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A.D.: Finite State MachineBased Optimization of Data Parallel Regular Domain Problems Applied in LowLevel Image Processing
 IEEE Trans. Parallel Distrib. Syst
, 2004
"... Abstract—A popular approach to providing nonexperts in parallel computing with an easytouse 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 ..."
Abstract

Cited by 9 (6 self)
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Abstract—A popular approach to providing nonexperts in parallel computing with an easytouse 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 lowlevel 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 librarybased parallelization tools is often suboptimal. This is because interoperation optimization (or: timeoptimization of communication steps across library calls) is generally not incorporated in the library implementations. This paper presents a simple, efficient, finite state machinebased approach for communication minimization of librarybased 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 lowlevel 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. Index Terms—Parallel processing, data communications aspects, optimization, image processing software. 1
Finite State Machine Based Optimization of Data Parallel Regular Domain Problems Applied in Low Level Image Processing
, 2004
"... A popular approach to providing nonexperts in parallel computing with an easytouse 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 ( ..."
Abstract

Cited by 7 (7 self)
 Add to MetaCart
A popular approach to providing nonexperts in parallel computing with an easytouse 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 eld are accessed in turn) speedup obtained with many such librarybased parallelization tools is often suboptimal. This is because interoperation optimization (or: timeoptimization of communication steps across library calls) is generally not incorporated in the library implementations.