ddf

Tests for a future article. AMD EPYC 8534PN 64-Core testing with a AMD Cinnabar (RCB1009C 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 2401089-NE-DDF54911740
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January 07
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ddf Suite 1.0.0 System Test suite extracted from ddf. pts/pytorch-1.0.1 cpu 1 resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/pytorch-1.0.1 cpu 1 resnet152 Device: CPU - Batch Size: 1 - Model: ResNet-152 pts/pytorch-1.0.1 cpu 16 resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/pytorch-1.0.1 cpu 32 resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/pytorch-1.0.1 cpu 64 resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/pytorch-1.0.1 cpu 16 resnet152 Device: CPU - Batch Size: 16 - Model: ResNet-152 pts/pytorch-1.0.1 cpu 32 resnet152 Device: CPU - Batch Size: 32 - Model: ResNet-152 pts/pytorch-1.0.1 cpu 64 resnet152 Device: CPU - Batch Size: 64 - Model: ResNet-152 pts/pytorch-1.0.1 cpu 1 efficientnet_v2_l Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cpu 16 efficientnet_v2_l Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cpu 32 efficientnet_v2_l Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cpu 64 efficientnet_v2_l Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l 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/ffmpeg-6.1.0 --encoder=libx265 live Encoder: libx265 - Scenario: Live pts/ffmpeg-6.1.0 --encoder=libx265 upload Encoder: libx265 - Scenario: Upload pts/ffmpeg-6.1.0 --encoder=libx265 platform Encoder: libx265 - Scenario: Platform pts/ffmpeg-6.1.0 --encoder=libx265 vod Encoder: libx265 - Scenario: Video On Demand pts/embree-1.6.1 pathtracer -c crown/crown.ecs Binary: Pathtracer - Model: Crown pts/embree-1.6.1 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/embree-1.6.1 pathtracer -c asian_dragon/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon pts/embree-1.6.1 pathtracer -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon Obj pts/embree-1.6.1 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/embree-1.6.1 pathtracer_ispc -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon Obj 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/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/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 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/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/quantlib-1.2.0 --mp Configuration: Multi-Threaded pts/quantlib-1.2.0 Configuration: Single-Threaded 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/lczero-1.7.0 -b blas Backend: BLAS pts/lczero-1.7.0 -b eigen Backend: Eigen 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/cloverleaf-1.2.0 clover_bm Input: clover_bm pts/cloverleaf-1.2.0 clover_bm64_short Input: clover_bm64_short pts/openradioss-1.1.1 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/openradioss-1.1.1 NEON1M11_0000.rad NEON1M11_0001.rad Model: Chrysler Neon 1M pts/openradioss-1.1.1 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test pts/openradioss-1.1.1 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/openradioss-1.1.1 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation pts/openradioss-1.1.1 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 240 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 1200 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 2400 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 pts/build-ffmpeg-6.1.0 Time To Compile pts/build-gem5-1.1.0 Time To Compile pts/y-cruncher-1.4.0 1b Pi Digits To Calculate: 1B pts/y-cruncher-1.4.0 5b Pi Digits To Calculate: 5B pts/y-cruncher-1.4.0 10b Pi Digits To Calculate: 10B pts/y-cruncher-1.4.0 500m Pi Digits To Calculate: 500M pts/blender-4.0.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/blender-4.0.0 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only pts/blender-4.0.0 -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-4.0.0 -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-4.0.0 -b ../pavillon_barcelone_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Pabellon Barcelona - Compute: CPU-Only