Future: Friendly Parallel and Distributed Processing in R - Henrik Bengtsson - SatRday LA 2019
SatRday Los Angeles, April 6, 2019 @ UCLA Alumni Center losangeles2019.satrdays.org We recommend that you download the slides before you start watching since some slides may be blurry in the video. * To view materials for other talks at SatRday LA 2019: https://github.com/satRdays/losangeles/tree/master/2019 Future: Friendly Parallel and Distributed Processing in R for Everyone Henrik Bengtsson - UCSF, Assoc Prof, CS/Stats, R since 2000 In this talk, Henrik presentS the future ecosystem, which provides a simple, unified framework for parallel and distributed processing in R. It allows you to "write parallel code once" regardless of the operating system and compute environment. In programming, a 'future' is an abstraction for a value that may be available at some point in the future. The non-blocking nature of futures makes them ideal for asynchronous evaluation of expressions in R, e.g. in parallel on the local machine, on a set of remote machines, via an HPC job scheduler, or in the cloud. The future framework is available on CRAN and has been used in production for several years. R packages: future (https://cran.r-project.org/package=future) future.apply (https://cran.r-project.org/package=future.apply) doFuture (https://cran.r-project.org/package=doFuture) Keywords: parallel processing, distributed processes, compute cluster, high-performance compute (HPC) environment, performance
SatRday Los Angeles, April 6, 2019 @ UCLA Alumni Center losangeles2019.satrdays.org We recommend that you download the slides before you start watching since some slides may be blurry in the video. * To view materials for other talks at SatRday LA 2019: https://github.com/satRdays/losangeles/tree/master/2019 Future: Friendly Parallel and Distributed Processing in R for Everyone Henrik Bengtsson - UCSF, Assoc Prof, CS/Stats, R since 2000 In this talk, Henrik presentS the future ecosystem, which provides a simple, unified framework for parallel and distributed processing in R. It allows you to "write parallel code once" regardless of the operating system and compute environment. In programming, a 'future' is an abstraction for a value that may be available at some point in the future. The non-blocking nature of futures makes them ideal for asynchronous evaluation of expressions in R, e.g. in parallel on the local machine, on a set of remote machines, via an HPC job scheduler, or in the cloud. The future framework is available on CRAN and has been used in production for several years. R packages: future (https://cran.r-project.org/package=future) future.apply (https://cran.r-project.org/package=future.apply) doFuture (https://cran.r-project.org/package=doFuture) Keywords: parallel processing, distributed processes, compute cluster, high-performance compute (HPC) environment, performance