10980xe eo june

Intel Core i9-10980XE testing with a ASRock X299 Steel Legend (P1.30 BIOS) and NVIDIA NV132 11GB on Ubuntu 21.04 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 2106288-IB-10980XEEO77
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10980xe eo juneProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanCompilerFile-SystemScreen Resolution123Intel Core i9-10980XE @ 4.80GHz (18 Cores / 36 Threads)ASRock X299 Steel Legend (P1.30 BIOS)Intel Sky Lake-E DMI3 Registers32GBSamsung SSD 970 PRO 512GBNVIDIA NV132 11GBRealtek ALC1220ASUS VP28UIntel I219-V + Intel I211Ubuntu 21.045.11.0-17-generic (x86_64)GNOME Shell 3.38.4X Server + Waylandnouveau4.3 Mesa 21.0.11.0.2GCC 10.3.0ext42560x1600OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseCompiler Details- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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-mutex --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-10-gDeRY6/gcc-10-10.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-10-gDeRY6/gcc-10-10.3.0/debian/tmp-gcn/usr,hsa --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_cpufreq schedutil - CPU Microcode: 0x5003006Security Details- itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: 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: Mitigation of TSX disabled

123Result OverviewPhoronix Test Suite100%101%102%104%105%Mobile Neural NetworkC-BloscNCNNTNN

10980xe eo junemnn: mobilenetV3ncnn: CPU - googlenetmnn: resnet-v2-50ncnn: CPU - resnet50ncnn: CPU - resnet18mnn: SqueezeNetV1.0mnn: MobileNetV2_224ncnn: CPU - mnasnetncnn: CPU-v3-v3 - mobilenet-v3ncnn: CPU-v2-v2 - mobilenet-v2ncnn: CPU - vgg16ncnn: CPU - mobilenetncnn: CPU - squeezenet_ssdblosc: blosclzncnn: CPU - efficientnet-b0mnn: inception-v3mnn: mobilenet-v1-1.0ncnn: CPU - regnety_400mncnn: CPU - yolov4-tinyncnn: CPU - blazefacencnn: CPU - shufflenet-v2tnn: CPU - DenseNettnn: CPU - SqueezeNet v2tnn: CPU - MobileNet v2tnn: CPU - SqueezeNet v1.1ncnn: CPU - alexnetmnn: squeezenetv1.11232.44512.6431.03518.2311.316.1243.9394.894.675.2736.6615.0015.9712212.26.4633.0862.75714.2823.632.505.083444.74867.685313.376278.3389.524.8902.46713.5831.10819.1511.786.1573.9884.824.735.2137.2115.1216.2211989.06.5333.3082.72114.4223.732.525.093444.94667.592313.433278.4199.774.7632.27112.7328.95517.9411.085.8863.8464.984.795.1436.3314.7815.8612033.66.5832.7492.71614.3423.832.55.13456.84467.656313.303278.4379.434.465OpenBenchmarking.org

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: mobilenetV31230.55511.11021.66532.22042.7755SE +/- 0.017, N = 3SE +/- 0.021, N = 32.4452.4672.271MIN: 2.3 / MAX: 2.64MIN: 2.3 / MAX: 2.62MIN: 2.09 / MAX: 2.431. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

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 20210525Target: CPU - Model: googlenet1233691215SE +/- 0.14, N = 3SE +/- 0.43, N = 312.6413.5812.73MIN: 12.33 / MAX: 13.48MIN: 12.61 / MAX: 14.47MIN: 12.58 / MAX: 16.431. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: resnet-v2-50123714212835SE +/- 0.12, N = 3SE +/- 0.09, N = 331.0431.1128.96MIN: 30.58 / MAX: 31.5MIN: 30.5 / MAX: 31.56MIN: 26.42 / MAX: 41.291. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

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 20210525Target: CPU - Model: resnet50123510152025SE +/- 0.39, N = 3SE +/- 0.02, N = 318.2319.1517.94MIN: 17.61 / MAX: 19.38MIN: 18.94 / MAX: 20.22MIN: 17.75 / MAX: 18.211. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU - Model: resnet181233691215SE +/- 0.39, N = 3SE +/- 0.35, N = 311.3111.7811.08MIN: 10.71 / MAX: 12.98MIN: 10.96 / MAX: 12.34MIN: 10.98 / MAX: 11.231. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: SqueezeNetV1.0123246810SE +/- 0.065, N = 3SE +/- 0.187, N = 36.1246.1575.886MIN: 5.73 / MAX: 6.41MIN: 5.65 / MAX: 6.58MIN: 5.76 / MAX: 6.341. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: MobileNetV2_2241230.89731.79462.69193.58924.4865SE +/- 0.075, N = 3SE +/- 0.061, N = 33.9393.9883.846MIN: 3.51 / MAX: 4.19MIN: 3.63 / MAX: 4.22MIN: 3.64 / MAX: 4.161. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

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 20210525Target: CPU - Model: mnasnet1231.12052.2413.36154.4825.6025SE +/- 0.04, N = 3SE +/- 0.06, N = 34.894.824.98MIN: 4.63 / MAX: 8.96MIN: 4.6 / MAX: 8.98MIN: 4.69 / MAX: 14.051. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU-v3-v3 - Model: mobilenet-v31231.07782.15563.23344.31125.389SE +/- 0.03, N = 3SE +/- 0.02, N = 34.674.734.79MIN: 4.5 / MAX: 10.21MIN: 4.55 / MAX: 9.08MIN: 4.59 / MAX: 13.881. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU-v2-v2 - Model: mobilenet-v21231.18582.37163.55744.74325.929SE +/- 0.07, N = 3SE +/- 0.07, N = 35.275.215.14MIN: 4.98 / MAX: 14.57MIN: 4.91 / MAX: 11.17MIN: 4.94 / MAX: 7.941. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU - Model: vgg16123918273645SE +/- 0.40, N = 3SE +/- 0.36, N = 336.6637.2136.33MIN: 35.94 / MAX: 45.92MIN: 36.37 / MAX: 38.58MIN: 36.18 / MAX: 37.141. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU - Model: mobilenet12348121620SE +/- 0.09, N = 3SE +/- 0.02, N = 315.0015.1214.78MIN: 14.7 / MAX: 15.9MIN: 14.92 / MAX: 25.35MIN: 14.62 / MAX: 14.981. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU - Model: squeezenet_ssd12348121620SE +/- 0.07, N = 3SE +/- 0.00, N = 315.9716.2215.86MIN: 15.73 / MAX: 16.89MIN: 16.08 / MAX: 17.23MIN: 15.73 / MAX: 16.821. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

C-Blosc

A simple, compressed, fast and persistent data store library for C. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterC-Blosc 2.0Compressor: blosclz1233K6K9K12K15KSE +/- 85.29, N = 3SE +/- 40.46, N = 312212.211989.012033.61. (CC) gcc options: -std=gnu99 -O3 -pthread -lrt -lm

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 20210525Target: CPU - Model: efficientnet-b0123246810SE +/- 0.07, N = 3SE +/- 0.11, N = 36.466.536.58MIN: 6.24 / MAX: 15.37MIN: 6.22 / MAX: 11.36MIN: 6.48 / MAX: 6.761. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: inception-v3123816243240SE +/- 0.44, N = 3SE +/- 0.77, N = 333.0933.3132.75MIN: 32.27 / MAX: 34.13MIN: 31.58 / MAX: 34.55MIN: 31.59 / MAX: 35.711. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: mobilenet-v1-1.01230.62031.24061.86092.48123.1015SE +/- 0.009, N = 3SE +/- 0.018, N = 32.7572.7212.716MIN: 2.51 / MAX: 3.06MIN: 2.53 / MAX: 2.86MIN: 2.5 / MAX: 3.011. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

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 20210525Target: CPU - Model: regnety_400m12348121620SE +/- 0.12, N = 3SE +/- 0.04, N = 314.2814.4214.34MIN: 13.81 / MAX: 15.27MIN: 14.12 / MAX: 15.6MIN: 14.1 / MAX: 14.641. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU - Model: yolov4-tiny123612182430SE +/- 0.16, N = 3SE +/- 0.31, N = 323.6323.7323.83MIN: 22.53 / MAX: 31.57MIN: 22.5 / MAX: 27.9MIN: 22.55 / MAX: 28.061. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU - Model: blazeface1230.5671.1341.7012.2682.835SE +/- 0.01, N = 3SE +/- 0.02, N = 32.502.522.50MIN: 2.43 / MAX: 3.88MIN: 2.45 / MAX: 3.08MIN: 2.45 / MAX: 3.11. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210525Target: CPU - Model: shufflenet-v21231.14752.2953.44254.595.7375SE +/- 0.05, N = 3SE +/- 0.00, N = 35.085.095.10MIN: 4.89 / MAX: 15.72MIN: 4.96 / MAX: 9.69MIN: 4.92 / MAX: 9.81. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: DenseNet1237001400210028003500SE +/- 0.75, N = 3SE +/- 0.89, N = 33444.753444.953456.84MIN: 3419.6 / MAX: 3487.48MIN: 3423.15 / MAX: 3472.15MIN: 3421.83 / MAX: 3793.271. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v21231530456075SE +/- 0.06, N = 3SE +/- 0.03, N = 367.6967.5967.66MIN: 67.31 / MAX: 68.21MIN: 67.26 / MAX: 68.07MIN: 67.4 / MAX: 68.041. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: MobileNet v212370140210280350SE +/- 0.35, N = 3SE +/- 0.23, N = 3313.38313.43313.30MIN: 312.28 / MAX: 319.93MIN: 312.21 / MAX: 322.13MIN: 312.67 / MAX: 314.851. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v1.112360120180240300SE +/- 0.06, N = 3SE +/- 0.09, N = 3278.34278.42278.44MIN: 277.77 / MAX: 279.28MIN: 277.51 / MAX: 280.12MIN: 277.79 / MAX: 279.291. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

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 20210525Target: CPU - Model: alexnet1233691215SE +/- 0.38, N = 3SE +/- 0.16, N = 39.529.779.43MIN: 8.64 / MAX: 10.09MIN: 9.35 / MAX: 10.38MIN: 9.33 / MAX: 9.631. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: squeezenetv1.11231.10032.20063.30094.40125.5015SE +/- 0.027, N = 3SE +/- 0.221, N = 34.8904.7634.465MIN: 4.77 / MAX: 5.06MIN: 4.22 / MAX: 5.15MIN: 4.36 / MAX: 4.871. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl