Xeon Silver March

Intel Xeon Silver 4216 testing with a TYAN S7100AG2NR (V4.02 BIOS) and ASPEED on Debian 10 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2103218-HA-XEONSILVE43
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C/C++ Compiler Tests 4 Tests
CPU Massive 5 Tests
Creator Workloads 5 Tests
Encoding 3 Tests
HPC - High Performance Computing 3 Tests
Machine Learning 2 Tests
Multi-Core 6 Tests
Server CPU Tests 5 Tests
Video Encoding 3 Tests

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March 21 2021
  2 Hours, 50 Minutes
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March 21 2021
  2 Hours, 52 Minutes
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March 21 2021
  2 Hours, 50 Minutes
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Xeon Silver March Suite 1.0.0 System Test suite extracted from Xeon Silver March. pts/sysbench-1.1.0 cpu run Test: CPU pts/aom-av1-2.2.0 --cpu-used=0 --limit=20 Encoder Mode: Speed 0 Two-Pass pts/aom-av1-2.2.0 --cpu-used=4 Encoder Mode: Speed 4 Two-Pass pts/aom-av1-2.2.0 --cpu-used=6 --rt Encoder Mode: Speed 6 Realtime pts/aom-av1-2.2.0 --cpu-used=6 Encoder Mode: Speed 6 Two-Pass pts/aom-av1-2.2.0 --cpu-used=8 --rt Encoder Mode: Speed 8 Realtime 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/luaradio-1.0.0 Test: Five Back to Back FIR Filters pts/luaradio-1.0.0 Test: FM Deemphasis Filter pts/luaradio-1.0.0 Test: Hilbert Transform pts/luaradio-1.0.0 Test: Complex Phase pts/sysbench-1.1.0 memory run Test: RAM / Memory pts/stockfish-1.3.0 Total Time 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_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/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