-v3 pts/ncnn-1.4.0 -1 Target: CPU - Model: mobilenet pts/ncnn-1.4.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.4.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.4.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.4.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.4.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.4.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.4.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.4.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.4.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.4.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.4.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.4.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.4.0 -1 Target: CPU - Model: vision_transformer pts/ncnn-1.4.0 -1 Target: CPU - Model: FastestDet pts/blender-3.3.1 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/blender-3.3.1 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only pts/blender-3.3.1 -b ../fishy_cat_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Fishy Cat - Compute: CPU-Only pts/blender-3.3.1 -b ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only pts/blender-3.3.1 -b ../pavillon_barcelone_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Pabellon Barcelona - Compute: CPU-Only pts/openvino-1.1.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/openvino-1.1.0 -m models/intel/person-detection-0106/FP16/person-detection-0106.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/openvino-1.1.0 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openvino-1.1.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/openvino-1.1.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/openvino-1.1.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/openvino-1.1.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/openvino-1.1.0 -m models/intel/machine-translation-nar-en-de-0002/FP16/machine-translation-nar-en-de-0002.xml -d CPU Model: Machine Translation EN To DE FP16 - Device: CPU pts/openvino-1.1.0 -m models/intel/weld-porosity-detection-0001/FP16-INT8/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16-INT8 - Device: CPU pts/openvino-1.1.0 -m models/intel/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004.xml -d CPU Model: Person Vehicle Bike Detection FP16 - Device: CPU pts/openvino-1.1.0 -m models/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU pts/openvino-1.1.0 -m models/intel/age-gender-recognition-retail-0013/FP16-INT8/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU pts/rocksdb-1.3.0 --benchmarks="readrandom" Test: Random Read pts/rocksdb-1.3.0 --benchmarks="updaterandom" Test: Update Random pts/rocksdb-1.3.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.3.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/nginx-3.0.0 -c 1 Connections: 1 pts/nginx-3.0.0 -c 20 Connections: 20 pts/nginx-3.0.0 -c 100 Connections: 100 pts/nginx-3.0.0 -c 200 Connections: 200 pts/nginx-3.0.0 -c 500 Connections: 500 pts/nginx-3.0.0 -c 1000 Connections: 1000 pts/nginx-3.0.0 -c 4000 Connections: 4000 pts/natron-1.1.0 Natron_2.3.12_Spaceship/Natron_project/Spaceship_Natron.ntp Input: Spaceship pts/ai-benchmark-1.0.2 Device Inference Score pts/ai-benchmark-1.0.2 Device Training Score pts/ai-benchmark-1.0.2 Device AI Score pts/encodec-1.0.1 -b 3 Target Bandwidth: 3 kbps pts/encodec-1.0.1 -b 6 Target Bandwidth: 6 kbps pts/encodec-1.0.1 -b 24 Target Bandwidth: 24 kbps pts/encodec-1.0.1 -b 1.5 Target Bandwidth: 1.5 kbps pts/brl-cad-1.3.0 VGR Performance Metric