Parallel Genetic Algorithm for Optimizing Compiler Sequences Ordering

Almohammed, Manal H.; Fanfakh, Ahmed; Alwan, Esraa H.

doi:10.1007/978-3-030-55340-1_9

Cited by 2 publications

(3 citation statements)

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“…To compute the entire runtimes, we employed the UNIX time command and performed five trials to obtain an average. Figure (2) illustrates the training set programs, or dataset, and displays the effects of these factors on program execution time compared to the normal case, i.e., no loop unrolling. The programs achieved speedups of 13%, 18%, 19%, and 21% for loop unroll factors of 2, 4, 6, and 8, respectively.…”

Section: Training Set Programsmentioning

confidence: 99%

“…Therefore, directing efforts towards enhancing the frequently executed portions of the code can have a substantial impact on the overall program execution time. [1,2] As a result, code optimization techniques that accelerate loop execution are essential. [3,4] One technique for improving program execution time is loop unrolling.…”

Section: Introductionmentioning

confidence: 99%

“…This is achieved by reducing the number of branch instructions needed at the end of the loop body. [5,2] Loop unrolling is essential for certain optimizations, particularly those aimed at improving the memory system. Enabling loops unroll, generates numerous static memory instructions that can be rescheduled to take advantage of memory locality.…”

Section: Introductionmentioning

confidence: 99%

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2023

Inf. Sci. Lett.

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Loop unrolling is a well-established code transformation technique that can improve the performance of a program at runtime. The key benefit of unrolling a loop is that it often requires fewer instruction executions than the original loop. However, determining the optimal number of loop unrolling is a critical concern. This paper presents a novel method for predicting the optimal unroll factor for a given program. Specifically, a dataset is constructed that includes the execution times of several programs with varying loop unroll factors. The programs are sourced from different benchmarks, such as Ploybench, Shooutout, and other programs. Similarity measures between the unseen program and the existing programs are computed, and the three most similar programs are identified. The unroll factor that led to the greatest reduction in execution time for the most similar programs is selected as the candidate for the unseen program. Experimental results demonstrate that the proposed method can enhance the performance of training programs for unroll factors of 2, 4, 6, and 8 by approximately 13%, 18%, 19%, and 21%, respectively. For the unseen programs, the speedup rate is approximately 37.7% for five programs.

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Section: Training Set Programsmentioning

confidence: 99%