xeon platinum 8280 2023

Tests for a future article. 2 x Intel Xeon Platinum 8280 testing with a GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS) and ASPEED 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 2301061-NE-XEONPLATI67
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C/C++ Compiler Tests 2 Tests
CPU Massive 3 Tests
Creator Workloads 6 Tests
Database Test Suite 2 Tests
Encoding 2 Tests
HPC - High Performance Computing 2 Tests
Machine Learning 2 Tests
Multi-Core 6 Tests
Intel oneAPI 3 Tests
Server 2 Tests
Video Encoding 2 Tests

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January 06 2023
  3 Hours, 31 Minutes
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January 06 2023
  3 Hours, 33 Minutes
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xeon platinum 8280 2023 Suite 1.0.0 System Test suite extracted from xeon platinum 8280 2023. pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset slow Video Input: Bosphorus 4K - Video Preset: Slow pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset slow Video Input: Bosphorus 1080p - Video Preset: Slow pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset medium Video Input: Bosphorus 1080p - Video Preset: Medium pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset superfast Video Input: Bosphorus 4K - Video Preset: Super Fast pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset veryfast Video Input: Bosphorus 1080p - Video Preset: Very Fast pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset superfast Video Input: Bosphorus 1080p - Video Preset: Super Fast pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset ultrafast Video Input: Bosphorus 1080p - Video Preset: Ultra Fast pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset slow Video Input: Bosphorus 4K - Video Preset: Slow pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset slow Video Input: Bosphorus 1080p - Video Preset: Slow pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset medium Video Input: Bosphorus 1080p - Video Preset: Medium pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset superfast Video Input: Bosphorus 4K - Video Preset: Super Fast pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset veryfast Video Input: Bosphorus 1080p - Video Preset: Very Fast pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset superfast Video Input: Bosphorus 1080p - Video Preset: Super Fast pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset ultrafast Video Input: Bosphorus 1080p - Video Preset: Ultra Fast pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=ispc Benchmark: vklBenchmark ISPC pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=scalar Benchmark: vklBenchmark Scalar pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=bf16bf16bf16 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/cockroach-1.0.2 movr --concurrency 128 Workload: MoVR - Concurrency: 128 pts/cockroach-1.0.2 movr --concurrency 256 Workload: MoVR - Concurrency: 256 pts/cockroach-1.0.2 movr --concurrency 512 Workload: MoVR - Concurrency: 512 pts/cockroach-1.0.2 movr --concurrency 1024 Workload: MoVR - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 128 Workload: KV, 10% Reads - Concurrency: 128 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 256 Workload: KV, 10% Reads - Concurrency: 256 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 512 Workload: KV, 10% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 128 Workload: KV, 50% Reads - Concurrency: 128 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 256 Workload: KV, 50% Reads - Concurrency: 256 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 512 Workload: KV, 50% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 128 Workload: KV, 60% Reads - Concurrency: 128 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 256 Workload: KV, 60% Reads - Concurrency: 256 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 512 Workload: KV, 60% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 128 Workload: KV, 95% Reads - Concurrency: 128 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 256 Workload: KV, 95% Reads - Concurrency: 256 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 512 Workload: KV, 95% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 1024 Workload: KV, 10% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 1024 Workload: KV, 50% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 1024 Workload: KV, 60% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 1024 Workload: KV, 95% Reads - Concurrency: 1024 pts/pgbench-1.13.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 800 -S Scaling Factor: 1 - Clients: 800 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 800 -S Scaling Factor: 1 - Clients: 800 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 1000 -S Scaling Factor: 1 - Clients: 1000 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 1000 -S Scaling Factor: 1 - Clients: 1000 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 250 Scaling Factor: 1 - Clients: 250 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 250 Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 800 Scaling Factor: 1 - Clients: 800 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 800 Scaling Factor: 1 - Clients: 800 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 1000 Scaling Factor: 1 - Clients: 1000 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 1000 Scaling Factor: 1 - Clients: 1000 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 800 -S Scaling Factor: 100 - Clients: 800 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 800 -S Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 1000 -S Scaling Factor: 100 - Clients: 1000 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 1000 -S Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 800 Scaling Factor: 100 - Clients: 800 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 800 Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 1000 Scaling Factor: 100 - Clients: 1000 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 1000 Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency pts/openvino-1.2.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/person-detection-0106/FP16/person-detection-0106.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openvino-1.2.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/openvino-1.2.0 -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-1.2.0 -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-1.2.0 -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-1.2.0 -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-1.2.0 -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/brl-cad-1.4.0 VGR Performance Metric