Tuning and hybrid parallelization of a genetic-based multi-point statistics simulation code
10.1016/j.parco.2014.04.005
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/84769
Tipus de documentArticle
Data publicació2014-05
EditorElsevier
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
Abstract
One of the main difficulties using multi-point statistical (MPS) simulation based on annealing techniques or genetic algorithms concerns the excessive amount of time and memory that must be spent in order to achieve convergence. In this work we propose code optimizations and parallelization schemes over a genetic-based MPS code with the aim of speeding up the execution time. The code optimizations involve the reduction of cache misses in the array accesses, avoid branching instructions and increase the locality of the accessed data. The hybrid parallelization scheme involves a fine-grain parallelization of loops using a shared-memory programming model (OpenMP) and a coarse-grain distribution of load among several computational nodes using a distributed-memory programming model (MPI). Convergence, execution time and speed-up results are presented using 2D training images of sizes 100 × 100 × 1 and 1000 × 1000 × 1 on a distributed-shared memory supercomputing facility.
CitacióPeredo, Oscar [et al.]. Tuning and hybrid parallelization of a genetic-based multi-point statistics simulation code. "Parallel Computing", Maig 2014, vol. 40, núm. 5-6, p. 144-158.
ISSN0167-8191
Versió de l'editorhttp://www.sciencedirect.com/science/article/pii/S0167819114000489
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
Tuning and hybr ... f a genetic-based code.pdf | 991,2Kb | Visualitza/Obre |