apple-macmini-2020-hpc

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.

Nebular Empirical Analysis Tool

NEAT is the Nebular Empirical Analysis Tool for empirical analysis of ionised nebulae, with uncertainty propagation. Learn more via the OpenBenchmarking.org test page.

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Timed MrBayes Analysis

This test performs a bayesian analysis of a set of primate genome sequences in order to estimate their phylogeny. Learn more via the OpenBenchmarking.org test page.

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Timed HMMer Search

This test searches through the Pfam database of profile hidden markov models. The search finds the domain structure of Drosophila Sevenless protein. Learn more via the OpenBenchmarking.org test page.

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Timed MAFFT Alignment

This test performs an alignment of 100 pyruvate decarboxylase sequences. Learn more via the OpenBenchmarking.org test page.

LAMMPS Molecular Dynamics Simulator

LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. Learn more via the OpenBenchmarking.org test page.

Model: 20k Atoms

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Model: Rhodopsin Protein

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ACES DGEMM

This is a multi-threaded DGEMM benchmark. Learn more via the OpenBenchmarking.org test page.

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Himeno Benchmark

The Himeno benchmark is a linear solver of pressure Poisson using a point-Jacobi method. Learn more via the OpenBenchmarking.org test page.

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oneDNN

Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU

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Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU

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Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU

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Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU

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Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU

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Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU

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Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU

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Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU

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Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU

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Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU

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Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU

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Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU

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Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU

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Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU

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Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU

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Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU

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Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU

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Numpy Benchmark

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

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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.

Acceleration: CPU

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GROMACS

The GROMACS (GROningen MAchine for Chemical Simulations) molecular dynamics package testing with the water_GMX50 data. This test profile allows selecting between CPU and GPU-based GROMACS builds. Learn more via the OpenBenchmarking.org test page.

Implementation: MPI CPU - Input: water_GMX50_bare

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GNU Octave Benchmark

This test profile measures how long it takes to complete several reference GNU Octave files via octave-benchmark. GNU Octave is used for numerical computations and is an open-source alternative to MATLAB. Learn more via the OpenBenchmarking.org test page.

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Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. This MNN test profile is building the OpenMP / CPU threaded version for processor benchmarking and not any GPU-accelerated test. MNN does allow making use of AVX-512 extensions. Learn more via the OpenBenchmarking.org test page.

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.

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.

FP16: No - Mode: Inference - Network: VGG16 - Device: CPU

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FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU

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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.

Detector: EXPoSE

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Detector: Relative Entropy

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Detector: Windowed Gaussian

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Detector: Earthgecko Skyline

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Detector: Bayesian Changepoint

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AI Benchmark Alpha

AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.

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PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Equation of State

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Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Equation of State

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Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Equation of State

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Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Equation of State

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Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Equation of State

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Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Equation of State

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Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Equation of State

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Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of State

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Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of State

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Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Equation of State

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Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Equation of State

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Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Equation of State

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Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of State

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Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Equation of State

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Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Equation of State

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Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Equation of State

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Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of State

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Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of State

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Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Equation of State

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Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Equation of State

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Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Equation of State

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Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Equation of State

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Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Equation of State

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Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Equation of State

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Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Equation of State

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Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Equation of State

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Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Equation of State

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Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Equation of State

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Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Equation of State

Apple M1: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'click'

Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Isoneutral Mixing

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Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Equation of State

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Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Apple M1: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'click'