susel-vm2-ML-05sep2020

VMware testing on openSUSE Leap 15.2 via the Phoronix Test Suite.

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susel-vm2-ML-05sep2020
September 06 2020
  3 Hours, 55 Minutes
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susel-vm2-ML-05sep2020

Processor: 8 x AMD Ryzen Threadripper 3960X 24-Core (16 Cores), Motherboard: Intel 440BX (6.00 BIOS), Chipset: Intel 440BX/ZX/DX, Memory: 16GB, Disk: 107GB VMware Virtual S, Graphics: SVGA3D; build: RELEASE; LLVM;, Audio: Ensoniq ES1371/ES1373, Network: 2 x Intel 82545EM + 3 x AMD 79c970

OS: openSUSE Leap 15.2, Kernel: 5.3.18-lp152.36-default (x86_64), Desktop: GNOME Shell 3.34.5, Display Server: X Server + Wayland, OpenGL: 2.1 Mesa 19.3.4, Compiler: GCC 7.5.0, File-System: btrfs, Screen Resolution: 1714x924, System Layer: VMware

Compiler Notes: --build=x86_64-suse-linux --disable-libcc1 --disable-libssp --disable-libstdcxx-pch --disable-libvtv --disable-plugin --disable-werror --enable-checking=release --enable-gnu-indirect-function --enable-languages=c,c++,objc,fortran,obj-c++,ada,go --enable-libstdcxx-allocator=new --enable-linux-futex --enable-multilib --enable-offload-targets=hsa,nvptx-none=/usr/nvptx-none, --enable-ssp --enable-version-specific-runtime-libs --host=x86_64-suse-linux --mandir=/usr/share/man --with-arch-32=x86-64 --with-gcc-major-version-only --with-slibdir=/lib64 --with-tune=generic --without-cuda-driver --without-system-libunwind
Processor Notes: CPU Microcode: 0x8301039
Python Notes: Python 2.7.17 + Python 3.6.10
Security 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

susel-vm2-ML-05sep2020OpenBenchmarking.orgPhoronix Test Suite8 x AMD Ryzen Threadripper 3960X 24-Core (16 Cores)Intel 440BX (6.00 BIOS)Intel 440BX/ZX/DX16GB107GB VMware Virtual SSVGA3D; buildEnsoniq ES1371/ES13732 x Intel 82545EM + 3 x AMD 79c970openSUSE Leap 15.25.3.18-lp152.36-default (x86_64)GNOME Shell 3.34.5X Server + Wayland2.1 Mesa 19.3.4GCC 7.5.0btrfs1714x924VMwareProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionSystem LayerSusel-vm2-ML-05sep2020 BenchmarksSystem Logs- --build=x86_64-suse-linux --disable-libcc1 --disable-libssp --disable-libstdcxx-pch --disable-libvtv --disable-plugin --disable-werror --enable-checking=release --enable-gnu-indirect-function --enable-languages=c,c++,objc,fortran,obj-c++,ada,go --enable-libstdcxx-allocator=new --enable-linux-futex --enable-multilib --enable-offload-targets=hsa,nvptx-none=/usr/nvptx-none, --enable-ssp --enable-version-specific-runtime-libs --host=x86_64-suse-linux --mandir=/usr/share/man --with-arch-32=x86-64 --with-gcc-major-version-only --with-slibdir=/lib64 --with-tune=generic --without-cuda-driver --without-system-libunwind - CPU Microcode: 0x8301039- Python 2.7.17 + Python 3.6.10- 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

susel-vm2-ML-05sep2020numenta-nab: EXPoSEnumpy: plaidml: No - Inference - ResNet 50 - CPUtensorflow-lite: SqueezeNettensorflow-lite: Mobilenet Floatonednn: IP Batch All - f32 - CPUonednn: Recurrent Neural Network Training - f32 - CPUonednn: Recurrent Neural Network Inference - f32 - CPUnumenta-nab: Earthgecko Skylineplaidml: No - Inference - VGG16 - CPUtensorflow-lite: Inception ResNet V2tensorflow-lite: Inception V4onednn: Deconvolution Batch deconv_1d - u8s8f32 - CPUtensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Quantonednn: Matrix Multiply Batch Shapes Transformer - f32 - CPUonednn: IP Batch 1D - u8s8f32 - CPUdeepspeech: onednn: IP Batch All - u8s8f32 - CPUnumenta-nab: Bayesian Changepointonednn: Convolution Batch Shapes Auto - f32 - CPUnumenta-nab: Relative Entropyonednn: Deconvolution Batch deconv_1d - f32 - CPUonednn: Deconvolution Batch deconv_3d - u8s8f32 - CPUonednn: Deconvolution Batch deconv_3d - f32 - CPUnumenta-nab: Windowed Gaussianonednn: IP Batch 1D - f32 - CPUonednn: Matrix Multiply Batch Shapes Transformer - u8s8f32 - CPUonednn: Convolution Batch Shapes Auto - u8s8f32 - CPUsusel-vm2-ML-05sep2020999.390296.156.33168897121773.080.1139571.32881.1272140.13418.922326460200514011.32601488111284281.950995.1969472.4386665.920855.4658.1887828.85810.561510.6463714.681415.3155.875283.3447032.3428OpenBenchmarking.org

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.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: EXPoSEsusel-vm2-ML-05sep20202004006008001000SE +/- 6.48, N = 3999.39

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy Benchmarksusel-vm2-ML-05sep202060120180240300SE +/- 7.95, N = 9296.15

PlaidML

This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterPlaidMLFP16: No - Mode: Inference - Network: ResNet 50 - Device: CPUsusel-vm2-ML-05sep2020246810SE +/- 0.03, N = 36.33

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: SqueezeNetsusel-vm2-ML-05sep202040K80K120K160K200KSE +/- 5033.34, N = 15168897

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet Floatsusel-vm2-ML-05sep202030K60K90K120K150KSE +/- 2633.94, N = 15121773.0

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.orgms, Fewer Is BetteroneDNN 1.5Harness: IP Batch All - Data Type: f32 - Engine: CPUsusel-vm2-ML-05sep202020406080100SE +/- 0.92, N = 1580.11MIN: 50.641. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPUsusel-vm2-ML-05sep2020120240360480600SE +/- 26.23, N = 15571.33MIN: 381.331. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPUsusel-vm2-ML-05sep202020406080100SE +/- 3.45, N = 1581.13MIN: 45.841. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

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.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko Skylinesusel-vm2-ML-05sep2020306090120150SE +/- 0.37, N = 3140.13

PlaidML

This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterPlaidMLFP16: No - Mode: Inference - Network: VGG16 - Device: CPUsusel-vm2-ML-05sep2020510152025SE +/- 0.25, N = 318.92

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2susel-vm2-ML-05sep2020500K1000K1500K2000K2500KSE +/- 4669.71, N = 32326460

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4susel-vm2-ML-05sep2020400K800K1200K1600K2000KSE +/- 7895.50, N = 32005140

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.orgms, Fewer Is BetteroneDNN 1.5Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPUsusel-vm2-ML-05sep20203691215SE +/- 0.20, N = 1211.33MIN: 6.681. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet Mobilesusel-vm2-ML-05sep202030K60K90K120K150KSE +/- 504.69, N = 3148811

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet Quantsusel-vm2-ML-05sep202030K60K90K120K150KSE +/- 702.49, N = 3128428

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.orgms, Fewer Is BetteroneDNN 1.5Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPUsusel-vm2-ML-05sep20200.4390.8781.3171.7562.195SE +/- 0.05695, N = 151.95099MIN: 0.871. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPUsusel-vm2-ML-05sep20201.16932.33863.50794.67725.8465SE +/- 0.09939, N = 125.19694MIN: 2.511. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

DeepSpeech

Mozilla DeepSpeech is a speech-to-text engine powered by TensorFlow for machine learning and derived from Baidu's Deep Speech research paper. This test profile times the speech-to-text process for a roughly three minute audio recording. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterDeepSpeech 0.6susel-vm2-ML-05sep20201632486480SE +/- 0.80, N = 372.44

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.orgms, Fewer Is BetteroneDNN 1.5Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPUsusel-vm2-ML-05sep20201530456075SE +/- 0.47, N = 365.92MIN: 50.231. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

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.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian Changepointsusel-vm2-ML-05sep20201224364860SE +/- 0.35, N = 355.47

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.orgms, Fewer Is BetteroneDNN 1.5Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPUsusel-vm2-ML-05sep2020246810SE +/- 0.17440, N = 158.18878MIN: 5.671. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

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.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative Entropysusel-vm2-ML-05sep2020714212835SE +/- 0.26, N = 328.86

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.orgms, Fewer Is BetteroneDNN 1.5Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPUsusel-vm2-ML-05sep20203691215SE +/- 0.03, N = 310.56MIN: 7.571. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPUsusel-vm2-ML-05sep20203691215SE +/- 0.15, N = 1510.65MIN: 5.391. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPUsusel-vm2-ML-05sep202048121620SE +/- 0.48, N = 1514.68MIN: 8.41. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

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.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed Gaussiansusel-vm2-ML-05sep202048121620SE +/- 0.05, N = 315.32

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.orgms, Fewer Is BetteroneDNN 1.5Harness: IP Batch 1D - Data Type: f32 - Engine: CPUsusel-vm2-ML-05sep20201.32192.64383.96575.28766.6095SE +/- 0.09161, N = 35.87528MIN: 3.831. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPUsusel-vm2-ML-05sep20200.75261.50522.25783.01043.763SE +/- 0.04052, N = 33.34470MIN: 1.91. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPUsusel-vm2-ML-05sep2020816243240SE +/- 0.21, N = 332.34MIN: 27.031. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -lpthread -ldl

susel-vm2-ML-05sep2020

Processor: 8 x AMD Ryzen Threadripper 3960X 24-Core (16 Cores), Motherboard: Intel 440BX (6.00 BIOS), Chipset: Intel 440BX/ZX/DX, Memory: 16GB, Disk: 107GB VMware Virtual S, Graphics: SVGA3D; build: RELEASE; LLVM;, Audio: Ensoniq ES1371/ES1373, Network: 2 x Intel 82545EM + 3 x AMD 79c970

OS: openSUSE Leap 15.2, Kernel: 5.3.18-lp152.36-default (x86_64), Desktop: GNOME Shell 3.34.5, Display Server: X Server + Wayland, OpenGL: 2.1 Mesa 19.3.4, Compiler: GCC 7.5.0, File-System: btrfs, Screen Resolution: 1714x924, System Layer: VMware

Compiler Notes: --build=x86_64-suse-linux --disable-libcc1 --disable-libssp --disable-libstdcxx-pch --disable-libvtv --disable-plugin --disable-werror --enable-checking=release --enable-gnu-indirect-function --enable-languages=c,c++,objc,fortran,obj-c++,ada,go --enable-libstdcxx-allocator=new --enable-linux-futex --enable-multilib --enable-offload-targets=hsa,nvptx-none=/usr/nvptx-none, --enable-ssp --enable-version-specific-runtime-libs --host=x86_64-suse-linux --mandir=/usr/share/man --with-arch-32=x86-64 --with-gcc-major-version-only --with-slibdir=/lib64 --with-tune=generic --without-cuda-driver --without-system-libunwind
Processor Notes: CPU Microcode: 0x8301039
Python Notes: Python 2.7.17 + Python 3.6.10
Security 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 6 September 2020 09:10 by user spingdev.