Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED on Ubuntu 22.04 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 2307296-NE-8490H1S1663 8490h 1s - Phoronix Test Suite 8490h 1s Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2307296-NE-8490H1S1663&export=pdf&gru&sro&rro .
8490h 1s Processor Motherboard Chipset Memory Disk Graphics Network OS Kernel Desktop Display Server Vulkan Compiler File-System Screen Resolution a b c d e Intel Xeon Platinum 8490H @ 3.50GHz (60 Cores / 120 Threads) Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) Intel Device 1bce 512GB 3 x 3841GB Micron_9300_MTFDHAL3T8TDP ASPEED 4 x Intel E810-C for QSFP Ubuntu 22.04 5.15.0-47-generic (x86_64) GNOME Shell 42.4 X Server 1.21.1.3 1.2.204 GCC 11.2.0 ext4 1024x768 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,brig,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-targets=nvptx-none=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-gBFGDP/gcc-11-11.2.0/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: 0x2b0000c0 Java Details - OpenJDK Runtime Environment (build 11.0.16+8-post-Ubuntu-0ubuntu122.04) Python Details - Python 3.10.6 Security Details - itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling + srbds: Not affected + tsx_async_abort: Not affected
8490h 1s deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - 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 - Synchronous Single-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Synchronous Single-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream cryptopp: All Algorithms cryptopp: Keyed Algorithms cryptopp: Unkeyed Algorithms cassandra: Writes dragonflydb: 10 - 1:5 dragonflydb: 10 - 1:10 dragonflydb: 10 - 1:100 memtier-benchmark: Redis - 50 - 1:1 memtier-benchmark: Redis - 50 - 1:5 memtier-benchmark: Redis - 100 - 1:1 memtier-benchmark: Redis - 100 - 1:5 memtier-benchmark: Redis - 50 - 1:10 memtier-benchmark: Redis - 100 - 1:10 brl-cad: VGR Performance Metric deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - 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 - Synchronous Single-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Synchronous Single-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream blender: BMW27 - CPU-Only blender: Classroom - CPU-Only blender: Fishy Cat - CPU-Only blender: Barbershop - CPU-Only blender: Pabellon Barcelona - CPU-Only a b c d e 56.1193 35.4911 1905.9808 208.9561 648.3175 180.141 192.5033 86.93 780.4996 272.7991 5672.7993 759.4847 346.9388 184.3867 62.575 21.1647 780.3982 267.9943 348.5304 183.6332 475.7181 121.8864 75.6034 39.9427 868.4975 117.2266 218.1115 61.6472 57.076 35.764 1663.920955 595.365201 452.343734 134694 14247982.45 14662941.42 14338166.15 2462536.66 2613416.65 2503378.33 2627400.98 2646929.55 2929613.17 825917 534.5014 28.1702 15.703 4.7834 46.2475 5.5454 155.8013 11.4928 38.4127 3.6608 5.268 1.3144 86.4291 5.4182 479.125 47.2403 38.3944 3.7271 86.0393 5.4431 63.0065 8.1989 396.734 25.0015 34.4904 8.5248 137.5084 16.2145 525.1385 27.9552 25.74 68.93 35.75 272.91 88.73 57.7892 35.3613 1891.0565 209.1021 644.0495 179.6816 191.9935 87.1582 782.0400 270.2295 5678.5152 746.7073 347.6552 185.7453 61.5956 21.1611 781.3024 270.8536 348.7206 183.9268 487.4646 123.6781 75.8818 39.9583 869.4702 114.6147 224.1648 61.8913 57.5702 35.7199 134932 14476834.10 14292262.52 14432034.21 2470315.91 2546591.86 2630752.03 2710324.97 2544897.58 2583878.29 823602 517.1634 28.2735 15.8316 4.7802 46.5440 5.5592 156.2109 11.4653 38.3371 3.6961 5.2627 1.3371 86.2508 5.3786 486.1851 47.2504 38.3727 3.6871 85.9332 5.4340 61.5102 8.0804 395.2099 24.9917 34.4634 8.7193 133.779 16.1504 520.8695 27.9897 25.63 69.16 35.26 273.14 88.5 57.9253 35.6223 1891.327 208.4283 649.2977 178.7189 191.6104 86.786 780.5294 268.146 5677.035 758.3359 348.0117 184.1966 61.4143 21.0871 780.3312 271.6229 347.5917 183.819 487.1764 124.6224 75.613 39.9318 870.7567 116.3483 220.4888 61.6088 57.9918 35.8555 121708 14235868.61 14204640.25 14307949.03 2377430.14 2516320.69 2460387.75 2554516.65 2508415.59 2611551.59 820410 517.8375 28.0659 15.8257 4.7956 46.1781 5.5886 156.4615 11.5118 38.4104 3.7248 5.2632 1.3167 86.1641 5.4239 488.2455 47.4151 38.4054 3.677 86.2759 5.4372 61.546 8.019 396.6828 25.0071 34.4193 8.5903 135.9984 16.2241 516.0418 27.884 25.69 70.03 35.03 272.64 88.83 58.2508 35.5925 1890.5705 209.6555 648.3864 180.4386 190.8181 87.3401 780.3141 270.3481 5665.4699 759.4253 346.5606 184.9319 62.1748 21.0613 778.7629 271.7797 347.5128 184.047 485.7154 123.4543 75.5523 39.8698 871.2052 117.2235 229.4472 61.8283 58.2129 35.7569 140934 14750102.52 14205235.71 14571297.77 2383147.07 2452685.99 2493158.97 2549066.22 2487096.51 2593991.1 812806 511.4186 28.0901 15.838 4.7675 46.2436 5.5358 157.1756 11.4418 38.422 3.6938 5.2743 1.3146 86.5253 5.402 482.4354 47.4732 38.4911 3.6743 86.2939 5.4307 61.6935 8.0945 396.9936 25.0454 34.4017 8.5255 130.5153 16.1666 514.8982 27.9607 25.75 69.16 35.16 272.41 88.18 57.2121 35.0944 1905.1491 205.2428 633.4455 177.0105 191.4971 85.159 783.198 266.3444 5679.7805 709.1549 347.7604 181.1993 61.0696 20.736 782.5439 264.3062 348.1392 181.1289 488.2494 120.4409 75.7124 39.7368 870.6664 113.2419 217.7184 61.8458 57.5236 35.8816 137849 14392511.79 14390358.99 14492478.36 2414601.07 2444450.11 2474272.95 2541163.68 2540848.35 2755166.14 822977 520.2239 28.488 15.7168 4.8699 47.3169 5.642 156.5562 11.7345 38.2796 3.7499 5.2615 1.4076 86.1663 5.5134 488.2347 48.2186 38.312 3.7788 86.1261 5.5183 61.4194 8.2973 396.1651 25.132 34.4226 8.8247 137.7614 16.1614 519.2686 27.8637 25.71 69.25 35.5 272.44 88.09 OpenBenchmarking.org
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 1.5 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream e d c b a 13 26 39 52 65 SE +/- 0.18, N = 3 57.21 58.25 57.93 57.79 56.12
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 1.5 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream e d c b a 8 16 24 32 40 SE +/- 0.07, N = 3 35.09 35.59 35.62 35.36 35.49
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 1.5 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream e d c b a 400 800 1200 1600 2000 SE +/- 1.24, N = 3 1905.15 1890.57 1891.33 1891.06 1905.98
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 1.5 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream e d c b a 50 100 150 200 250 SE +/- 0.71, N = 3 205.24 209.66 208.43 209.10 208.96
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream e d c b a 140 280 420 560 700 SE +/- 5.86, N = 3 633.45 648.39 649.30 644.05 648.32
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream e d c b a 40 80 120 160 200 SE +/- 0.21, N = 3 177.01 180.44 178.72 179.68 180.14
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream e d c b a 40 80 120 160 200 SE +/- 0.10, N = 3 191.50 190.82 191.61 191.99 192.50
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream e d c b a 20 40 60 80 100 SE +/- 0.09, N = 3 85.16 87.34 86.79 87.16 86.93
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream e d c b a 200 400 600 800 1000 SE +/- 0.80, N = 3 783.20 780.31 780.53 782.04 780.50
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream e d c b a 60 120 180 240 300 SE +/- 0.64, N = 3 266.34 270.35 268.15 270.23 272.80
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream e d c b a 1200 2400 3600 4800 6000 SE +/- 3.30, N = 3 5679.78 5665.47 5677.04 5678.52 5672.80
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream e d c b a 160 320 480 640 800 SE +/- 0.65, N = 3 709.15 759.43 758.34 746.71 759.48
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream e d c b a 80 160 240 320 400 SE +/- 0.33, N = 3 347.76 346.56 348.01 347.66 346.94
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream e d c b a 40 80 120 160 200 SE +/- 0.69, N = 3 181.20 184.93 184.20 185.75 184.39
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream e d c b a 14 28 42 56 70 SE +/- 0.44, N = 3 61.07 62.17 61.41 61.60 62.58
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream e d c b a 5 10 15 20 25 SE +/- 0.07, N = 3 20.74 21.06 21.09 21.16 21.16
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream e d c b a 200 400 600 800 1000 SE +/- 0.42, N = 3 782.54 778.76 780.33 781.30 780.40
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream e d c b a 60 120 180 240 300 SE +/- 0.45, N = 3 264.31 271.78 271.62 270.85 267.99
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream e d c b a 80 160 240 320 400 SE +/- 0.57, N = 3 348.14 347.51 347.59 348.72 348.53
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream e d c b a 40 80 120 160 200 SE +/- 0.31, N = 3 181.13 184.05 183.82 183.93 183.63
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream e d c b a 110 220 330 440 550 SE +/- 0.85, N = 3 488.25 485.72 487.18 487.46 475.72
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream e d c b a 30 60 90 120 150 SE +/- 0.43, N = 3 120.44 123.45 124.62 123.68 121.89
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream e d c b a 20 40 60 80 100 SE +/- 0.23, N = 3 75.71 75.55 75.61 75.88 75.60
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream e d c b a 9 18 27 36 45 SE +/- 0.01, N = 3 39.74 39.87 39.93 39.96 39.94
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 1.5 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream e d c b a 200 400 600 800 1000 SE +/- 0.53, N = 3 870.67 871.21 870.76 869.47 868.50
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 1.5 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream e d c b a 30 60 90 120 150 113.24 117.22 116.35 114.61 117.23
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream e d c b a 50 100 150 200 250 217.72 229.45 220.49 224.16 218.11
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream e d c b a 14 28 42 56 70 61.85 61.83 61.61 61.89 61.65
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 1.5 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream e d c b a 13 26 39 52 65 57.52 58.21 57.99 57.57 57.08
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 1.5 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream e d c b a 8 16 24 32 40 35.88 35.76 35.86 35.72 35.76
Crypto++ Test: All Algorithms OpenBenchmarking.org MiB/second, More Is Better Crypto++ 8.8 Test: All Algorithms a 400 800 1200 1600 2000 1663.92 1. (CXX) g++ options: -g2 -O3 -fPIC -pthread -pipe
Crypto++ Test: Keyed Algorithms OpenBenchmarking.org MiB/second, More Is Better Crypto++ 8.8 Test: Keyed Algorithms a 130 260 390 520 650 595.37 1. (CXX) g++ options: -g2 -O3 -fPIC -pthread -pipe
Crypto++ Test: Unkeyed Algorithms OpenBenchmarking.org MiB/second, More Is Better Crypto++ 8.8 Test: Unkeyed Algorithms a 100 200 300 400 500 452.34 1. (CXX) g++ options: -g2 -O3 -fPIC -pthread -pipe
Apache Cassandra Test: Writes OpenBenchmarking.org Op/s, More Is Better Apache Cassandra 4.1.3 Test: Writes e d c b a 30K 60K 90K 120K 150K 137849 140934 121708 134932 134694
Dragonflydb Clients Per Thread: 10 - Set To Get Ratio: 1:5 OpenBenchmarking.org Ops/sec, More Is Better Dragonflydb 1.6.2 Clients Per Thread: 10 - Set To Get Ratio: 1:5 e d c b a 3M 6M 9M 12M 15M SE +/- 71046.49, N = 3 14392511.79 14750102.52 14235868.61 14476834.10 14247982.45 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Dragonflydb Clients Per Thread: 10 - Set To Get Ratio: 1:10 OpenBenchmarking.org Ops/sec, More Is Better Dragonflydb 1.6.2 Clients Per Thread: 10 - Set To Get Ratio: 1:10 e d c b a 3M 6M 9M 12M 15M SE +/- 31999.75, N = 3 14390358.99 14205235.71 14204640.25 14292262.52 14662941.42 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Dragonflydb Clients Per Thread: 10 - Set To Get Ratio: 1:100 OpenBenchmarking.org Ops/sec, More Is Better Dragonflydb 1.6.2 Clients Per Thread: 10 - Set To Get Ratio: 1:100 e d c b a 3M 6M 9M 12M 15M SE +/- 145332.09, N = 3 14492478.36 14571297.77 14307949.03 14432034.21 14338166.15 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Redis 7.0.12 + memtier_benchmark Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:1 OpenBenchmarking.org Ops/sec, More Is Better Redis 7.0.12 + memtier_benchmark 2.0 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:1 e d c b a 500K 1000K 1500K 2000K 2500K 2414601.07 2383147.07 2377430.14 2470315.91 2462536.66 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Redis 7.0.12 + memtier_benchmark Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5 OpenBenchmarking.org Ops/sec, More Is Better Redis 7.0.12 + memtier_benchmark 2.0 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5 e d c b a 600K 1200K 1800K 2400K 3000K 2444450.11 2452685.99 2516320.69 2546591.86 2613416.65 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Redis 7.0.12 + memtier_benchmark Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:1 OpenBenchmarking.org Ops/sec, More Is Better Redis 7.0.12 + memtier_benchmark 2.0 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:1 e d c b a 600K 1200K 1800K 2400K 3000K 2474272.95 2493158.97 2460387.75 2630752.03 2503378.33 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Redis 7.0.12 + memtier_benchmark Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5 OpenBenchmarking.org Ops/sec, More Is Better Redis 7.0.12 + memtier_benchmark 2.0 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5 e d c b a 600K 1200K 1800K 2400K 3000K 2541163.68 2549066.22 2554516.65 2710324.97 2627400.98 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Redis 7.0.12 + memtier_benchmark Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10 OpenBenchmarking.org Ops/sec, More Is Better Redis 7.0.12 + memtier_benchmark 2.0 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10 e d c b a 600K 1200K 1800K 2400K 3000K 2540848.35 2487096.51 2508415.59 2544897.58 2646929.55 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Redis 7.0.12 + memtier_benchmark Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10 OpenBenchmarking.org Ops/sec, More Is Better Redis 7.0.12 + memtier_benchmark 2.0 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10 e d c b a 600K 1200K 1800K 2400K 3000K 2755166.14 2593991.10 2611551.59 2583878.29 2929613.17 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
BRL-CAD VGR Performance Metric OpenBenchmarking.org VGR Performance Metric, More Is Better BRL-CAD 7.36 VGR Performance Metric e d c b a 200K 400K 600K 800K 1000K 822977 812806 820410 823602 825917 1. (CXX) g++ options: -std=c++14 -pipe -fvisibility=hidden -fno-strict-aliasing -fno-common -fexceptions -ftemplate-depth-128 -m64 -ggdb3 -O3 -fipa-pta -fstrength-reduce -finline-functions -flto -ltcl8.6 -lregex_brl -lz_brl -lnetpbm -ldl -lm -ltk8.6
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 1.5 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream e d c b a 120 240 360 480 600 SE +/- 1.23, N = 3 520.22 511.42 517.84 517.16 534.50
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 1.5 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream e d c b a 7 14 21 28 35 SE +/- 0.05, N = 3 28.49 28.09 28.07 28.27 28.17
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 1.5 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream e d c b a 4 8 12 16 20 SE +/- 0.01, N = 3 15.72 15.84 15.83 15.83 15.70
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 1.5 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream e d c b a 1.0957 2.1914 3.2871 4.3828 5.4785 SE +/- 0.0162, N = 3 4.8699 4.7675 4.7956 4.7802 4.7834
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream e d c b a 11 22 33 44 55 SE +/- 0.41, N = 3 47.32 46.24 46.18 46.54 46.25
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream e d c b a 1.2695 2.539 3.8085 5.078 6.3475 SE +/- 0.0064, N = 3 5.6420 5.5358 5.5886 5.5592 5.5454
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream e d c b a 30 60 90 120 150 SE +/- 0.08, N = 3 156.56 157.18 156.46 156.21 155.80
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream e d c b a 3 6 9 12 15 SE +/- 0.01, N = 3 11.73 11.44 11.51 11.47 11.49
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream e d c b a 9 18 27 36 45 SE +/- 0.04, N = 3 38.28 38.42 38.41 38.34 38.41
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream e d c b a 0.8437 1.6874 2.5311 3.3748 4.2185 SE +/- 0.0089, N = 3 3.7499 3.6938 3.7248 3.6961 3.6608
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream e d c b a 1.1867 2.3734 3.5601 4.7468 5.9335 SE +/- 0.0032, N = 3 5.2615 5.2743 5.2632 5.2627 5.2680
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream e d c b a 0.3167 0.6334 0.9501 1.2668 1.5835 SE +/- 0.0011, N = 3 1.4076 1.3146 1.3167 1.3371 1.3144
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream e d c b a 20 40 60 80 100 SE +/- 0.08, N = 3 86.17 86.53 86.16 86.25 86.43
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream e d c b a 1.2405 2.481 3.7215 4.962 6.2025 SE +/- 0.0199, N = 3 5.5134 5.4020 5.4239 5.3786 5.4182
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream e d c b a 110 220 330 440 550 SE +/- 2.68, N = 3 488.23 482.44 488.25 486.19 479.13
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream e d c b a 11 22 33 44 55 SE +/- 0.16, N = 3 48.22 47.47 47.42 47.25 47.24
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream e d c b a 9 18 27 36 45 SE +/- 0.02, N = 3 38.31 38.49 38.41 38.37 38.39
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream e d c b a 0.8502 1.7004 2.5506 3.4008 4.251 SE +/- 0.0060, N = 3 3.7788 3.6743 3.6770 3.6871 3.7271
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream e d c b a 20 40 60 80 100 SE +/- 0.14, N = 3 86.13 86.29 86.28 85.93 86.04
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream e d c b a 1.2416 2.4832 3.7248 4.9664 6.208 SE +/- 0.0092, N = 3 5.5183 5.4307 5.4372 5.4340 5.4431
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream e d c b a 14 28 42 56 70 SE +/- 0.11, N = 3 61.42 61.69 61.55 61.51 63.01
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream e d c b a 2 4 6 8 10 SE +/- 0.0280, N = 3 8.2973 8.0945 8.0190 8.0804 8.1989
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream e d c b a 90 180 270 360 450 SE +/- 1.22, N = 3 396.17 396.99 396.68 395.21 396.73
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream e d c b a 6 12 18 24 30 SE +/- 0.00, N = 3 25.13 25.05 25.01 24.99 25.00
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 1.5 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream e d c b a 8 16 24 32 40 SE +/- 0.02, N = 3 34.42 34.40 34.42 34.46 34.49
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 1.5 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream e d c b a 2 4 6 8 10 8.8247 8.5255 8.5903 8.7193 8.5248
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream e d c b a 30 60 90 120 150 137.76 130.52 136.00 133.78 137.51
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream e d c b a 4 8 12 16 20 16.16 16.17 16.22 16.15 16.21
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 1.5 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream e d c b a 110 220 330 440 550 519.27 514.90 516.04 520.87 525.14
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 1.5 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream e d c b a 7 14 21 28 35 27.86 27.96 27.88 27.99 27.96
Blender Blend File: BMW27 - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 3.6 Blend File: BMW27 - Compute: CPU-Only e d c b a 6 12 18 24 30 25.71 25.75 25.69 25.63 25.74
Blender Blend File: Classroom - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 3.6 Blend File: Classroom - Compute: CPU-Only e d c b a 16 32 48 64 80 69.25 69.16 70.03 69.16 68.93
Blender Blend File: Fishy Cat - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 3.6 Blend File: Fishy Cat - Compute: CPU-Only e d c b a 8 16 24 32 40 35.50 35.16 35.03 35.26 35.75
Blender Blend File: Barbershop - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 3.6 Blend File: Barbershop - Compute: CPU-Only e d c b a 60 120 180 240 300 272.44 272.41 272.64 273.14 272.91
Blender Blend File: Pabellon Barcelona - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 3.6 Blend File: Pabellon Barcelona - Compute: CPU-Only e d c b a 20 40 60 80 100 88.09 88.18 88.83 88.50 88.73
Phoronix Test Suite v10.8.4