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arXiv · 1604.06414

FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs

Da Zheng·Disa Mhembere·Joshua T. Vogelstein·Carey E. Priebe·Randal Burns
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Abstract

R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets. The general approach for speeding up an implementation in R is to implement the algorithms in C or FORTRAN and provide an R wrapper. FlashR takes a different approach: it executes R code in parallel and scales the code beyond memory capacity by utilizing solid-state drives (SSDs) automatically. It provides a small number of generalized operations (GenOps) upon which we reimplement a large number of matrix functions in the R base package. As such, FlashR parallelizes and scales existing R code with little/no modification. To reduce data movement between CPU and SSDs, FlashR evaluates matrix operations lazily, fuses operations at runtime, and uses cache-aware, two-level matrix partitioning. We evaluate FlashR on a variety of machine learning and statistics algorithms on inputs of up to four billion data points. FlashR out-of-core tracks closely the performance of FlashR in-memory. The R code for machine learning algorithms executed in FlashR outperforms the in-memory execution of H2O and Spark MLlib by a factor of 2-10 and outperforms Revolution R Open by more than an order of magnitude.

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Da Zheng, Disa Mhembere, Joshua T. Vogelstein, Carey E. Priebe, Randal Burns. 2016-04-21. FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs. https://arxiv.org/abs/1604.06414

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