INTEL XEON PLATINUM 8592+ testing with a Quanta Cloud S6Q-MB-MPS (3B05.TEL4P1 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 2312187-NE-NET50813920 net - Phoronix Test Suite net INTEL XEON PLATINUM 8592+ testing with a Quanta Cloud S6Q-MB-MPS (3B05.TEL4P1 BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2312187-NE-NET50813920&grr .
net Processor Motherboard Chipset Memory Disk Graphics OS Kernel Desktop Display Server Compiler File-System Screen Resolution a b c INTEL XEON PLATINUM 8592+ @ 3.90GHz (64 Cores / 128 Threads) Quanta Cloud S6Q-MB-MPS (3B05.TEL4P1 BIOS) Intel Device 1bce 512GB 3201GB Micron_7450_MTFDKCC3T2TFS + 0GB Virtual HDisk0 + 0GB Virtual HDisk2 + 0GB Virtual HDisk1 + 0GB Virtual HDisk3 ASPEED Ubuntu 23.10 6.6.0-rc5-phx-patched (x86_64) GNOME Shell 45.0 X Server 1.21.1.7 GCC 13.2.0 ext4 1920x1200 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details - Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0x21000161 Java Details - a: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu1) Python Details - Python 3.11.6 Security Details - gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
net lczero: Eigen lczero: BLAS xmrig: GhostRider - 1M deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream deepsparse: ResNet-50, Baseline - Synchronous Single-Stream deepsparse: ResNet-50, Baseline - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream deepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Stream deepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Stream xmrig: KawPow - 1M xmrig: Monero - 1M xmrig: CryptoNight-Femto UPX2 - 1M xmrig: CryptoNight-Heavy - 1M svt-av1: Preset 4 - Bosphorus 4K xmrig: Wownero - 1M svt-av1: Preset 8 - Bosphorus 4K svt-av1: Preset 4 - Bosphorus 1080p svt-av1: Preset 8 - Bosphorus 1080p svt-av1: Preset 13 - Bosphorus 4K svt-av1: Preset 12 - Bosphorus 4K svt-av1: Preset 12 - Bosphorus 1080p svt-av1: Preset 13 - Bosphorus 1080p a b c 796 67 6862.9 28.4611 35.1023 370.7287 86.3024 32.0197 31.2246 13.7504 2323.9799 442.3119 71.9864 4.7354 210.9638 442.2210 72.0462 34.0725 938.0661 29.0016 34.4749 28.8890 34.6092 14.7276 67.8694 332.8063 96.1343 49.8955 641.1047 5.3729 186.0257 34.2126 934.6524 34.2750 933.0074 73.2660 436.5226 5.5953 5706.6964 70.7373 452.1951 6.0577 164.9393 5.9970 166.6567 3.4825 286.8882 3.4452 290.0133 1.4200 701.7915 39936.2 40329.6 40425.9 40394.9 7.156 43088.3 75.139 20.770 139.811 227.285 228.874 545.910 632.459 805 67 6857.8 28.8654 34.6174 370.7761 86.2914 32.0999 31.1465 13.7462 2324.7362 443.3971 71.6759 4.7386 210.8100 441.4776 72.0875 34.0905 937.5775 28.9953 34.4826 28.8756 34.6250 14.6612 68.1760 334.3153 95.6329 50.1708 637.6092 5.3005 188.5376 34.4463 928.4462 34.2173 934.5224 73.2935 436.3286 5.5844 5717.5185 70.7412 452.1641 6.0589 164.8968 6.0023 166.5123 3.4417 290.2826 3.4609 288.6849 1.4123 705.8268 40344.7 40153.2 40080.4 40282.2 7.168 42998.7 75.737 20.587 141.977 229.556 229.514 543.725 636.909 817 68 6820.8 29.6799 33.7246 371.2957 86.1709 32.0585 31.1875 13.7597 2322.5803 442.7525 71.9685 4.7654 209.6095 443.1582 71.7827 34.0792 937.8630 29.2030 34.2377 29.0205 34.4526 14.7898 67.5802 330.3352 96.8521 50.1864 637.4175 5.3092 188.2324 34.0380 939.5363 34.0080 940.2198 73.4267 435.4932 5.5965 5704.9530 71.0046 450.4918 6.0922 163.9718 6.0096 166.3050 3.4767 287.3469 3.4473 289.8081 1.4205 701.7061 40242.5 40072.2 40331.1 40286.6 7.151 43080.4 74.926 20.492 141.873 229.075 228.518 546.877 637.337 OpenBenchmarking.org
LeelaChessZero Backend: Eigen OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.30 Backend: Eigen a b c 200 400 600 800 1000 SE +/- 21.82, N = 9 SE +/- 11.08, N = 9 SE +/- 32.94, N = 6 796 805 817 1. (CXX) g++ options: -flto -pthread
LeelaChessZero Backend: BLAS OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.30 Backend: BLAS a b c 15 30 45 60 75 SE +/- 0.67, N = 3 SE +/- 0.67, N = 3 SE +/- 0.58, N = 3 67 67 68 1. (CXX) g++ options: -flto -pthread
Xmrig Variant: GhostRider - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: GhostRider - Hash Count: 1M a b c 1500 3000 4500 6000 7500 SE +/- 5.37, N = 3 SE +/- 29.78, N = 3 SE +/- 18.86, N = 3 6862.9 6857.8 6820.8 1. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream a b c 7 14 21 28 35 SE +/- 0.12, N = 3 SE +/- 0.29, N = 3 SE +/- 0.34, N = 15 28.46 28.87 29.68
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream a b c 8 16 24 32 40 SE +/- 0.16, N = 3 SE +/- 0.35, N = 3 SE +/- 0.38, N = 15 35.10 34.62 33.72
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream a b c 80 160 240 320 400 SE +/- 0.59, N = 3 SE +/- 0.36, N = 3 SE +/- 0.22, N = 3 370.73 370.78 371.30
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream a b c 20 40 60 80 100 SE +/- 0.14, N = 3 SE +/- 0.09, N = 3 SE +/- 0.05, N = 3 86.30 86.29 86.17
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream a b c 7 14 21 28 35 SE +/- 0.02, N = 3 SE +/- 0.03, N = 3 SE +/- 0.08, N = 3 32.02 32.10 32.06
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream a b c 7 14 21 28 35 SE +/- 0.02, N = 3 SE +/- 0.03, N = 3 SE +/- 0.08, N = 3 31.22 31.15 31.19
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 4 8 12 16 20 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 SE +/- 0.00, N = 3 13.75 13.75 13.76
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 500 1000 1500 2000 2500 SE +/- 2.42, N = 3 SE +/- 2.92, N = 3 SE +/- 0.54, N = 3 2323.98 2324.74 2322.58
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream a b c 100 200 300 400 500 SE +/- 0.57, N = 3 SE +/- 0.20, N = 3 SE +/- 0.77, N = 3 442.31 443.40 442.75
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream a b c 16 32 48 64 80 SE +/- 0.16, N = 3 SE +/- 0.17, N = 3 SE +/- 0.20, N = 3 71.99 71.68 71.97
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream a b c 1.0722 2.1444 3.2166 4.2888 5.361 SE +/- 0.0394, N = 3 SE +/- 0.0188, N = 3 SE +/- 0.0109, N = 3 4.7354 4.7386 4.7654
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream a b c 50 100 150 200 250 SE +/- 1.74, N = 3 SE +/- 0.83, N = 3 SE +/- 0.49, N = 3 210.96 210.81 209.61
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream a b c 100 200 300 400 500 SE +/- 0.73, N = 3 SE +/- 0.59, N = 3 SE +/- 0.25, N = 3 442.22 441.48 443.16
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream a b c 16 32 48 64 80 SE +/- 0.17, N = 3 SE +/- 0.04, N = 3 SE +/- 0.07, N = 3 72.05 72.09 71.78
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 8 16 24 32 40 SE +/- 0.01, N = 3 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 34.07 34.09 34.08
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 200 400 600 800 1000 SE +/- 0.34, N = 3 SE +/- 0.09, N = 3 SE +/- 0.07, N = 3 938.07 937.58 937.86
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream a b c 7 14 21 28 35 SE +/- 0.02, N = 3 SE +/- 0.05, N = 3 SE +/- 0.06, N = 3 29.00 29.00 29.20
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream a b c 8 16 24 32 40 SE +/- 0.02, N = 3 SE +/- 0.05, N = 3 SE +/- 0.07, N = 3 34.47 34.48 34.24
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream a b c 7 14 21 28 35 SE +/- 0.03, N = 3 SE +/- 0.02, N = 3 SE +/- 0.03, N = 3 28.89 28.88 29.02
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream a b c 8 16 24 32 40 SE +/- 0.04, N = 3 SE +/- 0.02, N = 3 SE +/- 0.03, N = 3 34.61 34.63 34.45
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream a b c 4 8 12 16 20 SE +/- 0.06, N = 3 SE +/- 0.03, N = 3 SE +/- 0.02, N = 3 14.73 14.66 14.79
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream a b c 15 30 45 60 75 SE +/- 0.27, N = 3 SE +/- 0.12, N = 3 SE +/- 0.11, N = 3 67.87 68.18 67.58
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream a b c 70 140 210 280 350 SE +/- 1.00, N = 3 SE +/- 0.71, N = 3 SE +/- 0.28, N = 3 332.81 334.32 330.34
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream a b c 20 40 60 80 100 SE +/- 0.29, N = 3 SE +/- 0.27, N = 3 SE +/- 0.08, N = 3 96.13 95.63 96.85
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream a b c 11 22 33 44 55 SE +/- 0.03, N = 3 SE +/- 0.15, N = 3 SE +/- 0.17, N = 3 49.90 50.17 50.19
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream a b c 140 280 420 560 700 SE +/- 0.45, N = 3 SE +/- 1.94, N = 3 SE +/- 2.11, N = 3 641.10 637.61 637.42
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream a b c 1.2089 2.4178 3.6267 4.8356 6.0445 SE +/- 0.0459, N = 3 SE +/- 0.0193, N = 3 SE +/- 0.0116, N = 3 5.3729 5.3005 5.3092
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream a b c 40 80 120 160 200 SE +/- 1.59, N = 3 SE +/- 0.69, N = 3 SE +/- 0.41, N = 3 186.03 188.54 188.23
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream a b c 8 16 24 32 40 SE +/- 0.13, N = 3 SE +/- 0.33, N = 3 SE +/- 0.03, N = 3 34.21 34.45 34.04
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream a b c 200 400 600 800 1000 SE +/- 3.60, N = 3 SE +/- 8.90, N = 3 SE +/- 0.83, N = 3 934.65 928.45 939.54
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream a b c 8 16 24 32 40 SE +/- 0.14, N = 3 SE +/- 0.17, N = 3 SE +/- 0.01, N = 3 34.28 34.22 34.01
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream a b c 200 400 600 800 1000 SE +/- 3.85, N = 3 SE +/- 4.66, N = 3 SE +/- 0.19, N = 3 933.01 934.52 940.22
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream a b c 16 32 48 64 80 SE +/- 0.05, N = 3 SE +/- 0.05, N = 3 SE +/- 0.03, N = 3 73.27 73.29 73.43
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream a b c 90 180 270 360 450 SE +/- 0.33, N = 3 SE +/- 0.34, N = 3 SE +/- 0.19, N = 3 436.52 436.33 435.49
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 1.2592 2.5184 3.7776 5.0368 6.296 SE +/- 0.0153, N = 3 SE +/- 0.0102, N = 3 SE +/- 0.0160, N = 3 5.5953 5.5844 5.5965
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 1200 2400 3600 4800 6000 SE +/- 15.58, N = 3 SE +/- 10.52, N = 3 SE +/- 16.89, N = 3 5706.70 5717.52 5704.95
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 16 32 48 64 80 SE +/- 0.05, N = 3 SE +/- 0.00, N = 3 SE +/- 0.09, N = 3 70.74 70.74 71.00
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 100 200 300 400 500 SE +/- 0.30, N = 3 SE +/- 0.02, N = 3 SE +/- 0.54, N = 3 452.20 452.16 450.49
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream a b c 2 4 6 8 10 SE +/- 0.0247, N = 3 SE +/- 0.0043, N = 3 SE +/- 0.0081, N = 3 6.0577 6.0589 6.0922
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream a b c 40 80 120 160 200 SE +/- 0.67, N = 3 SE +/- 0.12, N = 3 SE +/- 0.23, N = 3 164.94 164.90 163.97
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream a b c 2 4 6 8 10 SE +/- 0.0119, N = 3 SE +/- 0.0087, N = 3 SE +/- 0.0069, N = 3 5.9970 6.0023 6.0096
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream a b c 40 80 120 160 200 SE +/- 0.33, N = 3 SE +/- 0.25, N = 3 SE +/- 0.19, N = 3 166.66 166.51 166.31
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream a b c 0.7836 1.5672 2.3508 3.1344 3.918 SE +/- 0.0119, N = 3 SE +/- 0.0077, N = 3 SE +/- 0.0147, N = 3 3.4825 3.4417 3.4767
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream a b c 60 120 180 240 300 SE +/- 0.98, N = 3 SE +/- 0.65, N = 3 SE +/- 1.23, N = 3 286.89 290.28 287.35
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream a b c 0.7787 1.5574 2.3361 3.1148 3.8935 SE +/- 0.0193, N = 3 SE +/- 0.0119, N = 3 SE +/- 0.0117, N = 3 3.4452 3.4609 3.4473
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream a b c 60 120 180 240 300 SE +/- 1.63, N = 3 SE +/- 0.99, N = 3 SE +/- 0.98, N = 3 290.01 288.68 289.81
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream a b c 0.3196 0.6392 0.9588 1.2784 1.598 SE +/- 0.0038, N = 3 SE +/- 0.0057, N = 3 SE +/- 0.0007, N = 3 1.4200 1.4123 1.4205
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream a b c 150 300 450 600 750 SE +/- 2.07, N = 3 SE +/- 2.96, N = 3 SE +/- 0.26, N = 3 701.79 705.83 701.71
Xmrig Variant: KawPow - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: KawPow - Hash Count: 1M a b c 9K 18K 27K 36K 45K SE +/- 185.86, N = 3 SE +/- 97.76, N = 3 SE +/- 102.95, N = 3 39936.2 40344.7 40242.5 1. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Xmrig Variant: Monero - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: Monero - Hash Count: 1M a b c 9K 18K 27K 36K 45K SE +/- 106.27, N = 3 SE +/- 234.97, N = 3 SE +/- 234.22, N = 3 40329.6 40153.2 40072.2 1. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Xmrig Variant: CryptoNight-Femto UPX2 - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: CryptoNight-Femto UPX2 - Hash Count: 1M a b c 9K 18K 27K 36K 45K SE +/- 55.21, N = 3 SE +/- 72.11, N = 3 SE +/- 88.91, N = 3 40425.9 40080.4 40331.1 1. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Xmrig Variant: CryptoNight-Heavy - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: CryptoNight-Heavy - Hash Count: 1M a b c 9K 18K 27K 36K 45K SE +/- 42.10, N = 3 SE +/- 64.66, N = 3 SE +/- 169.87, N = 3 40394.9 40282.2 40286.6 1. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
SVT-AV1 Encoder Mode: Preset 4 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 4 - Input: Bosphorus 4K a b c 2 4 6 8 10 SE +/- 0.022, N = 3 SE +/- 0.025, N = 3 SE +/- 0.071, N = 3 7.156 7.168 7.151 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
Xmrig Variant: Wownero - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: Wownero - Hash Count: 1M a b c 9K 18K 27K 36K 45K SE +/- 76.66, N = 3 SE +/- 81.91, N = 3 SE +/- 104.95, N = 3 43088.3 42998.7 43080.4 1. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 8 - Input: Bosphorus 4K a b c 20 40 60 80 100 SE +/- 0.38, N = 3 SE +/- 0.27, N = 3 SE +/- 0.15, N = 3 75.14 75.74 74.93 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 4 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 4 - Input: Bosphorus 1080p a b c 5 10 15 20 25 SE +/- 0.08, N = 3 SE +/- 0.01, N = 3 SE +/- 0.25, N = 3 20.77 20.59 20.49 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 8 - Input: Bosphorus 1080p a b c 30 60 90 120 150 SE +/- 0.16, N = 3 SE +/- 1.29, N = 7 SE +/- 1.86, N = 3 139.81 141.98 141.87 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 13 - Input: Bosphorus 4K a b c 50 100 150 200 250 SE +/- 2.23, N = 3 SE +/- 0.75, N = 3 SE +/- 0.19, N = 3 227.29 229.56 229.08 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 12 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 12 - Input: Bosphorus 4K a b c 50 100 150 200 250 SE +/- 0.42, N = 3 SE +/- 1.07, N = 3 SE +/- 1.46, N = 3 228.87 229.51 228.52 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 12 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 12 - Input: Bosphorus 1080p a b c 120 240 360 480 600 SE +/- 4.95, N = 3 SE +/- 3.52, N = 3 SE +/- 4.85, N = 3 545.91 543.73 546.88 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 13 - Input: Bosphorus 1080p a b c 140 280 420 560 700 SE +/- 3.46, N = 3 SE +/- 4.83, N = 3 SE +/- 4.46, N = 3 632.46 636.91 637.34 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
Phoronix Test Suite v10.8.4