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Sadeghi, Morteza, Torabi, Abdolreza. (1403). ACCELERATION OF NEAR FIELD COMPUTATION IN MLFMA ON A SINGLE GPU BY GENERATING REDUNDANCY IN DATA. , 56(2), 89-112. doi: 10.22059/jac.2025.387101.1219
Morteza Sadeghi; Abdolreza Torabi. "ACCELERATION OF NEAR FIELD COMPUTATION IN MLFMA ON A SINGLE GPU BY GENERATING REDUNDANCY IN DATA". , 56, 2, 1403, 89-112. doi: 10.22059/jac.2025.387101.1219
Sadeghi, Morteza, Torabi, Abdolreza. (1403). 'ACCELERATION OF NEAR FIELD COMPUTATION IN MLFMA ON A SINGLE GPU BY GENERATING REDUNDANCY IN DATA', , 56(2), pp. 89-112. doi: 10.22059/jac.2025.387101.1219
Sadeghi, Morteza, Torabi, Abdolreza. ACCELERATION OF NEAR FIELD COMPUTATION IN MLFMA ON A SINGLE GPU BY GENERATING REDUNDANCY IN DATA. , 1403; 56(2): 89-112. doi: 10.22059/jac.2025.387101.1219

ACCELERATION OF NEAR FIELD COMPUTATION IN MLFMA ON A SINGLE GPU BY GENERATING REDUNDANCY IN DATA

Journal of Algorithms and Computation
دوره 56، شماره 2، اسفند 2024، صفحه 89-112    اصل مقاله (1.43 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2025.387101.1219
نویسندگان
Morteza Sadeghi1؛ Abdolreza Torabi* 2
1PhD Student, Department of Algorithm and Computations, University of Tehran
2Department of Algorithm and Computations, Engineering Science, University of Tehran, Tehran, Iran
چکیده
Improving efficiency of multi-level fast multi-pole algorithm (MLFMA) on distributed and parallel systems has been vastly studied, specially for GPUs. Unlike the far-field computation, acceleration of near-field computation in MLFMA algorithm on GPUs was of less concern in the literature, however there are some solutions that exploited special specifications of GPU’s memory. This article proposes data replication for P2P operator and uses analytical performance models to determine its optimality criteria. By modelling the speedup, we found that making threads independence by creating redundancy in the data makes the algorithm for lower dense problems nearly 13 times faster than non-redundant mode.
کلیدواژه‌ها
Multilevel Fast Multi-Pole Algorithm؛ MLFMA؛ Graphics Processors؛ GPU؛ Performance Evaluation؛ Parallel Processing
آمار
تعداد مشاهده مقاله: 37
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