High-Performance, High-Level Tools for Statistical Inference and Unsupervised Learning

University Of California-Davis, Davis CA

Investigators

Abstract

Using the "Julia" language for scientific computing developed at MIT, the UC Davis, MIT, and Julia Computing, Inc. teams funded by this project will extend the Julia language and runtime to utilize massively-parallel graphics processing units (GPUs) as first-class processors for scientific computing. Julia offers the twin advantages of straightforward, high-level programmability as well as excellent performance; adding GPU capability within Julia opens the door to even greater performance. The team will use Julia and its new GPU capabilities to address emerging important problems in statistical inference and unsupervised learning, an application area that aims to draw useful conclusions from massive amounts of data. Using a high-level, high-performance language such as Julia will allow non-computer-science experts to address these important problems. The project team brings together three threads of expertise to address the challenge of delivering best-of-breed performance from a high-level language in the context of the important application domain of statistical inference and unsupervised learning: (1) application experts in this domain; (2) the designers of the programming language Julia, which allows its users to express their ideas in high-level abstractions that are natural to statisticians and mathematicians; and (3) parallel computing experts, who will develop the new support within Julia to target high-performance GPUs as first-class processors. The major outcome of this project will be a significantly enhanced Julia language and runtime that will deliver both high-level programmability, targeted at scientists who are not parallel computing experts, and best-of-breed performance.