{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/flashr-r-programmed-parallel-and-scalable","title":"FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs","arxiv_id":"1604.06414","date":"2016-04-21","proceeding":null,"authors":["Da Zheng","Disa Mhembere","Joshua T. Vogelstein","Carey E. Priebe","Randal Burns"],"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.","url_abs":"http://arxiv.org/abs/1604.06414v4","url_pdf":"http://arxiv.org/pdf/1604.06414v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"flashr-r-programmed-parallel-and-scalable","repo_url":"https://github.com/KHmyung/GraphCache","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"flashr-r-programmed-parallel-and-scalable","repo_url":"https://github.com/flashxio/FlashX","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}