2024 year

AMD Ryzen Threadripper PRO 5965WX 24-Cores testing with a ASUS Pro WS WRX80E-SAGE SE WIFI (1201 BIOS) and ASUS NVIDIA NV106 2GB 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 2402040-NE-2024YEAR116
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AV1 2 Tests
BLAS (Basic Linear Algebra Sub-Routine) Tests 2 Tests
CPU Massive 4 Tests
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HPC - High Performance Computing 6 Tests
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Machine Learning 6 Tests
Multi-Core 2 Tests
Python Tests 3 Tests
Video Encoding 2 Tests

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a
February 03
  1 Hour, 38 Minutes
b
February 04
  4 Hours, 50 Minutes
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February 04
  1 Hour, 37 Minutes
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February 04
  1 Hour, 34 Minutes
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2024 year Suite 1.0.0 System Test suite extracted from 2024 year. pts/cachebench-1.2.0 -r Test: Read pts/cachebench-1.2.0 -w Test: Write pts/cachebench-1.2.0 -b Test: Read / Modify / Write pts/lczero-1.7.0 -b blas Backend: BLAS pts/lczero-1.7.0 -b eigen Backend: Eigen pts/llama-cpp-1.0.0 -m ../llama-2-7b.Q4_0.gguf Model: llama-2-7b.Q4_0.gguf pts/llama-cpp-1.0.0 -m ../llama-2-13b.Q4_0.gguf Model: llama-2-13b.Q4_0.gguf pts/llama-cpp-1.0.0 -m ../llama-2-70b-chat.Q5_0.gguf Model: llama-2-70b-chat.Q5_0.gguf pts/llamafile-1.0.0 run-llava --gpu DISABLE Test: llava-v1.5-7b-q4 - Acceleration: CPU pts/llamafile-1.0.0 run-mistral --gpu DISABLE Test: mistral-7b-instruct-v0.2.Q8_0 - Acceleration: CPU pts/llamafile-1.0.0 run-wizardcoder --gpu DISABLE Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU pts/compress-lz4-1.1.0 -b1 -e1 Compression Level: 1 - Compression Speed pts/compress-lz4-1.1.0 -b1 -e1 Compression Level: 1 - Decompression Speed pts/compress-lz4-1.1.0 -b3 -e3 Compression Level: 3 - Compression Speed pts/compress-lz4-1.1.0 -b3 -e3 Compression Level: 3 - Decompression Speed pts/compress-lz4-1.1.0 -b9 -e9 Compression Level: 9 - Compression Speed pts/compress-lz4-1.1.0 -b9 -e9 Compression Level: 9 - Decompression Speed pts/deepsparse-1.6.0 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario sync Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario sync Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario async Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario sync Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none --input_shapes='[1,128]' --scenario sync Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario async Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario sync Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario sync Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario sync Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.6.0 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream pts/pytorch-1.0.1 cpu 1 resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/pytorch-1.0.1 cpu 16 resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/pytorch-1.0.1 cpu 256 resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/quicksilver-1.0.0 ../Examples/CTS2_Benchmark/CTS2.inp Input: CTS2 pts/quicksilver-1.0.0 ../Examples/CORAL2_Benchmark/Problem1/Coral2_P1.inp Input: CORAL2 P1 pts/quicksilver-1.0.0 ../Examples/CORAL2_Benchmark/Problem2/Coral2_P2.inp Input: CORAL2 P2 pts/rav1e-1.8.0 -s 1 -l 80 Speed: 1 pts/rav1e-1.8.0 -s 5 -l 200 Speed: 5 pts/rav1e-1.8.0 -s 6 -l 200 Speed: 6 pts/rav1e-1.8.0 -s 10 -l 600 Speed: 10 pts/speedb-1.0.1 --benchmarks="fillrandom" Test: Random Fill pts/speedb-1.0.1 --benchmarks="readrandom" Test: Random Read pts/speedb-1.0.1 --benchmarks="updaterandom" Test: Update Random pts/speedb-1.0.1 --benchmarks="fillseq" Test: Sequential Fill pts/speedb-1.0.1 --benchmarks="fillsync" Test: Random Fill Sync pts/speedb-1.0.1 --benchmarks="readwhilewriting" Test: Read While Writing pts/speedb-1.0.1 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/svt-av1-2.11.1 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/svt-av1-2.11.1 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/svt-av1-2.11.1 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/svt-av1-2.11.1 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/svt-av1-2.11.1 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/svt-av1-2.11.1 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/svt-av1-2.11.1 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/svt-av1-2.11.1 --preset 13 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 13 - Input: Bosphorus 1080p pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=vgg16 Device: CPU - Batch Size: 1 - Model: VGG-16 pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=alexnet Device: CPU - Batch Size: 1 - Model: AlexNet pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=vgg16 Device: CPU - Batch Size: 16 - Model: VGG-16 pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=googlenet Device: CPU - Batch Size: 1 - Model: GoogLeNet pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/y-cruncher-1.4.0 1b Pi Digits To Calculate: 1B pts/y-cruncher-1.4.0 500m Pi Digits To Calculate: 500M