Efficient CPU-GPU work sharing for data-parallel javascript workloads

Xianglan Piao, Channoh Kim, Younghwan Oh, Hanjun Kim, Jae W. Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Modern web browsers are required to execute many complex, compute-intensive applications, mostly written in JavaScript. With widespread adoption of heterogeneous processors, re- cent JavaScript-based data-parallel programming models, such as River Trail and WebCL, support multiple types of processing elements including CPUs and GPUS. However, significant performance gains are still left on the table since the program kernel runs on only one compute device, typ- ically selected at kernel invocation. This paper proposes a new framework for efficient work sharing between CPU and GPU for data-parallel JavaScript workloads. The work shar- ing scheduler partitions the input data into smaller chunks and dynamically dispatches them to both CPU and GPU for concurrent execution. For four data-parallel programs, our framework improves performance by up to 65% with a geometric mean speedup of 33% over GPU-only execution.

Original languageEnglish
Title of host publicationWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages357-358
Number of pages2
ISBN (Electronic)9781450327459
DOIs
Publication statusPublished - 2014 Apr 7
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: 2014 Apr 72014 Apr 11

Other

Other23rd International Conference on World Wide Web, WWW 2014
CountryKorea, Republic of
CitySeoul
Period14/4/714/4/11

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Cite this

Piao, X., Kim, C., Oh, Y., Kim, H., & Lee, J. W. (2014). Efficient CPU-GPU work sharing for data-parallel javascript workloads. In WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web (pp. 357-358). Association for Computing Machinery, Inc. https://doi.org/10.1145/2567948.2577338