Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

A popular approach to providing nonexperts in parallel computing with an easy-to-use programming model is to design a software library consisting of a set of preparallelized routines, and hide the intricacies of parallelization behind the library's API. However, for regular domain problems (suc...

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Vydáno v:IEEE transactions on parallel and distributed systems Ročník 15; číslo 10; s. 865 - 877
Hlavní autoři: Seinstra, F.J., Koelma, D., Bagdanov, A.D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Communication system operations and management
Computation
Computer networks
Computer programs
data communications aspects
Image processing
image processing software
Law
Legal factors
Libraries
Mathematical analysis
Memory management
Optimization
Parallel processing
Parallel programming
Runtime
Software design
Software libraries
Studies
ISSN:1045-9219, 1558-2183
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Shrnutí:A popular approach to providing nonexperts in parallel computing with an easy-to-use programming model is to design a software library consisting of a set of preparallelized routines, and hide the intricacies of parallelization behind the library's API. However, for regular domain problems (such as simple matrix manipulations or low-level image processing applications-in which all elements in a regular subset of a dense data field are accessed in turn) speedup obtained with many such library-based parallelization tools is often suboptimal. This is because interoperation optimization (or: time-optimization of communication steps across library calls) is generally not incorporated in the library implementations. We present a simple, efficient, finite state machine-based approach for communication minimization of library-based data parallel regular domain problems. In the approach, referred to as lazy parallelization, a sequential program is parallelized automatically at runtime by inserting communication primitives and memory management operations whenever necessary. Apart from being simple and cheap, lazy parallelization guarantees to generate legal, correct, and efficient parallel programs at all times. The effectiveness of the approach is demonstrated by analyzing the performance characteristics of two typical regular domain problems obtained from the field of low-level image processing. Experimental results show significant performance improvements over nonoptimized parallel applications. Moreover, obtained communication behavior is found to be optimal with respect to the abstraction level of message passing programs.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2004.55