Revisiting communication code generation algorithms for message-passing systems

Ferner, Clayton

doi:10.1080/17445760600568089

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…Ferner [9] also describes a method of automatically deriving communication code for distributed memory architectures. His approach is based on a parallelization technique of Lim and Lam [19] that generates so-called partitions of the index spaces of all statements in the input program.…”

Section: Related Workmentioning

confidence: 99%

Automatic code generation for distributed memory architectures in the polytope model

Classen

Griebl

2006

Proceedings 20th IEEE International Parallel &Amp; Distributed Processing Symposium

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Abstract-The polytope model has been used successfully as a tool for program analysis and transformation in the field of automatic loop parallelization. However, for the final step of automatic code generation, the generated code is either only usable on shared memory architectures or severely restricts the parallelization methods that can be applied. In this paper, we present a fully automated method for generating efficient target code, which is executable on clusters that are based on a distributed memory architecture. We also provide speedup results of experiments on a local cluster.

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Section: Related Workmentioning

confidence: 99%

Automatic code generation for distributed memory architectures in the polytope model

Classen

Griebl

2006

Proceedings 20th IEEE International Parallel &Amp; Distributed Processing Symposium

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“…3,Ferner built an open source parallelizing compiler Paraguin to generate messagepassing code for distributed-memory computer systems 3 . In addition, to reduce the number of interprocessor messages, he extended these algorithms to incorporate the mapping of virtual processors to physical processors 4 . In Refs.…”

Section: Introductionmentioning

confidence: 99%

Code generation for accurate array redistribution on automatic distributed-memory parallelization

Zhao¹,

Ding²,

Han³

et al. 2014

IJNDC

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Code generation belongs to the backend of parallelizing compiler, and is for generating efficient computation and communication code for the target parallel computing system. Traditional research resolve array redistribution mainly by generating communication code that each processor sends all data defined in its local memory to all processors, but this will bring large amount of communication redundancy, which increase with the growth of number of processors. Focusing on this problem, this paper presents an accurate code generation algorithm of array redistribution for distributed-memory architecture. The algorithm determines source processor and goals processor of each array element's migration in array redistribution by the transformation of data decompositions, then generate accurate communication code. The experimental results show that algorithm proposed by this paper can effectively reduce communication redundancy with the processor scale growth, and improve the parallel performance of applications.

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