Mapping OpenMP concepts to the stream programming model

OpenMP is a widely used parallel programming model on traditional multi-core processors. Generally, OpenMP is used to develop fine-grained parallelism through a multi-thread model. Stream programming model is a new kind of parallel programming model for stream architectures. OpenMP bears a resemblan...

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Bibliographic Details
Published in:2010 5th International Conference on Computer Science and Education pp. 1900 - 1905
Main Authors: Tao Tang, Yisong Lin, Xiaoguang Ren
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2010
Subjects:
Benchmark testing
Instruction sets
Kernel
mapping
OpenMP
parallel mechanism
Parallel processing
Programming
stream programming model
Synchronization
ISBN:1424460026, 9781424460021
Online Access:Get full text
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Summary:OpenMP is a widely used parallel programming model on traditional multi-core processors. Generally, OpenMP is used to develop fine-grained parallelism through a multi-thread model. Stream programming model is a new kind of parallel programming model for stream architectures. OpenMP bears a resemblance to the stream programming model at some level. The transformation between the two models has attracted much attention from the research community, since it is the foundation of porting programs between the two architectures. Most related researches focus on the efficiency of porting existing parallel programs to the new architectures such as GPUs. Very few of these studies, however, focus on the portative problem systematically, namely, what kind of parallel programs can be or should be transplanted into stream programs and mapped to run on the stream processors. In this paper, we study the mapping relationship of parallel mechanism in OpenMP to the stream programming model, and point out those parallel mechanisms in OpenMP that are infeasible or undesirable for stream programs. By analyzing two typical benchmarks, we draw the conclusion that a majority of scientific applications are suitable to be mapped to the stream programming model. Our conclusion effectively validates the idea of accelerating scientific applications with the stream processors.
ISBN:1424460026
9781424460021
DOI:10.1109/ICCSE.2010.5593822