Core i9 10980XE Intel Core i9-10980XE testing with a Gigabyte X299X DESIGNARE 10G (F1 BIOS) and AMD Navi 10 8GB on Ubuntu 19.10 via the Phoronix Test Suite.
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phoronix-test-suite benchmark 1911271-HU-COREI910993 Core i9 10980XE Processor: Intel Core i9-10980XE @ 4.60GHz (18 Cores / 36 Threads), Motherboard: Gigabyte X299X DESIGNARE 10G (F1 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 32768MB, Disk: 240GB Force MP510, Graphics: AMD Navi 10 8GB (2100/875MHz), Audio: Realtek ALC1220, Monitor: Acer B286HK, Network: 2 x Intel 10G X550T + Intel Device 2723
OS: Ubuntu 19.10, Kernel: 5.3.0-23-generic (x86_64), Desktop: GNOME Shell 3.34.1, Display Server: X Server 1.20.5, Display Driver: modesetting 1.20.5, OpenGL: 4.5 Mesa 19.2.1 (LLVM 9.0.0), Compiler: GCC 9.2.1 20191008, File-System: ext4, Screen Resolution: 3840x2160
Compiler Notes: --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++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-offload-targets=nvptx-none,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-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 -vProcessor Notes: Scaling Governor: intel_pstate powersave - CPU Microcode: 0x500002cPython Notes: Python 2.7.17rc1 + Python 3.7.5rc1Security Notes: itlb_multihit: KVM: Mitigation of Split huge pages + 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 + tsx_async_abort: Mitigation of TSX disabled
Core i9 10980XE OpenBenchmarking.org Phoronix Test Suite Intel Core i9-10980XE @ 4.60GHz (18 Cores / 36 Threads) Gigabyte X299X DESIGNARE 10G (F1 BIOS) Intel Sky Lake-E DMI3 Registers 32768MB 240GB Force MP510 AMD Navi 10 8GB (2100/875MHz) Realtek ALC1220 Acer B286HK 2 x Intel 10G X550T + Intel Device 2723 Ubuntu 19.10 5.3.0-23-generic (x86_64) GNOME Shell 3.34.1 X Server 1.20.5 modesetting 1.20.5 4.5 Mesa 19.2.1 (LLVM 9.0.0) GCC 9.2.1 20191008 ext4 3840x2160 Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server Display Driver OpenGL Compiler File-System Screen Resolution Core I9 10980XE Benchmarks System Logs - --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++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-offload-targets=nvptx-none,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-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: intel_pstate powersave - CPU Microcode: 0x500002c - Python 2.7.17rc1 + Python 3.7.5rc1 - itlb_multihit: KVM: Mitigation of Split huge pages + 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 + tsx_async_abort: Mitigation of TSX disabled
Core i9 10980XE blender: Pabellon Barcelona - CPU-Only blender: Barbershop - CPU-Only blender: Fishy Cat - CPU-Only blender: Classroom - CPU-Only blender: BMW27 - CPU-Only gromacs: Water Benchmark askap: tConvolve OpenMP - Degridding askap: tConvolve OpenMP - Gridding askap: tConvolve MPI - Degridding askap: tConvolve MPI - Gridding askap: tConvolve MT - Degridding askap: tConvolve MT - Gridding build2: Time To Compile build-llvm: Time To Compile build-linux-kernel: Time To Compile himeno: Poisson Pressure Solver dav1d: Chimera 1080p 10-bit dav1d: Summer Nature 1080p dav1d: Summer Nature 4K dav1d: Chimera 1080p mkl-dnn: Convolution Batch conv_googlenet_v3 - bf16bf16bf16 mkl-dnn: Deconvolution Batch deconv_all - bf16bf16bf16 mkl-dnn: Convolution Batch conv_googlenet_v3 - u8s8f32 mkl-dnn: Convolution Batch conv_alexnet - bf16bf16bf16 mkl-dnn: Deconvolution Batch deconv_3d - bf16bf16bf16 mkl-dnn: Deconvolution Batch deconv_1d - bf16bf16bf16 mkl-dnn: Convolution Batch conv_all - bf16bf16bf16 mkl-dnn: Convolution Batch conv_alexnet - u8s8f32 mkl-dnn: Convolution Batch conv_3d - bf16bf16bf16 mkl-dnn: Deconvolution Batch deconv_3d - u8s8f32 mkl-dnn: Deconvolution Batch deconv_1d - u8s8f32 mkl-dnn: Convolution Batch conv_all - u8s8f32 mkl-dnn: Convolution Batch conv_3d - u8s8f32 mkl-dnn: IP Batch All - bf16bf16bf16 mkl-dnn: IP Batch 1D - bf16bf16bf16 mkl-dnn: IP Batch 1D - u8s8f32 namd: ATPase Simulation - 327,506 Atoms minife: Small mkl-dnn: IP Batch All - u8s8f32 Core i9 10980XE 331.95 373.69 143.09 267.91 92.94 1.510 4841.02 3288.12 2388.74 1573.75 2413.60 1608.44 72.360 225.99 43.473 4135.886112 48.21 299.94 170.27 275.30 224.498 3750.00 20.9331 871.392 10.7182 8.54513 4766.51 42.4483 20.1256 4761.12 0.464852 3846.89 7715.21 20.7494 5.74064 0.653733 0.99020 6900.44 7.42361 OpenBenchmarking.org
OpenBenchmarking.org Million Grid Points Per Second, More Is Better ASKAP 2018-11-10 Test: tConvolve OpenMP - Gridding Core i9 10980XE 700 1400 2100 2800 3500 SE +/- 41.10, N = 3 3288.12 1. (CXX) g++ options: -lpthread
OpenBenchmarking.org Million Grid Points Per Second, More Is Better ASKAP 2018-11-10 Test: tConvolve MPI - Degridding Core i9 10980XE 500 1000 1500 2000 2500 SE +/- 0.79, N = 3 2388.74 1. (CXX) g++ options: -lpthread
OpenBenchmarking.org Million Grid Points Per Second, More Is Better ASKAP 2018-11-10 Test: tConvolve MPI - Gridding Core i9 10980XE 300 600 900 1200 1500 SE +/- 0.46, N = 3 1573.75 1. (CXX) g++ options: -lpthread
OpenBenchmarking.org Million Grid Points Per Second, More Is Better ASKAP 2018-11-10 Test: tConvolve MT - Degridding Core i9 10980XE 500 1000 1500 2000 2500 SE +/- 0.40, N = 3 2413.60 1. (CXX) g++ options: -lpthread
OpenBenchmarking.org Million Grid Points Per Second, More Is Better ASKAP 2018-11-10 Test: tConvolve MT - Gridding Core i9 10980XE 300 600 900 1200 1500 SE +/- 0.36, N = 3 1608.44 1. (CXX) g++ options: -lpthread
OpenBenchmarking.org FPS, More Is Better dav1d 0.5.0 Video Input: Summer Nature 1080p Core i9 10980XE 70 140 210 280 350 SE +/- 0.88, N = 3 299.94 MIN: 188.75 / MAX: 326.81 1. (CC) gcc options: -pthread
OpenBenchmarking.org FPS, More Is Better dav1d 0.5.0 Video Input: Summer Nature 4K Core i9 10980XE 40 80 120 160 200 SE +/- 0.87, N = 3 170.27 MIN: 100.45 / MAX: 182.19 1. (CC) gcc options: -pthread
OpenBenchmarking.org FPS, More Is Better dav1d 0.5.0 Video Input: Chimera 1080p Core i9 10980XE 60 120 180 240 300 SE +/- 0.46, N = 3 275.30 MIN: 216.13 / MAX: 336.87 1. (CC) gcc options: -pthread
MKL-DNN DNNL This is a test of the Intel MKL-DNN (DNNL / Deep Neural Network Library) as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Convolution Batch conv_googlenet_v3 - Data Type: bf16bf16bf16 Core i9 10980XE 50 100 150 200 250 SE +/- 0.03, N = 3 224.50 MIN: 223.6 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Deconvolution Batch deconv_all - Data Type: bf16bf16bf16 Core i9 10980XE 800 1600 2400 3200 4000 SE +/- 0.56, N = 3 3750.00 MIN: 3744.26 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8f32 Core i9 10980XE 5 10 15 20 25 SE +/- 0.01, N = 3 20.93 MIN: 20.53 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Convolution Batch conv_alexnet - Data Type: bf16bf16bf16 Core i9 10980XE 200 400 600 800 1000 SE +/- 2.06, N = 3 871.39 MIN: 868.49 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Deconvolution Batch deconv_3d - Data Type: bf16bf16bf16 Core i9 10980XE 3 6 9 12 15 SE +/- 0.01, N = 3 10.72 MIN: 10.6 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Deconvolution Batch deconv_1d - Data Type: bf16bf16bf16 Core i9 10980XE 2 4 6 8 10 SE +/- 0.01735, N = 3 8.54513 MIN: 8.48 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Convolution Batch conv_all - Data Type: bf16bf16bf16 Core i9 10980XE 1000 2000 3000 4000 5000 SE +/- 0.39, N = 3 4766.51 MIN: 4757.15 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Convolution Batch conv_alexnet - Data Type: u8s8f32 Core i9 10980XE 10 20 30 40 50 SE +/- 0.07, N = 3 42.45 MIN: 41.79 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Convolution Batch conv_3d - Data Type: bf16bf16bf16 Core i9 10980XE 5 10 15 20 25 SE +/- 0.01, N = 3 20.13 MIN: 19.92 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 Core i9 10980XE 1000 2000 3000 4000 5000 SE +/- 1.50, N = 3 4761.12 MIN: 4758.06 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 Core i9 10980XE 0.1046 0.2092 0.3138 0.4184 0.523 SE +/- 0.004036, N = 15 0.464852 MIN: 0.45 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Convolution Batch conv_all - Data Type: u8s8f32 Core i9 10980XE 800 1600 2400 3200 4000 SE +/- 10.85, N = 3 3846.89 MIN: 3805.98 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: Convolution Batch conv_3d - Data Type: u8s8f32 Core i9 10980XE 1700 3400 5100 6800 8500 SE +/- 12.23, N = 3 7715.21 MIN: 7662.74 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: IP Batch All - Data Type: bf16bf16bf16 Core i9 10980XE 5 10 15 20 25 SE +/- 0.27, N = 3 20.75 MIN: 18.56 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: IP Batch 1D - Data Type: bf16bf16bf16 Core i9 10980XE 1.2916 2.5832 3.8748 5.1664 6.458 SE +/- 0.00504, N = 3 5.74064 MIN: 5.46 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: IP Batch 1D - Data Type: u8s8f32 Core i9 10980XE 0.1471 0.2942 0.4413 0.5884 0.7355 SE +/- 0.001733, N = 3 0.653733 MIN: 0.61 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
NAMD NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org days/ns, Fewer Is Better NAMD 2.13b1 ATPase Simulation - 327,506 Atoms Core i9 10980XE 0.2228 0.4456 0.6684 0.8912 1.114 SE +/- 0.00647, N = 3 0.99020
MKL-DNN DNNL This is a test of the Intel MKL-DNN (DNNL / Deep Neural Network Library) as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN DNNL 1.1 Harness: IP Batch All - Data Type: u8s8f32 Core i9 10980XE 2 4 6 8 10 SE +/- 1.89064, N = 12 7.42361 MIN: 4.2 1. (CXX) g++ options: -O3 -march=native -std=c++11 -msse4.1 -fPIC -fopenmp -pie -lpthread -ldl
Core i9 10980XE Processor: Intel Core i9-10980XE @ 4.60GHz (18 Cores / 36 Threads), Motherboard: Gigabyte X299X DESIGNARE 10G (F1 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 32768MB, Disk: 240GB Force MP510, Graphics: AMD Navi 10 8GB (2100/875MHz), Audio: Realtek ALC1220, Monitor: Acer B286HK, Network: 2 x Intel 10G X550T + Intel Device 2723
OS: Ubuntu 19.10, Kernel: 5.3.0-23-generic (x86_64), Desktop: GNOME Shell 3.34.1, Display Server: X Server 1.20.5, Display Driver: modesetting 1.20.5, OpenGL: 4.5 Mesa 19.2.1 (LLVM 9.0.0), Compiler: GCC 9.2.1 20191008, File-System: ext4, Screen Resolution: 3840x2160
Compiler Notes: --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++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-offload-targets=nvptx-none,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-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 -vProcessor Notes: Scaling Governor: intel_pstate powersave - CPU Microcode: 0x500002cPython Notes: Python 2.7.17rc1 + Python 3.7.5rc1Security Notes: itlb_multihit: KVM: Mitigation of Split huge pages + 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 + tsx_async_abort: Mitigation of TSX disabled
Testing initiated at 27 November 2019 06:27 by user phoronix.