eps

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 2312241-NE-EPS60637430
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CPU Massive 3 Tests
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December 24 2023
  1 Day, 26 Minutes
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December 25 2023
  7 Hours, 39 Minutes
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eps Suite 1.0.0 System Test suite extracted from eps. pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Geometric Mean Of All Queries pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q01 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q02 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q03 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q04 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q05 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q06 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q07 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q08 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q09 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q10 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q11 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q12 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q13 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q14 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q15 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q16 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q17 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q18 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q19 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q20 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q21 pts/spark-tpch-1.0.0 -s 1 Scale Factor: 1 - Q22 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Geometric Mean Of All Queries pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q01 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q02 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q03 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q04 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q05 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q06 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q07 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q08 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q09 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q10 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q11 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q12 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q13 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q14 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q15 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q16 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q17 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q18 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q19 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q20 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q21 pts/spark-tpch-1.0.0 -s 10 Scale Factor: 10 - Q22 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Geometric Mean Of All Queries pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q01 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q02 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q03 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q04 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q05 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q06 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q07 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q08 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q09 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q10 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q11 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q12 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q13 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q14 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q15 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q16 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q17 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q18 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q19 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q20 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q21 pts/spark-tpch-1.0.0 -s 50 Scale Factor: 50 - Q22 pts/java-scimark2-1.2.0 TEST_COMPOSITE Computational Test: Composite pts/java-scimark2-1.2.0 TEST_MONTE Computational Test: Monte Carlo pts/java-scimark2-1.2.0 TEST_FFT Computational Test: Fast Fourier Transform pts/java-scimark2-1.2.0 TEST_SPARSE Computational Test: Sparse Matrix Multiply pts/java-scimark2-1.2.0 TEST_DENSE Computational Test: Dense LU Matrix Factorization pts/java-scimark2-1.2.0 TEST_SOR Computational Test: Jacobi Successive Over-Relaxation pts/lczero-1.7.0 -b blas Backend: BLAS pts/lczero-1.7.0 -b eigen Backend: Eigen 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 system/openssl-1.2.0 sha256 Algorithm: SHA256 system/openssl-1.2.0 sha512 Algorithm: SHA512 system/openssl-1.2.0 rsa4096 Algorithm: RSA4096 system/openssl-1.2.0 -evp chacha20 Algorithm: ChaCha20 system/openssl-1.2.0 -evp aes-128-gcm Algorithm: AES-128-GCM system/openssl-1.2.0 -evp aes-256-gcm Algorithm: AES-256-GCM system/openssl-1.2.0 -evp chacha20-poly1305 Algorithm: ChaCha20-Poly1305 pts/pytorch-1.0.0 cpu 1 resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/pytorch-1.0.0 cpu 1 resnet152 Device: CPU - Batch Size: 1 - Model: ResNet-152 pts/pytorch-1.0.0 cpu 16 resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/pytorch-1.0.0 cpu 32 resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/pytorch-1.0.0 cpu 16 resnet152 Device: CPU - Batch Size: 16 - Model: ResNet-152 pts/pytorch-1.0.0 cpu 256 resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/pytorch-1.0.0 cpu 32 resnet152 Device: CPU - Batch Size: 32 - Model: ResNet-152 pts/pytorch-1.0.0 cpu 256 resnet152 Device: CPU - Batch Size: 256 - Model: ResNet-152 pts/pytorch-1.0.0 cpu 1 efficientnet_v2_l Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l pts/pytorch-1.0.0 cpu 16 efficientnet_v2_l Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l pts/pytorch-1.0.0 cpu 32 efficientnet_v2_l Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l pts/pytorch-1.0.0 cpu 256 efficientnet_v2_l Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l 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/webp2-1.2.1 Encode Settings: Default pts/webp2-1.2.1 -q 75 -effort 7 Encode Settings: Quality 75, Compression Effort 7 pts/webp2-1.2.1 -q 95 -effort 7 Encode Settings: Quality 95, Compression Effort 7 pts/webp2-1.2.1 -q 100 -effort 5 Encode Settings: Quality 100, Compression Effort 5 pts/webp2-1.2.1 -q 100 -effort 9 Encode Settings: Quality 100, Lossless Compression pts/xmrig-1.2.0 -a kawpow --bench=1M Variant: KawPow - Hash Count: 1M pts/xmrig-1.2.0 --bench=1M Variant: Monero - Hash Count: 1M pts/xmrig-1.2.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/xmrig-1.2.0 -a gr --bench=1M Variant: GhostRider - Hash Count: 1M pts/xmrig-1.2.0 -a cn-heavy/0 --bench=1M Variant: CryptoNight-Heavy - Hash Count: 1M pts/xmrig-1.2.0 -a cn/upx2 --bench=1M Variant: CryptoNight-Femto UPX2 - Hash Count: 1M