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&grw&rdt&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 cryptopp: All Algorithms cryptopp: Keyed Algorithms cryptopp: Unkeyed Algorithms 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 - 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 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 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 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: 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 Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-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: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - 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: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Stream deepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO - 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: 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: 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: 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 Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream 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, Sparse INT8 - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-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 - 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 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-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 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 cassandra: Writes a b c d e 1663.920955 595.365201 452.343734 825917 56.1193 534.5014 35.4911 28.1702 1905.9808 15.703 208.9561 4.7834 648.3175 46.2475 180.141 5.5454 192.5033 155.8013 86.93 11.4928 780.4996 38.4127 272.7991 3.6608 5672.7993 5.268 759.4847 1.3144 346.9388 86.4291 184.3867 5.4182 62.575 479.125 21.1647 47.2403 780.3982 38.3944 267.9943 3.7271 348.5304 86.0393 183.6332 5.4431 475.7181 63.0065 121.8864 8.1989 75.6034 396.734 39.9427 25.0015 868.4975 34.4904 117.2266 8.5248 218.1115 137.5084 61.6472 16.2145 57.076 525.1385 35.764 27.9552 25.74 68.93 35.75 272.91 88.73 14247982.45 14662941.42 14338166.15 2462536.66 2613416.65 2503378.33 2627400.98 2646929.55 2929613.17 134694 823602 57.7892 517.1634 35.3613 28.2735 1891.0565 15.8316 209.1021 4.7802 644.0495 46.5440 179.6816 5.5592 191.9935 156.2109 87.1582 11.4653 782.0400 38.3371 270.2295 3.6961 5678.5152 5.2627 746.7073 1.3371 347.6552 86.2508 185.7453 5.3786 61.5956 486.1851 21.1611 47.2504 781.3024 38.3727 270.8536 3.6871 348.7206 85.9332 183.9268 5.4340 487.4646 61.5102 123.6781 8.0804 75.8818 395.2099 39.9583 24.9917 869.4702 34.4634 114.6147 8.7193 224.1648 133.779 61.8913 16.1504 57.5702 520.8695 35.7199 27.9897 25.63 69.16 35.26 273.14 88.5 14476834.10 14292262.52 14432034.21 2470315.91 2546591.86 2630752.03 2710324.97 2544897.58 2583878.29 134932 820410 57.9253 517.8375 35.6223 28.0659 1891.327 15.8257 208.4283 4.7956 649.2977 46.1781 178.7189 5.5886 191.6104 156.4615 86.786 11.5118 780.5294 38.4104 268.146 3.7248 5677.035 5.2632 758.3359 1.3167 348.0117 86.1641 184.1966 5.4239 61.4143 488.2455 21.0871 47.4151 780.3312 38.4054 271.6229 3.677 347.5917 86.2759 183.819 5.4372 487.1764 61.546 124.6224 8.019 75.613 396.6828 39.9318 25.0071 870.7567 34.4193 116.3483 8.5903 220.4888 135.9984 61.6088 16.2241 57.9918 516.0418 35.8555 27.884 25.69 70.03 35.03 272.64 88.83 14235868.61 14204640.25 14307949.03 2377430.14 2516320.69 2460387.75 2554516.65 2508415.59 2611551.59 121708 812806 58.2508 511.4186 35.5925 28.0901 1890.5705 15.838 209.6555 4.7675 648.3864 46.2436 180.4386 5.5358 190.8181 157.1756 87.3401 11.4418 780.3141 38.422 270.3481 3.6938 5665.4699 5.2743 759.4253 1.3146 346.5606 86.5253 184.9319 5.402 62.1748 482.4354 21.0613 47.4732 778.7629 38.4911 271.7797 3.6743 347.5128 86.2939 184.047 5.4307 485.7154 61.6935 123.4543 8.0945 75.5523 396.9936 39.8698 25.0454 871.2052 34.4017 117.2235 8.5255 229.4472 130.5153 61.8283 16.1666 58.2129 514.8982 35.7569 27.9607 25.75 69.16 35.16 272.41 88.18 14750102.52 14205235.71 14571297.77 2383147.07 2452685.99 2493158.97 2549066.22 2487096.51 2593991.1 140934 822977 57.2121 520.2239 35.0944 28.488 1905.1491 15.7168 205.2428 4.8699 633.4455 47.3169 177.0105 5.642 191.4971 156.5562 85.159 11.7345 783.198 38.2796 266.3444 3.7499 5679.7805 5.2615 709.1549 1.4076 347.7604 86.1663 181.1993 5.5134 61.0696 488.2347 20.736 48.2186 782.5439 38.312 264.3062 3.7788 348.1392 86.1261 181.1289 5.5183 488.2494 61.4194 120.4409 8.2973 75.7124 396.1651 39.7368 25.132 870.6664 34.4226 113.2419 8.8247 217.7184 137.7614 61.8458 16.1614 57.5236 519.2686 35.8816 27.8637 25.71 69.25 35.5 272.44 88.09 14392511.79 14390358.99 14492478.36 2414601.07 2444450.11 2474272.95 2541163.68 2540848.35 2755166.14 137849 OpenBenchmarking.org
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
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 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: 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 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 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 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: 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 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 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 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: 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 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 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 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: 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 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: 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 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: 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 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, 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 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: 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 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: 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 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: 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 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: 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 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: 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 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: 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 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: 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 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 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 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: 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 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: 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 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: 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 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: 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 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: 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 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: 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 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: 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 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: 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 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: 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 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 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 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: 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 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
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
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
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
Phoronix Test Suite v10.8.4