7763 2204

AMD EPYC 7763 64-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite.

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a
August 04 2023
  6 Hours, 9 Minutes
b
August 04 2023
  4 Hours, 37 Minutes
c
August 05 2023
  4 Hours, 37 Minutes
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a

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --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-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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 Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)
Python Notes: Python 3.10.6
Security Notes: 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 + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

b

c

Processor: AMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 800GB INTEL SSDPF21Q800GB, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710

OS: Ubuntu 22.04, Kernel: 6.2.0-phx (x86_64), Desktop: GNOME Shell 42.5, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 11.3.0 + LLVM 14.0.0, File-System: ext4, Screen Resolution: 1920x1080

7763 2204OpenBenchmarking.orgPhoronix Test SuiteAMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads)AMD DAYTONA_X (RYM1009B BIOS)AMD Starship/Matisse256GB800GB INTEL SSDPF21Q800GBASPEEDVE2282 x Mellanox MT27710Ubuntu 22.046.2.0-phx (x86_64)GNOME Shell 42.5X Server 1.21.1.31.3.224GCC 11.3.0 + LLVM 14.0.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsMonitorNetworkOSKernelDesktopDisplay ServerVulkanCompilerFile-SystemScreen Resolution7763 2204 BenchmarksSystem Logs- Transparent Huge Pages: madvise- --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-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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 - Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173 - OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)- Python 3.10.6- 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 + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcResult OverviewPhoronix Test Suite100%101%103%104%105%NCNNsrsRAN ProjectApache CassandraBRL-CADVVenCApache IoTDBBlenderNeural Magic DeepSparseTimed GCC Compilation

7763 2204brl-cad: VGR Performance Metricdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Synchronous Single-Streamdeepsparse: ResNet-50, Baseline - Synchronous Single-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Streamdeepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Streamncnn: CPU - mobilenetncnn: CPU-v2-v2 - mobilenet-v2ncnn: CPU-v3-v3 - mobilenet-v3ncnn: CPU - shufflenet-v2ncnn: CPU - mnasnetncnn: CPU - efficientnet-b0ncnn: CPU - blazefacencnn: CPU - googlenetncnn: CPU - vgg16ncnn: CPU - resnet18ncnn: CPU - alexnetncnn: CPU - resnet50ncnn: CPU - yolov4-tinyncnn: CPU - squeezenet_ssdncnn: CPU - regnety_400mncnn: CPU - vision_transformerncnn: CPU - FastestDetbuild-gcc: Time To Compileblender: BMW27 - CPU-Onlyblender: Classroom - CPU-Onlyblender: Fishy Cat - CPU-Onlyblender: Barbershop - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlyvvenc: Bosphorus 4K - Fastvvenc: Bosphorus 4K - Fastervvenc: Bosphorus 1080p - Fastvvenc: Bosphorus 1080p - Fastersrsran: Downlink Processor Benchmarksrsran: PUSCH Processor Benchmark, Throughput Totalsrsran: PUSCH Processor Benchmark, Throughput Threadcouchdb: 100 - 1000 - 30couchdb: 100 - 3000 - 30couchdb: 300 - 1000 - 30couchdb: 300 - 3000 - 30couchdb: 500 - 1000 - 30couchdb: 500 - 3000 - 30apache-iotdb: 100 - 1 - 200apache-iotdb: 100 - 1 - 200apache-iotdb: 100 - 1 - 500apache-iotdb: 100 - 1 - 500apache-iotdb: 200 - 1 - 200apache-iotdb: 200 - 1 - 200apache-iotdb: 200 - 1 - 500apache-iotdb: 200 - 1 - 500apache-iotdb: 500 - 1 - 200apache-iotdb: 500 - 1 - 200apache-iotdb: 500 - 1 - 500apache-iotdb: 500 - 1 - 500apache-iotdb: 100 - 100 - 200apache-iotdb: 100 - 100 - 200apache-iotdb: 100 - 100 - 500apache-iotdb: 100 - 100 - 500apache-iotdb: 200 - 100 - 200apache-iotdb: 200 - 100 - 200apache-iotdb: 200 - 100 - 500apache-iotdb: 200 - 100 - 500apache-iotdb: 500 - 100 - 200apache-iotdb: 500 - 100 - 200apache-iotdb: 500 - 100 - 500apache-iotdb: 500 - 100 - 500cassandra: Writesabc73438637.6144840.541619.946950.12581105.379128.9150173.94625.747489.770965.275086.610411.5378143.0725223.471539.421925.356468.138368.2852159.84926.25253814.51948.3665723.71491.3784225.3104141.7036119.80418.342346.7204681.275624.442240.9048467.984868.3188159.96606.2481227.4188140.3979120.36458.3056326.312397.871197.356110.264553.4831597.967928.631034.9104574.955055.581794.509710.5770165.9980192.354653.784518.585837.6109841.477920.072049.813214.116.357.009.096.099.983.9714.6223.848.505.2315.4920.6614.1735.2448.7910.251020.13327.2768.8033.70253.4984.555.99110.64616.08329.352657.79682.1211.1101.578346.085169.505572.125339.9672390.933644019.7217.451038515.6234.36898967.0815.241232509.1933.51182440.6213.541636128.7327.139287432.9236.0451316464.4481.246437377.6735.0942048733.22109.3851341708.8535.0556935634.5581.8123665072987637.5315840.949320.027949.92261104.037728.9451172.75055.7863486.172865.735986.390511.5666143.2058223.367039.563125.2659467.969668.2763159.74816.25663824.30458.3441732.11011.3623225.4592141.6185119.88188.336846.5739679.824024.565540.6993468.108768.2716159.92386.2495227.6423140.2607120.17628.3182326.543997.843497.612110.238053.5531596.809128.632934.9082575.285955.560194.044510.6299166.2231192.164553.702318.614437.5352840.257320.031549.914413.976.266.557.935.99.783.4314.5323.648.425.2215.3420.5114.1127.5448.499.041020.84627.568.5033.76253.7784.355.99310.81516.09029.39658.19718.6208.2648308.2717.281069145.7932.92978176.7613.61226219.8833.861365831.511.631686943.162638401769.1736.8550507747.1282.2647245476.7833.8441987111.39110.8850045888.9836.159505306.5579.8323816173043437.5814840.123420.024549.93121103.355228.9644172.06595.8092482.127466.283786.321911.5764143.5714222.817539.511725.2989468.329368.2503160.59326.22383823.08338.3468731.50991.3634225.8007141.4775119.97908.330046.9021679.811824.528440.7606467.639668.3388160.69856.2195227.5740140.3036120.58138.2903326.410897.869896.882210.314653.6166596.534328.641634.8978575.115855.583594.554810.5720166.058192.214853.95218.52837.5755840.435020.053649.858014.036.176.347.605.869.753.4814.4723.918.515.2215.5420.8014.5927.5948.438.881020.21627.2468.7033.72253.4384.175.97610.81816.05529.469619.39727.1210.8667880.9616.351044153.4434.08870795.9216.071261385.8932.711367763.4911.831446487.731.439945212.9935.0152464142.8379.1646674344.6934.7943363203.76106.7349201448.8137.1256463717.5483.14234887OpenBenchmarking.org

BRL-CAD

BRL-CAD is a cross-platform, open-source solid modeling system with built-in benchmark mode. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgVGR Performance Metric, More Is BetterBRL-CAD 7.36VGR Performance Metriccba160K320K480K640K800KSE +/- 963.50, N = 2SE +/- 357.50, N = 2SE +/- 1805.50, N = 27304347298767343861. (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

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Streamcba918273645SE +/- 0.02, N = 2SE +/- 0.01, N = 2SE +/- 0.09, N = 237.5837.5337.61

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Streamcba2004006008001000SE +/- 0.59, N = 2SE +/- 0.16, N = 2SE +/- 1.02, N = 2840.12840.95840.54

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Streamcba510152025SE +/- 0.02, N = 2SE +/- 0.04, N = 2SE +/- 0.01, N = 220.0220.0319.95

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Streamcba1122334455SE +/- 0.05, N = 2SE +/- 0.11, N = 2SE +/- 0.02, N = 249.9349.9250.13

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Streamcba2004006008001000SE +/- 0.21, N = 2SE +/- 1.15, N = 2SE +/- 1.07, N = 21103.361104.041105.38

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Streamcba714212835SE +/- 0.01, N = 2SE +/- 0.03, N = 2SE +/- 0.03, N = 228.9628.9528.92

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Streamcba4080120160200SE +/- 0.80, N = 2SE +/- 0.63, N = 2SE +/- 0.98, N = 2172.07172.75173.95

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Streamcba1.30712.61423.92135.22846.5355SE +/- 0.0271, N = 2SE +/- 0.0212, N = 2SE +/- 0.0325, N = 25.80925.78635.7470

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Streamcba110220330440550SE +/- 7.21, N = 2SE +/- 1.03, N = 2SE +/- 0.68, N = 2482.13486.17489.77

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Streamcba1530456075SE +/- 0.99, N = 2SE +/- 0.10, N = 2SE +/- 0.10, N = 266.2865.7465.28

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Streamcba20406080100SE +/- 0.42, N = 2SE +/- 0.08, N = 2SE +/- 0.59, N = 286.3286.3986.61

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Streamcba3691215SE +/- 0.06, N = 2SE +/- 0.01, N = 2SE +/- 0.08, N = 211.5811.5711.54

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Streamcba306090120150SE +/- 0.03, N = 2SE +/- 0.18, N = 2SE +/- 0.01, N = 2143.57143.21143.07

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Streamcba50100150200250SE +/- 0.05, N = 2SE +/- 0.26, N = 2SE +/- 0.03, N = 2222.82223.37223.47

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Streamcba918273645SE +/- 0.01, N = 2SE +/- 0.11, N = 2SE +/- 0.06, N = 239.5139.5639.42

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Streamcba612182430SE +/- 0.01, N = 2SE +/- 0.07, N = 2SE +/- 0.04, N = 225.3025.2725.36

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Streamcba100200300400500SE +/- 0.09, N = 2SE +/- 0.62, N = 2SE +/- 0.19, N = 2468.33467.97468.14

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Streamcba1530456075SE +/- 0.01, N = 2SE +/- 0.04, N = 2SE +/- 0.04, N = 268.2568.2868.29

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Synchronous Single-Streamcba4080120160200SE +/- 0.80, N = 2SE +/- 0.22, N = 2SE +/- 0.43, N = 2160.59159.75159.85

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Synchronous Single-Streamcba246810SE +/- 0.0308, N = 2SE +/- 0.0087, N = 2SE +/- 0.0166, N = 26.22386.25666.2525

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Streamcba8001600240032004000SE +/- 17.23, N = 2SE +/- 10.54, N = 2SE +/- 0.54, N = 23823.083824.303814.52

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Streamcba246810SE +/- 0.0366, N = 2SE +/- 0.0212, N = 2SE +/- 0.0013, N = 28.34688.34418.3665

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Streamcba160320480640800SE +/- 0.48, N = 2SE +/- 2.91, N = 2SE +/- 10.71, N = 2731.51732.11723.71

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Streamcba0.31010.62020.93031.24041.5505SE +/- 0.0010, N = 2SE +/- 0.0055, N = 2SE +/- 0.0205, N = 21.36341.36231.3784

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Streamcba50100150200250SE +/- 0.22, N = 2SE +/- 0.10, N = 2SE +/- 0.21, N = 2225.80225.46225.31

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Streamcba306090120150SE +/- 0.05, N = 2SE +/- 0.09, N = 2SE +/- 0.13, N = 2141.48141.62141.70

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Streamcba306090120150SE +/- 0.01, N = 2SE +/- 0.02, N = 2SE +/- 0.08, N = 2119.98119.88119.80

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Streamcba246810SE +/- 0.0003, N = 2SE +/- 0.0012, N = 2SE +/- 0.0056, N = 28.33008.33688.3423

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Streamcba1122334455SE +/- 0.02, N = 2SE +/- 0.03, N = 2SE +/- 0.10, N = 246.9046.5746.72

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Streamcba150300450600750SE +/- 0.34, N = 2SE +/- 0.17, N = 2SE +/- 0.37, N = 2679.81679.82681.28

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Streamcba612182430SE +/- 0.00, N = 2SE +/- 0.01, N = 2SE +/- 0.01, N = 224.5324.5724.44

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Streamcba918273645SE +/- 0.01, N = 2SE +/- 0.01, N = 2SE +/- 0.01, N = 240.7640.7040.90

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Streamcba100200300400500SE +/- 0.26, N = 2SE +/- 1.23, N = 2SE +/- 0.36, N = 2467.64468.11467.98

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Streamcba1530456075SE +/- 0.01, N = 2SE +/- 0.13, N = 2SE +/- 0.04, N = 268.3468.2768.32

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Streamcba4080120160200SE +/- 0.62, N = 2SE +/- 0.32, N = 2SE +/- 0.24, N = 2160.70159.92159.97

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Streamcba246810SE +/- 0.0244, N = 2SE +/- 0.0121, N = 2SE +/- 0.0097, N = 26.21956.24956.2481

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Streamcba50100150200250SE +/- 0.22, N = 2SE +/- 0.19, N = 2SE +/- 0.11, N = 2227.57227.64227.42

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Streamcba306090120150SE +/- 0.23, N = 2SE +/- 0.12, N = 2SE +/- 0.07, N = 2140.30140.26140.40

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Streamcba306090120150SE +/- 0.08, N = 2SE +/- 0.02, N = 2SE +/- 0.19, N = 2120.58120.18120.36

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Streamcba246810SE +/- 0.0053, N = 2SE +/- 0.0014, N = 2SE +/- 0.0127, N = 28.29038.31828.3056

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Streamcba70140210280350SE +/- 0.49, N = 2SE +/- 0.48, N = 2SE +/- 0.12, N = 2326.41326.54326.31

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Streamcba20406080100SE +/- 0.16, N = 2SE +/- 0.15, N = 2SE +/- 0.00, N = 297.8797.8497.87

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Streamcba20406080100SE +/- 0.08, N = 2SE +/- 0.17, N = 2SE +/- 0.04, N = 296.8897.6197.36

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Streamcba3691215SE +/- 0.01, N = 2SE +/- 0.02, N = 2SE +/- 0.00, N = 210.3110.2410.26

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Streamcba1224364860SE +/- 0.00, N = 2SE +/- 0.05, N = 2SE +/- 0.00, N = 253.6253.5553.48

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Streamcba130260390520650SE +/- 0.01, N = 2SE +/- 0.12, N = 2SE +/- 0.05, N = 2596.53596.81597.97

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Streamcba714212835SE +/- 0.02, N = 2SE +/- 0.04, N = 2SE +/- 0.03, N = 228.6428.6328.63

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Streamcba816243240SE +/- 0.03, N = 2SE +/- 0.05, N = 2SE +/- 0.03, N = 234.9034.9134.91

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Streamcba120240360480600SE +/- 0.31, N = 2SE +/- 0.68, N = 2SE +/- 0.03, N = 2575.12575.29574.96

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Streamcba1224364860SE +/- 0.04, N = 2SE +/- 0.06, N = 2SE +/- 0.03, N = 255.5855.5655.58

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Streamcba20406080100SE +/- 0.18, N = 2SE +/- 0.64, N = 2SE +/- 0.05, N = 294.5594.0494.51

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Streamcba3691215SE +/- 0.02, N = 2SE +/- 0.07, N = 2SE +/- 0.01, N = 210.5710.6310.58

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Streamcba4080120160200SE +/- 0.08, N = 2SE +/- 0.06, N = 2SE +/- 0.05, N = 2166.06166.22166.00

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Streamcba4080120160200SE +/- 0.21, N = 2SE +/- 0.00, N = 2SE +/- 0.06, N = 2192.21192.16192.35

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Streamcba1224364860SE +/- 0.10, N = 2SE +/- 0.01, N = 2SE +/- 0.08, N = 253.9553.7053.78

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Streamcba510152025SE +/- 0.03, N = 2SE +/- 0.00, N = 2SE +/- 0.03, N = 218.5318.6118.59

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Streamcba918273645SE +/- 0.02, N = 2SE +/- 0.01, N = 2SE +/- 0.04, N = 237.5837.5437.61

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Streamcba2004006008001000SE +/- 0.44, N = 2SE +/- 0.01, N = 2SE +/- 0.49, N = 2840.44840.26841.48

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Streamcba510152025SE +/- 0.02, N = 2SE +/- 0.00, N = 2SE +/- 0.02, N = 220.0520.0320.07

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Streamcba1122334455SE +/- 0.06, N = 2SE +/- 0.00, N = 2SE +/- 0.05, N = 249.8649.9149.81

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mobilenetcba48121620SE +/- 0.09, N = 2SE +/- 0.03, N = 2SE +/- 0.04, N = 214.0313.9714.11MIN: 13.68 / MAX: 18.14MIN: 13.64 / MAX: 19.68MIN: 13.76 / MAX: 19.751. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v2-v2 - Model: mobilenet-v2cba246810SE +/- 0.07, N = 2SE +/- 0.00, N = 2SE +/- 0.01, N = 26.176.266.35MIN: 6.02 / MAX: 6.83MIN: 6.11 / MAX: 12.76MIN: 6.19 / MAX: 12.451. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v3-v3 - Model: mobilenet-v3cba246810SE +/- 0.04, N = 2SE +/- 0.18, N = 2SE +/- 0.55, N = 26.346.557.00MIN: 6.15 / MAX: 11.57MIN: 6.24 / MAX: 7.61MIN: 6.3 / MAX: 10.21. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: shufflenet-v2cba3691215SE +/- 0.04, N = 2SE +/- 0.31, N = 2SE +/- 1.23, N = 27.607.939.09MIN: 7.44 / MAX: 11.6MIN: 7.51 / MAX: 11.45MIN: 7.74 / MAX: 15.921. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mnasnetcba246810SE +/- 0.05, N = 2SE +/- 0.00, N = 2SE +/- 0.11, N = 25.865.906.09MIN: 5.73 / MAX: 11.67MIN: 5.81 / MAX: 12.33MIN: 5.89 / MAX: 10.351. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: efficientnet-b0cba3691215SE +/- 0.04, N = 2SE +/- 0.01, N = 2SE +/- 0.03, N = 29.759.789.98MIN: 9.58 / MAX: 13.38MIN: 9.64 / MAX: 16.01MIN: 9.82 / MAX: 10.961. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: blazefacecba0.89331.78662.67993.57324.4665SE +/- 0.05, N = 2SE +/- 0.01, N = 2SE +/- 0.09, N = 23.483.433.97MIN: 3.32 / MAX: 8.58MIN: 3.35 / MAX: 3.83MIN: 3.5 / MAX: 7.611. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: googlenetcba48121620SE +/- 0.06, N = 2SE +/- 0.02, N = 2SE +/- 0.03, N = 214.4714.5314.62MIN: 14.26 / MAX: 20.57MIN: 14.31 / MAX: 24.11MIN: 14.46 / MAX: 25.511. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vgg16cba612182430SE +/- 0.08, N = 2SE +/- 0.06, N = 2SE +/- 0.05, N = 223.9123.6423.84MIN: 23.48 / MAX: 30.63MIN: 23.33 / MAX: 28.05MIN: 23.45 / MAX: 28.551. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet18cba246810SE +/- 0.03, N = 2SE +/- 0.04, N = 2SE +/- 0.03, N = 28.518.428.50MIN: 8.3 / MAX: 13.61MIN: 8.27 / MAX: 14.6MIN: 8.33 / MAX: 14.691. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: alexnetcba1.17682.35363.53044.70725.884SE +/- 0.02, N = 2SE +/- 0.02, N = 2SE +/- 0.01, N = 25.225.225.23MIN: 5.12 / MAX: 7.84MIN: 5.11 / MAX: 5.77MIN: 5.12 / MAX: 11.621. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet50cba48121620SE +/- 0.14, N = 2SE +/- 0.10, N = 2SE +/- 0.08, N = 215.5415.3415.49MIN: 15.15 / MAX: 27.2MIN: 15.07 / MAX: 21.69MIN: 15.24 / MAX: 21.821. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: yolov4-tinycba510152025SE +/- 0.13, N = 2SE +/- 0.08, N = 2SE +/- 0.02, N = 220.8020.5120.66MIN: 20.01 / MAX: 96.45MIN: 19.87 / MAX: 24.86MIN: 20.04 / MAX: 25.041. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: squeezenet_ssdcba48121620SE +/- 0.62, N = 2SE +/- 0.09, N = 2SE +/- 0.01, N = 214.5914.1114.17MIN: 13.37 / MAX: 277.61MIN: 13.32 / MAX: 18.63MIN: 13.53 / MAX: 18.451. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: regnety_400mcba816243240SE +/- 0.07, N = 2SE +/- 0.15, N = 2SE +/- 5.99, N = 227.5927.5435.24MIN: 26.64 / MAX: 33.41MIN: 26.96 / MAX: 33.56MIN: 27.86 / MAX: 47.91. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vision_transformercba1122334455SE +/- 0.30, N = 2SE +/- 0.04, N = 2SE +/- 0.07, N = 248.4348.4948.79MIN: 47.33 / MAX: 85.35MIN: 47.44 / MAX: 58.53MIN: 47.65 / MAX: 78.361. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: FastestDetcba3691215SE +/- 0.01, N = 2SE +/- 0.11, N = 2SE +/- 0.94, N = 28.889.0410.25MIN: 8.58 / MAX: 13.34MIN: 8.65 / MAX: 15.19MIN: 8.95 / MAX: 17.141. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

Timed GCC Compilation

This test times how long it takes to build the GNU Compiler Collection (GCC) open-source compiler. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed GCC Compilation 13.2Time To Compilecba2004006008001000SE +/- 0.66, N = 2SE +/- 0.03, N = 2SE +/- 1.81, N = 21020.221020.851020.13

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: BMW27 - Compute: CPU-Onlycba612182430SE +/- 0.04, N = 2SE +/- 0.15, N = 2SE +/- 0.06, N = 227.2427.5027.27

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: Classroom - Compute: CPU-Onlycba1530456075SE +/- 0.13, N = 2SE +/- 0.03, N = 2SE +/- 0.14, N = 268.7068.5068.80

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: Fishy Cat - Compute: CPU-Onlycba816243240SE +/- 0.01, N = 2SE +/- 0.25, N = 2SE +/- 0.02, N = 233.7233.7633.70

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: Barbershop - Compute: CPU-Onlycba60120180240300SE +/- 0.52, N = 2SE +/- 0.23, N = 2SE +/- 0.11, N = 2253.43253.77253.49

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: Pabellon Barcelona - Compute: CPU-Onlycba20406080100SE +/- 0.04, N = 2SE +/- 0.14, N = 2SE +/- 0.40, N = 284.1784.3584.55

VVenC

VVenC is the Fraunhofer Versatile Video Encoder as a fast/efficient H.266/VVC encoder. The vvenc encoder makes use of SIMD Everywhere (SIMDe). The vvenc software is published under the Clear BSD License. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 4K - Video Preset: Fastcba1.34842.69684.04525.39366.742SE +/- 0.002, N = 2SE +/- 0.006, N = 2SE +/- 0.001, N = 25.9765.9935.9911. (CXX) g++ options: -O3 -flto -fno-fat-lto-objects -flto=auto

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 4K - Video Preset: Fastercba3691215SE +/- 0.00, N = 2SE +/- 0.02, N = 2SE +/- 0.18, N = 210.8210.8210.651. (CXX) g++ options: -O3 -flto -fno-fat-lto-objects -flto=auto

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 1080p - Video Preset: Fastcba48121620SE +/- 0.02, N = 2SE +/- 0.02, N = 2SE +/- 0.02, N = 216.0616.0916.081. (CXX) g++ options: -O3 -flto -fno-fat-lto-objects -flto=auto

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 1080p - Video Preset: Fastercba714212835SE +/- 0.07, N = 2SE +/- 0.11, N = 2SE +/- 0.07, N = 229.4729.3929.351. (CXX) g++ options: -O3 -flto -fno-fat-lto-objects -flto=auto

srsRAN Project

srsRAN Project is a complete ORAN-native 5G RAN solution created by Software Radio Systems (SRS). The srsRAN Project radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMbps, More Is BettersrsRAN Project 23.5Test: Downlink Processor Benchmarkcba140280420560700SE +/- 0.35, N = 2SE +/- 27.75, N = 2SE +/- 17.85, N = 2619.3658.1657.71. (CXX) g++ options: -march=native -mfma -O3 -fno-trapping-math -fno-math-errno -lgtest

OpenBenchmarking.orgMbps, More Is BettersrsRAN Project 23.5Test: PUSCH Processor Benchmark, Throughput Totalcba2K4K6K8K10KSE +/- 56.45, N = 2SE +/- 44.35, N = 2SE +/- 13.30, N = 29727.19718.69682.11. (CXX) g++ options: -march=native -mfma -O3 -fno-trapping-math -fno-math-errno -lgtest

OpenBenchmarking.orgMbps, More Is BettersrsRAN Project 23.5Test: PUSCH Processor Benchmark, Throughput Threadcba50100150200250SE +/- 0.20, N = 2SE +/- 1.90, N = 2SE +/- 0.10, N = 2210.8208.2211.11. (CXX) g++ options: -march=native -mfma -O3 -fno-trapping-math -fno-math-errno -lgtest

Apache CouchDB

This is a bulk insertion benchmark of Apache CouchDB. CouchDB is a document-oriented NoSQL database implemented in Erlang. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 100 - Inserts: 1000 - Rounds: 30a20406080100SE +/- 0.50, N = 2101.581. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 100 - Inserts: 3000 - Rounds: 30a80160240320400SE +/- 0.25, N = 2346.091. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 300 - Inserts: 1000 - Rounds: 30a4080120160200SE +/- 0.64, N = 2169.511. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 300 - Inserts: 3000 - Rounds: 30a120240360480600SE +/- 0.52, N = 2572.131. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 500 - Inserts: 1000 - Rounds: 30a70140210280350SE +/- 8.78, N = 2339.971. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 500 - Inserts: 3000 - Rounds: 30a50010001500200025002390.931. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

Apache IoTDB

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200cba140K280K420K560K700K667880.96648308.27644019.72

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200cba4812162016.3517.2817.45MAX: 668.86MAX: 644.33MAX: 645.35

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500cba200K400K600K800K1000K1044153.441069145.791038515.62

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500cba81624324034.0832.9234.36MAX: 699.28MAX: 728.63MAX: 704.53

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200cba200K400K600K800K1000K870795.92978176.76898967.08

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200cba4812162016.0713.6015.24MAX: 592.48MAX: 586.94MAX: 583.94

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500cba300K600K900K1200K1500K1261385.891226219.881232509.19

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500cba81624324032.7133.8633.50MAX: 725.08MAX: 659.59MAX: 690.29

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200cba300K600K900K1200K1500K1367763.491365831.501182440.62

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200cba369121511.8311.6313.54MAX: 836.9MAX: 860.78MAX: 856.65

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500cba400K800K1200K1600K2000K1446487.701686943.161636128.73

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500cba71421283531.426.027.1MAX: 890.05MAX: 873.88MAX: 934.45

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200cba9M18M27M36M45M39945212.9938401769.1739287432.92

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200cba81624324035.0136.8536.04MAX: 746.4MAX: 721.27MAX: 804.01

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500cba11M22M33M44M55M52464142.8350507747.1251316464.44

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500cba2040608010079.1682.2681.20MAX: 1006.03MAX: 864.29MAX: 1009.28

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200cba10M20M30M40M50M46674344.6947245476.7846437377.67

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200cba81624324034.7933.8435.09MAX: 780.01MAX: 773.52MAX: 804.64

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500cba9M18M27M36M45M43363203.7641987111.3942048733.22

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500cba20406080100106.73110.88109.38MAX: 3485.91MAX: 3569.78MAX: 3597.09

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200cba11M22M33M44M55M49201448.8150045888.9851341708.85

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200cba91827364537.1236.1035.05MAX: 2182.81MAX: 1990.15MAX: 2157.23

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500cba13M26M39M52M65M56463717.5459505306.5556935634.55

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500cba2040608010083.1479.8381.81MAX: 2932.1MAX: 1607.86MAX: 3018.16

Apache Cassandra

This is a benchmark of the Apache Cassandra NoSQL database management system making use of cassandra-stress. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterApache Cassandra 4.1.3Test: Writescba50K100K150K200K250KSE +/- 669.00, N = 2SE +/- 817.50, N = 2SE +/- 633.50, N = 2234887238161236650

122 Results Shown

BRL-CAD
Neural Magic DeepSparse:
  NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Stream:
    items/sec
    ms/batch
  ResNet-50, Baseline - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  ResNet-50, Baseline - Synchronous Single-Stream:
    items/sec
    ms/batch
  ResNet-50, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  ResNet-50, Sparse INT8 - Synchronous Single-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO - Synchronous Single-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering - Synchronous Single-Stream:
    items/sec
    ms/batch
  CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream:
    items/sec
    ms/batch
  CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream:
    items/sec
    ms/batch
NCNN:
  CPU - mobilenet
  CPU-v2-v2 - mobilenet-v2
  CPU-v3-v3 - mobilenet-v3
  CPU - shufflenet-v2
  CPU - mnasnet
  CPU - efficientnet-b0
  CPU - blazeface
  CPU - googlenet
  CPU - vgg16
  CPU - resnet18
  CPU - alexnet
  CPU - resnet50
  CPU - yolov4-tiny
  CPU - squeezenet_ssd
  CPU - regnety_400m
  CPU - vision_transformer
  CPU - FastestDet
Timed GCC Compilation
Blender:
  BMW27 - CPU-Only
  Classroom - CPU-Only
  Fishy Cat - CPU-Only
  Barbershop - CPU-Only
  Pabellon Barcelona - CPU-Only
VVenC:
  Bosphorus 4K - Fast
  Bosphorus 4K - Faster
  Bosphorus 1080p - Fast
  Bosphorus 1080p - Faster
srsRAN Project:
  Downlink Processor Benchmark
  PUSCH Processor Benchmark, Throughput Total
  PUSCH Processor Benchmark, Throughput Thread
Apache CouchDB:
  100 - 1000 - 30
  100 - 3000 - 30
  300 - 1000 - 30
  300 - 3000 - 30
  500 - 1000 - 30
  500 - 3000 - 30
Apache IoTDB:
  100 - 1 - 200:
    point/sec
    Average Latency
  100 - 1 - 500:
    point/sec
    Average Latency
  200 - 1 - 200:
    point/sec
    Average Latency
  200 - 1 - 500:
    point/sec
    Average Latency
  500 - 1 - 200:
    point/sec
    Average Latency
  500 - 1 - 500:
    point/sec
    Average Latency
  100 - 100 - 200:
    point/sec
    Average Latency
  100 - 100 - 500:
    point/sec
    Average Latency
  200 - 100 - 200:
    point/sec
    Average Latency
  200 - 100 - 500:
    point/sec
    Average Latency
  500 - 100 - 200:
    point/sec
    Average Latency
  500 - 100 - 500:
    point/sec
    Average Latency
Apache Cassandra

a

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --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-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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 Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)
Python Notes: Python 3.10.6
Security Notes: 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 + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 4 August 2023 14:20 by user phoronix.

b

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --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-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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 Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)
Python Notes: Python 3.10.6
Security Notes: 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 + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 4 August 2023 19:01 by user phoronix.

c

Processor: AMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 800GB INTEL SSDPF21Q800GB, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710

OS: Ubuntu 22.04, Kernel: 6.2.0-phx (x86_64), Desktop: GNOME Shell 42.5, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 11.3.0 + LLVM 14.0.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --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-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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 Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)
Python Notes: Python 3.10.6
Security Notes: 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 + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 5 August 2023 05:10 by user phoronix.