Microbenchmarks for determining the run time performance of aspects of the R programming environment and packages relevant to high-performance computation. The benchmarks are divided into three categories: dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functionality.

This package performs microbenchmarking for determining the run time performance of aspects of the R programming environment and packages relevant to high-performance computation. The benchmarks are divided into three categories: dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functionality. The top-level benchmark functions covering the three categories are RunDenseMatrixBenchmark, RunSparseMatrixBenchmark, RunMachineLearningBenchmark.

The companion data package RHPCBenchmarkData contains the sparse matrix files needed by the sparse matrix benchmarking function.

Installation of the benchmarking and companion data packages is trivial with
the use of the `install.packages`

function

See the vignette named 'vignette' for a more detailed explanation of the package
and additional examples. New benchmarks can be specified using the classes
`DenseMatrixMicrobenchmark`

, `SparseMatrixMicrobenchmark`

, and
`ClusteringMicrobenchmark`

; see the vignette and the object documentation for
each of these classes to learn how new microbenchmarks can be constructed.