Exploiting Task Parallelism with OpenCL: A Case Study

作     者：Jaaskelainen, Pekka Korhonen, Ville Koskela, Matias Takala, Jarmo Egiazarian, Karen Danielyan, Aram Cruz, Cristovao Price, James McIntosh-Smith, Simon 

作者机构：Tampere Univ Technol Tampere Finland Tampere Univ Technol Customized Parallel Comp CPC Grp Tampere Finland Noiseless Imaging Ltd Tampere Finland Univ Bristol High Performance Comp Bristol Avon England 

出 版 物：《JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY》 (信号处理系统杂志)

年 卷 期：2019年第91卷第1期

页      面：33-46页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：ARTEMIS joint undertaking Academy of Finland Finnish Funding Agency for Technology and Innovation [40115/13] 

主　　题：OpenCL Task-level parallelism 

摘      要：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.