ml-run1 AMD Ryzen Threadripper 2920X 12-Core testing with a MSI X399 SLI PLUS (MS-7B09) v2.0 (A.70 BIOS) and ASUS NVIDIA GeForce RTX 2080 Ti 11GB on Ubuntu 18.04 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 2008010-NE-MLRUN145899 ml-run1 Processor: AMD Ryzen Threadripper 2920X 12-Core (12 Cores / 24 Threads), Motherboard: MSI X399 SLI PLUS (MS-7B09) v2.0 (A.70 BIOS), Chipset: AMD 17h, Memory: 64GB, Disk: 1000GB Samsung SSD 970 EVO 1TB, Graphics: ASUS NVIDIA GeForce RTX 2080 Ti 11GB (1350/7000MHz), Audio: Realtek ALC1220, Monitor: E24, Network: Intel I211
OS: Ubuntu 18.04, Kernel: 5.4.0-42-generic (x86_64), Desktop: GNOME Shell 3.28.4, Display Server: X Server 1.20.8, Display Driver: NVIDIA 440.100, OpenGL: 4.6.0, OpenCL: OpenCL 1.2 CUDA 10.2.185, Compiler: GCC 7.5.0, File-System: ext4, Screen Resolution: 1920x1080
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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -vProcessor Notes: CPU Microcode: 0x800820bOpenCL Notes: GPU Compute Cores: 4352Python Notes: Python 2.7.17 + Python 3.6.9Security Notes: itlb_multihit: Not affected + 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 Full AMD retpoline IBPB: conditional STIBP: disabled RSB filling + srbds: Not affected + tsx_async_abort: Not affected
ml-run1 OpenBenchmarking.org Phoronix Test Suite AMD Ryzen Threadripper 2920X 12-Core (12 Cores / 24 Threads) MSI X399 SLI PLUS (MS-7B09) v2.0 (A.70 BIOS) AMD 17h 64GB 1000GB Samsung SSD 970 EVO 1TB ASUS NVIDIA GeForce RTX 2080 Ti 11GB (1350/7000MHz) Realtek ALC1220 E24 Intel I211 Ubuntu 18.04 5.4.0-42-generic (x86_64) GNOME Shell 3.28.4 X Server 1.20.8 NVIDIA 440.100 4.6.0 OpenCL 1.2 CUDA 10.2.185 GCC 7.5.0 ext4 1920x1080 Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server Display Driver OpenGL OpenCL Compiler File-System Screen Resolution Ml-run1 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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -v - CPU Microcode: 0x800820b - GPU Compute Cores: 4352 - Python 2.7.17 + Python 3.6.9 - itlb_multihit: Not affected + 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 Full AMD retpoline IBPB: conditional STIBP: disabled RSB filling + srbds: Not affected + tsx_async_abort: Not affected
ml-run1 onednn: IP Batch 1D - f32 - CPU onednn: IP Batch All - f32 - CPU onednn: IP Batch 1D - u8s8f32 - CPU onednn: IP Batch All - u8s8f32 - CPU onednn: Convolution Batch Shapes Auto - f32 - CPU onednn: Deconvolution Batch deconv_1d - f32 - CPU onednn: Deconvolution Batch deconv_3d - f32 - CPU onednn: Convolution Batch Shapes Auto - u8s8f32 - CPU onednn: Deconvolution Batch deconv_1d - u8s8f32 - CPU onednn: Deconvolution Batch deconv_3d - u8s8f32 - CPU onednn: Recurrent Neural Network Training - f32 - CPU onednn: Recurrent Neural Network Inference - f32 - CPU onednn: Matrix Multiply Batch Shapes Transformer - f32 - CPU onednn: Matrix Multiply Batch Shapes Transformer - u8s8f32 - CPU numpy: deepspeech: rbenchmark: tensorflow: Cifar10 plaidml: No - Inference - VGG16 - CPU plaidml: No - Inference - ResNet 50 - CPU numenta-nab: EXPoSE numenta-nab: Relative Entropy numenta-nab: Windowed Gaussian numenta-nab: Earthgecko Skyline numenta-nab: Bayesian Changepoint mlpack: scikit_ica mlpack: scikit_qda mlpack: scikit_svm mlpack: scikit_linearridgeregression scikit-learn: ml-run1 5.39728 71.4652 4.44911 48.0483 10.5290 5.84482 9.17598 13.0359 7.83018 7.07491 457.178 93.7784 2.99577 2.80790 287.74 88.64066 0.2495 81.02 11.62 4.90 941.262 20.475 9.601 113.030 50.119 62.19 175.63 14.19 6.09 14.729 OpenBenchmarking.org
oneDNN This is a test of the Intel oneDNN 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. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the oneAPI initiative. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: IP Batch 1D - Data Type: f32 - Engine: CPU ml-run1 1.2144 2.4288 3.6432 4.8576 6.072 SE +/- 0.06856, N = 3 5.39728 MIN: 4.9 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: IP Batch All - Data Type: f32 - Engine: CPU ml-run1 16 32 48 64 80 SE +/- 0.36, N = 3 71.47 MIN: 66.97 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPU ml-run1 1.001 2.002 3.003 4.004 5.005 SE +/- 0.01155, N = 3 4.44911 MIN: 4.24 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU ml-run1 11 22 33 44 55 SE +/- 0.15, N = 3 48.05 MIN: 46.2 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ml-run1 3 6 9 12 15 SE +/- 0.01, N = 3 10.53 MIN: 10.19 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU ml-run1 1.3151 2.6302 3.9453 5.2604 6.5755 SE +/- 0.02020, N = 3 5.84482 MIN: 5.37 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU ml-run1 3 6 9 12 15 SE +/- 0.07358, N = 3 9.17598 MIN: 8.71 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ml-run1 3 6 9 12 15 SE +/- 0.02, N = 3 13.04 MIN: 11.44 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPU ml-run1 2 4 6 8 10 SE +/- 0.04665, N = 3 7.83018 MIN: 7.13 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPU ml-run1 2 4 6 8 10 SE +/- 0.00995, N = 3 7.07491 MIN: 6.88 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ml-run1 100 200 300 400 500 SE +/- 1.26, N = 3 457.18 MIN: 434.86 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ml-run1 20 40 60 80 100 SE +/- 0.14, N = 3 93.78 MIN: 89.36 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ml-run1 0.674 1.348 2.022 2.696 3.37 SE +/- 0.00327, N = 3 2.99577 MIN: 2.84 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org ms, Fewer Is Better oneDNN 1.5 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ml-run1 0.6318 1.2636 1.8954 2.5272 3.159 SE +/- 0.00980, N = 3 2.80790 MIN: 2.58 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl
OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU ml-run1 1.1025 2.205 3.3075 4.41 5.5125 SE +/- 0.01, N = 3 4.90
Numenta Anomaly Benchmark Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Seconds, Fewer Is Better Numenta Anomaly Benchmark 1.1 Detector: EXPoSE ml-run1 200 400 600 800 1000 SE +/- 9.98, N = 3 941.26
ml-run1 Processor: AMD Ryzen Threadripper 2920X 12-Core (12 Cores / 24 Threads), Motherboard: MSI X399 SLI PLUS (MS-7B09) v2.0 (A.70 BIOS), Chipset: AMD 17h, Memory: 64GB, Disk: 1000GB Samsung SSD 970 EVO 1TB, Graphics: ASUS NVIDIA GeForce RTX 2080 Ti 11GB (1350/7000MHz), Audio: Realtek ALC1220, Monitor: E24, Network: Intel I211
OS: Ubuntu 18.04, Kernel: 5.4.0-42-generic (x86_64), Desktop: GNOME Shell 3.28.4, Display Server: X Server 1.20.8, Display Driver: NVIDIA 440.100, OpenGL: 4.6.0, OpenCL: OpenCL 1.2 CUDA 10.2.185, Compiler: GCC 7.5.0, File-System: ext4, Screen Resolution: 1920x1080
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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -vProcessor Notes: CPU Microcode: 0x800820bOpenCL Notes: GPU Compute Cores: 4352Python Notes: Python 2.7.17 + Python 3.6.9Security Notes: itlb_multihit: Not affected + 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 Full AMD retpoline IBPB: conditional STIBP: disabled RSB filling + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 1 August 2020 01:46 by user kai.