Performance Benchmarking of the R Programming Environment on the Stampede 1 . 5 Supercomputer. Mccombs, J., R. & Michael, S. In Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact (PEARC17), volume Part F1287, pages 8, 2017. Association for Computing Machinery. ![Performance Benchmarking of the R Programming Environment on the Stampede 1 . 5 Supercomputer [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 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 Xeonbased compute nodes of the Stampede supercomputer cluster at the Texas Advanced Computing Center. The benchmark 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 microbenchmarks. For matrices of medium to large dimensions, the Knights Landing nodes were competitive with or outperformed the standard Xeon-based nodes with most of the linear algebra microbenchmarks, executing as much as five times faster than the standard Xeon-based nodes. For the clustering and neural network training microbenchmarks, the standard Xeonbased 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. © 2017 Association for Computing Machinery.
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title = {Performance Benchmarking of the R Programming Environment on the Stampede 1 . 5 Supercomputer},
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abstract = {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 Xeonbased compute nodes of the Stampede supercomputer cluster at the Texas Advanced Computing Center. The benchmark 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 microbenchmarks. For matrices of medium to large dimensions, the Knights Landing nodes were competitive with or outperformed the standard Xeon-based nodes with most of the linear algebra microbenchmarks, executing as much as five times faster than the standard Xeon-based nodes. For the clustering and neural network training microbenchmarks, the standard Xeonbased 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. © 2017 Association for Computing Machinery.},
bibtype = {inproceedings},
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