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We present performance results obtained with a new single-node performance benchmark of the R programming environment on the many-core Xeon Phi Knights Landing and standard Xeon-based compute nodes of the Stampede supercomputer cluster at the Texas Advanced Computing Center. The benchmark package consists of microbenchmarks of linear algebra kernels and machine learning functionality that includes clustering and neural network training from the R distribution. The standard Xeon-based nodes outperformed their Xeon Phi counterparts for matrices of small to medium dimensions, performing approximately twice as fast for most of the linear algebra micro benchmarks. For most of the same microbenchmarks the Knights Landing compute nodes were competitive with or outperformed the standard Xeon-based nodes for matrices of medium to large dimensions, executing as much as five times faster than the standard Xeon-based nodes. For the clustering and neural network training microbenchmarks, the standard Xeon-based nodes performed up to four times faster than their Xeon Phi counterparts for many large data sets, indicating that commonly used R packages may need to be reengineered to take advantage of existing optimized, scalable kernels.
