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