RHPCBenchmark: RHPCBenchmark: A package for performance testing intrinsic R...
In RHPCBenchmark: Benchmarks for High-Performance Computing Environments

Description Details Top-level benchmark functions Microbenchmark definition classes

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.

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.

RunDenseMatrixBenchmark: Executes the dense matrix microbenchmarks
RunSparseMatrixBenchmark: Executes the sparse matrix microbenchmarks
RunMachineLearningBenchmark: Executes the machine learning microbenchmarks

DenseMatrixMicrobenchmark: Specifies a dense matrix microbenchmark
SparseMatrixMicrobenchmark: Specifies a sparse matrix microbenchmark
ClusteringMicrobenchmark: Specifies a clustering for machine learning microbenchmark