Wave-Fronts Parallelization and Scheduling

Parsa, Saeed; Lotfi, Shahriar

doi:10.1109/iit.2007.4430369

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…In the first step, called data dependence analysis, the type of dependencies is determined in the form of dependence vectors [19][20][21]. Obviously, some of them are not able to run in parallel due to the lack of sufficient information at the compilation time, but those dependencies that are regular and fixed can be run in parallel as long as they have a uniform structure because of using well-known methods such as Wave-Front [22][23][24][25] run in parallel. However, if they do not have a uniform structure, that is, it is not possible to change the dependence vectors in a non-uniform pattern using a set of fixed vectors in such a way that they have a uniform pattern, it is practically impossible to use this and other similar methods.…”

Section: Introductionmentioning

confidence: 99%

TLP: Towards three‐level loop parallelisation

Mahjoub

Golsorkhtabaramiri

Amiri

2022

IET Computers & Digital Tech

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Due to the design of computer systems in the multi‐core and/or multi‐processor form, it is possible to use the maximum capacity of processors to run an application with the least time consumed through parallelisation. This is the responsibility of parallel compilers, which perform parallelisation in several steps by distributing iterations between different processors and executing them simultaneously to achieve lower runtime. The present paper focuses on the uniformisation of three‐level perfect nested loops as an important step in parallelisation and proposes a method called Towards Three‐Level Loop Parallelisation (TLP) that uses a combination of a Frog Leaping Algorithm and Fuzzy to achieve optimal results because in recent years, many algorithms have worked on volumetric data, that is, three‐dimensional spaces. Results of the implementation of the TLP algorithm in comparison with existing methods lead to a wide variety of optimal results at desired times, with minimum cone size resulting from the vectors. Besides, the maximum number of input dependence vectors is decomposed by this algorithm. These results can accelerate the process of generating parallel codes and facilitate their development for High‐Performance Computing purposes.

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