Automatic CUDA code synthesis framework for multicore CPU and GPU architectures

Hanwoong Jung, Youngmin Yi, Soonhoi Ha

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Recently, general purpose GPU (GPGPU) programming has spread rapidly after CUDA was first introduced to write parallel programs in high-level languages for NVIDIA GPUs. While a GPU exploits data parallelism very effectively, task-level parallelism is exploited as a multi-threaded program on a multicore CPU. For such a heterogeneous platform that consists of a multicore CPU and GPU, we propose an automatic code synthesis framework that takes a process network model specification as input and generates a multithreaded CUDA code. With the model based specification, one can explicitly specify both function-level and loop-level parallelism in an application and explore the wide design space in mapping of function blocks and selecting the communication methods between CPU and GPU. The proposed technique is complementary to other high-level methods of CUDA programming.

Original languageEnglish
Title of host publicationParallel Processing and Applied Mathematics - 9th International Conference, PPAM 2011, Revised Selected Papers
Pages579-588
Number of pages10
EditionPART 1
DOIs
StatePublished - 2012
Event9th International Conference on Parallel Processing and Applied Mathematics, PPAM 2011 - Torun, Poland
Duration: 11 Sep 201114 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7203 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Parallel Processing and Applied Mathematics, PPAM 2011
Country/TerritoryPoland
CityTorun
Period11/09/1114/09/11

Keywords

  • CUDA
  • GPGPU
  • automatic code synthesis
  • model-based design

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