Compilation of MATLAB computations to CPU/GPU via C/OpenCL generation. (1st June 2020)

• Record Type: Journal Article
• Authors: Reis, Luís; Bispo, João; Cardoso, João M. P.
• Is Part Of: Concurrency and computation. Volume 32:Number 22(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-01
• Subjects: compiler optimizations -- GPU -- MATLAB -- OpenCL -- parallel programming -- shared virtual memory; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5854
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In order to take advantage of the processing power of current computing platforms, programmers typically need to develop software versions for different target devices. This task is time‐consuming and requires significant programming and computer architecture expertise. A possible and more convenient alternative is to start with a single high‐level description of a program with minimum implementation details, and generate custom implementations according to the target platform. In this paper, we use MATLAB as a high‐level programming language and propose a compiler that targets CPU/GPU computing platforms by generating customized implementations in C and OpenCL. We propose a number of compiler techniques to automatically generate efficient C and OpenCL code from MATLAB programs. One of such compiler techniques relies on heuristics to decide when and how to use Shared Virtual Memory (SVM). The experimental results show that our approach is able to generate code that provides significant speedups (eg, geometric mean speedup of 11× for a set of simple benchmarks) using a discrete GPU over equivalent sequential C code executing on a CPU. With more complex benchmarks, for which only some code regions can be parallelized, and are thus offloaded, the generated code achieved speedups of up to 2.2×. We also show the impact of using SVM, specifically fine‐grained buffers, and the results show that the compiler is able to achieve significant speedups, both over the versions without SVM and with naïve aggressive SVM use, across three CPU/GPU platforms.