Just-In-Time GPU Compilation for Interpreted Languages with Partial Evaluation

Fumero, J., Steuwer, M. , Stadler, L. and Dubach, C. (2017) Just-In-Time GPU Compilation for Interpreted Languages with Partial Evaluation. In: VEE '17 Proceedings of the 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, Xi'an, China, 08-09 Apr 2017, ISBN 9781450349482 (doi:10.1145/3050748.3050761)

146598.pdf - Published Version



Computer systems are increasingly featuring powerful parallel devices with the advent of many-core CPUs and GPUs. This offers the opportunity to solve computationally-intensive problems at a fraction of the time traditional CPUs need. However, exploiting heterogeneous hardware requires the use of low-level programming language approaches such as OpenCL, which is incredibly challenging, even for advanced programmers. On the application side, interpreted dynamic languages are increasingly becoming popular in many domains due to their simplicity, expressiveness and flexibility. However, this creates a wide gap between the high-level abstractions offered to programmers and the low-level hardware-specific interface. Currently, programmers must rely on high performance libraries or they are forced to write parts of their application in a low-level language like OpenCL. Ideally, non-expert programmers should be able to exploit heterogeneous hardware directly from their interpreted dynamic languages. In this paper, we present a technique to transparently and automatically offload computations from interpreted dynamic languages to heterogeneous devices. Using just-in-time compilation, we automatically generate OpenCL code at runtime which is specialized to the actual observed data types using profiling information. We demonstrate our technique using R, which is a popular interpreted dynamic language predominately used in big data analytic. Our experimental results show the execution on a GPU yields speedups of over 150x compared to the sequential FastR implementation and the obtained performance is competitive with manually written GPU code. We also show that when taking into account start-up time, large speedups are achievable, even when the applications run for as little as a few seconds.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Steuwer, Dr Michel
Authors: Fumero, J., Steuwer, M., Stadler, L., and Dubach, C.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in VEE '17 Proceedings of the 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record