8490h april

2 x Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED 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 2304136-NE-8490HAPRI45
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C/C++ Compiler Tests 2 Tests
CPU Massive 5 Tests
Creator Workloads 3 Tests
HPC - High Performance Computing 2 Tests
Machine Learning 2 Tests
Multi-Core 3 Tests
Server 2 Tests
Server CPU Tests 2 Tests

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a
April 12 2023
  52 Minutes
b
April 12 2023
  4 Hours, 49 Minutes
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April 13 2023
  52 Minutes
d
April 13 2023
  52 Minutes
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April 13 2023
  52 Minutes
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8490h april Suite 1.0.0 System Test suite extracted from 8490h april. pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/vvenc-1.8.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset faster Video Input: Bosphorus 1080p - Video Preset: Faster pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/apache-3.0.0 -c 500 Concurrent Requests: 500 pts/srsran-2.0.0 tests/benchmarks/phy/upper/channel_processors/pusch_processor_benchmark -m throughput_total -R 100 Test: PUSCH Processor Benchmark, Throughput Total pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/tensorflow-2.1.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/tensorflow-2.1.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/blender-3.5.0 -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/srsran-2.0.0 tests/benchmarks/phy/upper/channel_processors/pusch_processor_benchmark -m throughput_thread -R 100 Test: PUSCH Processor Benchmark, Throughput Thread pts/vvenc-1.8.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset fast Video Input: Bosphorus 1080p - Video Preset: Fast pts/blender-3.5.0 -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/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/vvenc-1.8.0 -i Bosphorus_3840x2160.y4m --preset fast Video Input: Bosphorus 4K - Video Preset: Fast pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=googlenet Device: CPU - Batch Size: 512 - Model: GoogLeNet pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/blender-3.5.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/tensorflow-2.1.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/srsran-2.0.0 tests/benchmarks/phy/upper/downlink_processor_benchmark -R 50000 Test: Downlink Processor Benchmark pts/nginx-3.0.1 -c 500 Connections: 500 pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=resnet50 Device: CPU - Batch Size: 512 - Model: ResNet-50 pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=alexnet Device: CPU - Batch Size: 512 - Model: AlexNet pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet pts/blender-3.5.0 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only pts/vvenc-1.8.0 -i Bosphorus_3840x2160.y4m --preset faster Video Input: Bosphorus 4K - Video Preset: Faster pts/blender-3.5.0 -b ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only pts/apache-3.0.0 -c 1000 Concurrent Requests: 1000 pts/apache-3.0.0 -c 200 Concurrent Requests: 200 pts/apache-3.0.0 -c 100 Concurrent Requests: 100 pts/nginx-3.0.1 -c 1000 Connections: 1000 pts/nginx-3.0.1 -c 200 Connections: 200 pts/nginx-3.0.1 -c 100 Connections: 100 pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU