mlaug2
Intel Core i7-10700K testing with a ASUS ROG STRIX Z490-F GAMING (0403 BIOS) and NVIDIA GeForce RTX 3060 Ti 8GB on Ubuntu 20.04 via the Phoronix Test Suite.
NVIDIA GeForce RTX 3060 Ti
Processor: Intel Core i7-10700K @ 5.10GHz (8 Cores / 16 Threads), Motherboard: ASUS ROG STRIX Z490-F GAMING (0403 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: 1024GB Viper M.2 VPN100 + Sabrent Rocket 4.0 500GB, Graphics: NVIDIA GeForce RTX 3060 Ti 8GB, Audio: Realtek ALC1220, Monitor: S24H85x, Network: Intel Device 15f3 + Intel Wi-Fi 6 AX200
OS: Ubuntu 20.04, Kernel: 5.8.0-63-generic (x86_64), Desktop: GNOME Shell 3.36.7, Display Server: X Server 1.20.9, Display Driver: NVIDIA 470.57.02, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 11.4.94, Vulkan: 1.2.175, Compiler: GCC 9.3.0 + CUDA 11.2, File-System: ext4, Screen Resolution: 5120x1440
Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --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-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-9-HskZEa/gcc-9-9.3.0/debian/tmp-nvptx/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-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: intel_pstate powersave - CPU Microcode: 0xec - Thermald 1.9.1
OpenCL Notes: GPU Compute Cores: 4864
Python Notes: Python 3.8.5
Security Notes: 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: Not affected
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.
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.
Numpy Benchmark
This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.
Mlpack Benchmark
Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.
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.
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.
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.
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.
ECP-CANDLE
The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.
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.
Mlpack Benchmark
Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.
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.
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.
Mlpack Benchmark
Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.
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.
Scikit-Learn
Scikit-learn is a Python module for machine learning Learn more via the OpenBenchmarking.org test page.
NVIDIA GeForce RTX 3060 Ti
Processor: Intel Core i7-10700K @ 5.10GHz (8 Cores / 16 Threads), Motherboard: ASUS ROG STRIX Z490-F GAMING (0403 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: 1024GB Viper M.2 VPN100 + Sabrent Rocket 4.0 500GB, Graphics: NVIDIA GeForce RTX 3060 Ti 8GB, Audio: Realtek ALC1220, Monitor: S24H85x, Network: Intel Device 15f3 + Intel Wi-Fi 6 AX200
OS: Ubuntu 20.04, Kernel: 5.8.0-63-generic (x86_64), Desktop: GNOME Shell 3.36.7, Display Server: X Server 1.20.9, Display Driver: NVIDIA 470.57.02, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 11.4.94, Vulkan: 1.2.175, Compiler: GCC 9.3.0 + CUDA 11.2, File-System: ext4, Screen Resolution: 5120x1440
Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --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-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-9-HskZEa/gcc-9-9.3.0/debian/tmp-nvptx/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-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: intel_pstate powersave - CPU Microcode: 0xec - Thermald 1.9.1
OpenCL Notes: GPU Compute Cores: 4864
Python Notes: Python 3.8.5
Security Notes: 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: Not affected
Testing initiated at 3 August 2021 06:48 by user bharath.