RHPCBenchmark {RHPCBenchmark}

RHPCBenchmark: A package for performance testing intrinsic R functionality and established packages relevant to high-performance computing

Description

The benchmarks are divided into three categories: dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functionality. All of the dense linear algebra kernels are implemented around BLAS or LAPACK interfaces. The sparse linear algebra kernels are members of the R Matrix library. The machine learning benchmarks currently only cover variants of K-means functionality for clustering using the cluster package. The dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functions that are benchmarked are all part of the R interpreter's intrinsic functionality or packages included the with the R programming environment standard distributions from CRAN.

Details

For fast performance of the dense matrix kernels, it is crucial to link the R programming environment with optimized BLAS and LAPACK libraries. It is also important to have substantial amounts of memory (16GB minimum) to run most of the microbenchmarks. If any of the microbenchmarks fails to run in a timely manner or fails due to memory constraints, the matrix sizes and number of performance trials per matrix can be adjusted. See the documentation for top-level benchmark functions and the microbenchmark definition classes listed below for information on how to configure the individual microbenchmarks.

Top-level benchmark functions

RunDenseMatrixBenchmark

Executes the dense matrix microbenchmarks

RunSparseMatrixBenchmark

Executes the sparse matrix microbenchmarks

RunMachineLearningBenchmark

Executes the machine learning microbenchmarks

Microbenchmark definition classes

DenseMatrixMicrobenchmark

Specifies a dense matrix microbenchmark

SparseMatrixMicrobenchmark

Specifies a sparse matrix microbenchmark

ClusteringMicrobenchmark

Specifies a clustering for machine learning microbenchmark