9684x-march

2 x AMD EPYC 9684X 96-Core testing with a AMD Titanite_4G (RTI1007B BIOS) and ASPEED on Ubuntu 23.10 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 2403274-NE-9684XMARC65
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CPU Massive 3 Tests
Creator Workloads 2 Tests
HPC - High Performance Computing 2 Tests
Machine Learning 2 Tests
Multi-Core 2 Tests
Python Tests 3 Tests
Common Workstation Benchmarks 2 Tests

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March 27
  2 Hours, 34 Minutes
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March 27
  8 Hours, 3 Minutes
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March 27
  2 Hours, 46 Minutes
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9684x-march Suite 1.0.0 System Test suite extracted from 9684x-march . pts/blender-4.1.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/blender-4.1.0 -b ../junkshop.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Junkshop - Compute: CPU-Only pts/blender-4.1.0 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only pts/blender-4.1.0 -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/blender-4.1.0 -b ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only pts/blender-4.1.0 -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/brl-cad-1.6.0 VGR Performance Metric pts/pytorch-1.1.0 cpu 1 resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/pytorch-1.1.0 cpu 1 resnet152 Device: CPU - Batch Size: 1 - Model: ResNet-152 pts/pytorch-1.1.0 cpu 16 resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/pytorch-1.1.0 cpu 32 resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/pytorch-1.1.0 cpu 64 resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/pytorch-1.1.0 cpu 16 resnet152 Device: CPU - Batch Size: 16 - Model: ResNet-152 pts/pytorch-1.1.0 cpu 256 resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/pytorch-1.1.0 cpu 32 resnet152 Device: CPU - Batch Size: 32 - Model: ResNet-152 pts/pytorch-1.1.0 cpu 512 resnet50 Device: CPU - Batch Size: 512 - Model: ResNet-50 pts/pytorch-1.1.0 cpu 64 resnet152 Device: CPU - Batch Size: 64 - Model: ResNet-152 pts/pytorch-1.1.0 cpu 256 resnet152 Device: CPU - Batch Size: 256 - Model: ResNet-152 pts/pytorch-1.1.0 cpu 512 resnet152 Device: CPU - Batch Size: 512 - Model: ResNet-152 pts/pytorch-1.1.0 cpu 1 efficientnet_v2_l Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l pts/pytorch-1.1.0 cpu 16 efficientnet_v2_l Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l pts/pytorch-1.1.0 cpu 32 efficientnet_v2_l Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l pts/pytorch-1.1.0 cpu 64 efficientnet_v2_l Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l pts/pytorch-1.1.0 cpu 256 efficientnet_v2_l Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l pts/pytorch-1.1.0 cpu 512 efficientnet_v2_l Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l pts/rocksdb-1.6.0 --benchmarks="overwrite" Test: Overwrite pts/rocksdb-1.6.0 --benchmarks="readrandom" Test: Random Read pts/rocksdb-1.6.0 --benchmarks="updaterandom" Test: Update Random pts/rocksdb-1.6.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.6.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/tensorflow-2.2.0 --device cpu --batch_size=1 --model=alexnet Device: CPU - Batch Size: 1 - Model: AlexNet pts/tensorflow-2.2.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/tensorflow-2.2.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/tensorflow-2.2.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/tensorflow-2.2.0 --device cpu --batch_size=1 --model=googlenet Device: CPU - Batch Size: 1 - Model: GoogLeNet pts/tensorflow-2.2.0 --device cpu --batch_size=1 --model=resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/tensorflow-2.2.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet pts/tensorflow-2.2.0 --device cpu --batch_size=512 --model=alexnet Device: CPU - Batch Size: 512 - Model: AlexNet pts/tensorflow-2.2.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/tensorflow-2.2.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/tensorflow-2.2.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/tensorflow-2.2.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/tensorflow-2.2.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/tensorflow-2.2.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/tensorflow-2.2.0 --device cpu --batch_size=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet pts/tensorflow-2.2.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/tensorflow-2.2.0 --device cpu --batch_size=512 --model=googlenet Device: CPU - Batch Size: 512 - Model: GoogLeNet pts/tensorflow-2.2.0 --device cpu --batch_size=512 --model=resnet50 Device: CPU - Batch Size: 512 - Model: ResNet-50 pts/tensorflow-2.2.0 --device cpu --batch_size=1 --model=vgg16 Device: CPU - Batch Size: 1 - Model: VGG-16 pts/tensorflow-2.2.0 --device cpu --batch_size=16 --model=vgg16 Device: CPU - Batch Size: 16 - Model: VGG-16 pts/tensorflow-2.2.0 --device cpu --batch_size=32 --model=vgg16 Device: CPU - Batch Size: 32 - Model: VGG-16 pts/tensorflow-2.2.0 --device cpu --batch_size=64 --model=vgg16 Device: CPU - Batch Size: 64 - Model: VGG-16 pts/tensorflow-2.2.0 --device cpu --batch_size=256 --model=vgg16 Device: CPU - Batch Size: 256 - Model: VGG-16 pts/tensorflow-2.2.0 --device cpu --batch_size=512 --model=vgg16 Device: CPU - Batch Size: 512 - Model: VGG-16 pts/build-mesa-1.1.0 Time To Compile