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Exploiting Task Parallelism with OpenCL: A Case Study.

Authors :
Jääskeläinen, Pekka
Korhonen, Ville
Koskela, Matias
Takala, Jarmo
Egiazarian, Karen
Danielyan, Aram
Cruz, Cristóvão
Price, James
McIntosh-Smith, Simon
Source :
Journal of Signal Processing Systems for Signal, Image & Video Technology; Jan2019, Vol. 91 Issue 1, p33-46, 14p
Publication Year :
2019

Abstract

While data parallelism aspects of OpenCL have been of primary interest due to the massively data parallel GPUs being on focus, OpenCL also provides powerful capabilities to describe task parallelism. In this article we study the task parallel concepts available in OpenCL and find out how well the different vendor-specific implementations can exploit task parallelism when the parallelism is described in various ways utilizing the command queues. We show that the vendor implementations are not yet capable of extracting kernel-level task parallelism from in-order queues automatically. To assess the potential performance benefits of in-order queue parallelization, we implemented such capabilities to an open source implementation of OpenCL. The evaluation was conducted by means of a case study of an advanced noise reduction algorithm described as a multi-kernel OpenCL application. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19398018
Volume :
91
Issue :
1
Database :
Complementary Index
Journal :
Journal of Signal Processing Systems for Signal, Image & Video Technology
Publication Type :
Academic Journal
Accession number :
134057784
Full Text :
https://doi.org/10.1007/s11265-018-1416-1