Xeon Plat 8280 March

2 x Intel Xeon Platinum 8280 testing with a GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS) and llvmpipe on Ubuntu 21.04 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 2103251-IB-XEONPLAT810
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C/C++ Compiler Tests 4 Tests
CPU Massive 5 Tests
Creator Workloads 9 Tests
Encoding 3 Tests
Game Development 2 Tests
HPC - High Performance Computing 3 Tests
Imaging 2 Tests
Machine Learning 2 Tests
Multi-Core 7 Tests
Programmer / Developer System Benchmarks 2 Tests
Server CPU Tests 5 Tests
Texture Compression 2 Tests
Video Encoding 3 Tests

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March 25 2021
  1 Hour, 17 Minutes
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March 25 2021
  1 Hour, 17 Minutes
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Xeon Plat 8280 March Suite 1.0.0 System Test suite extracted from Xeon Plat 8280 March. pts/sysbench-1.1.0 cpu run Test: CPU pts/aom-av1-2.3.0 --cpu-used=0 --limit=20 Bosphorus_3840x2160.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K pts/aom-av1-2.3.0 --cpu-used=4 Bosphorus_3840x2160.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K pts/aom-av1-2.3.0 --cpu-used=6 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K pts/aom-av1-2.3.0 --cpu-used=6 Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K pts/aom-av1-2.3.0 --cpu-used=8 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K pts/aom-av1-2.3.0 --cpu-used=9 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K pts/aom-av1-2.3.0 --cpu-used=0 --limit=20 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p pts/aom-av1-2.3.0 --cpu-used=4 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p pts/aom-av1-2.3.0 --cpu-used=6 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p pts/aom-av1-2.3.0 --cpu-used=6 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p pts/aom-av1-2.3.0 --cpu-used=8 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p pts/aom-av1-2.3.0 --cpu-used=9 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p pts/svt-hevc-1.2.0 -encMode 1 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 1 - Input: Bosphorus 1080p pts/svt-hevc-1.2.0 -encMode 7 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 7 - Input: Bosphorus 1080p pts/svt-hevc-1.2.0 -encMode 10 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 10 - Input: Bosphorus 1080p pts/svt-vp9-1.3.0 -tune 2 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: VMAF Optimized - Input: Bosphorus 1080p pts/svt-vp9-1.3.0 -tune 1 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p pts/svt-vp9-1.3.0 -tune 0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: Visual Quality Optimized - Input: Bosphorus 1080p pts/simdjson-1.2.0 kostya Throughput Test: Kostya pts/simdjson-1.2.0 large_random Throughput Test: LargeRandom pts/simdjson-1.2.0 partial_tweets Throughput Test: PartialTweets pts/simdjson-1.2.0 distinct_user_id Throughput Test: DistinctUserID pts/sysbench-1.1.0 memory run Test: RAM / Memory pts/jpegxl-1.2.0 sample-4.png out.jxl -s 5 --num_reps 55 Input: PNG - Encode Speed: 5 pts/jpegxl-1.2.0 sample-4.png out.jxl -s 7 --num_reps 45 Input: PNG - Encode Speed: 7 pts/jpegxl-1.2.0 sample-4.png out.jxl -s 8 --num_reps 12 Input: PNG - Encode Speed: 8 pts/jpegxl-1.2.0 sample-photo-6000x4000.JPG out.jxl -s 5 --num_reps 55 Input: JPEG - Encode Speed: 5 pts/jpegxl-1.2.0 sample-photo-6000x4000.JPG out.jxl -s 7 --num_reps 45 Input: JPEG - Encode Speed: 7 pts/jpegxl-1.2.0 sample-photo-6000x4000.JPG out.jxl -s 8 --num_reps 12 Input: JPEG - Encode Speed: 8 pts/jpegxl-decode-1.1.0 --num_threads=1 --num_reps=100 CPU Threads: 1 pts/jpegxl-decode-1.1.0 --num_reps=300 CPU Threads: All pts/stockfish-1.3.0 Total Time pts/liquid-dsp-1.0.0 -n 1 -b 256 -f 57 Threads: 1 - Buffer Length: 256 - Filter Length: 57 pts/liquid-dsp-1.0.0 -n 2 -b 256 -f 57 Threads: 2 - Buffer Length: 256 - Filter Length: 57 pts/liquid-dsp-1.0.0 -n 4 -b 256 -f 57 Threads: 4 - Buffer Length: 256 - Filter Length: 57 pts/liquid-dsp-1.0.0 -n 8 -b 256 -f 57 Threads: 8 - Buffer Length: 256 - Filter Length: 57 pts/liquid-dsp-1.0.0 -n 16 -b 256 -f 57 Threads: 16 - Buffer Length: 256 - Filter Length: 57 pts/liquid-dsp-1.0.0 -n 32 -b 256 -f 57 Threads: 32 - Buffer Length: 256 - Filter Length: 57 pts/liquid-dsp-1.0.0 -n 64 -b 256 -f 57 Threads: 64 - Buffer Length: 256 - Filter Length: 57 pts/liquid-dsp-1.0.0 -n 112 -b 256 -f 57 Threads: 112 - Buffer Length: 256 - Filter Length: 57 pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=bf16bf16bf16 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/mnn-1.2.0 Model: SqueezeNetV1.0 pts/mnn-1.2.0 Model: resnet-v2-50 pts/mnn-1.2.0 Model: MobileNetV2_224 pts/mnn-1.2.0 Model: mobilenet-v1-1.0 pts/mnn-1.2.0 Model: inception-v3 pts/incompact3d-2.0.2 input.i3d Input: X3D-benchmarking input.i3d pts/incompact3d-2.0.2 input_129_nodes.i3d Input: input.i3d 129 Cells Per Direction pts/incompact3d-2.0.2 input_193_nodes.i3d Input: input.i3d 193 Cells Per Direction pts/build-nodejs-1.0.0 Time To Compile pts/astcenc-1.1.0 -medium Preset: Medium pts/astcenc-1.1.0 -thorough Preset: Thorough pts/astcenc-1.1.0 -exhaustive Preset: Exhaustive pts/basis-1.1.0 Settings: ETC1S pts/basis-1.1.0 -uastc -uastc_level 0 Settings: UASTC Level 0 pts/basis-1.1.0 -uastc -uastc_level 2 Settings: UASTC Level 2 pts/basis-1.1.0 -uastc -uastc_level 3 Settings: UASTC Level 3 system/openscad-1.0.0 Pistol.scad Render: Pistol system/openscad-1.0.0 RetroCar.scad Render: Retro Car system/openscad-1.0.0 mini-itx.scad Render: Mini-ITX Case system/openscad-1.0.0 swivel.scad Render: Projector Mount Swivel system/openscad-1.0.0 leonardo_case_slim_lid_v1_2.scad Render: Leonardo Phone Case Slim