BinLPT: A Novel Workload-Aware Loop Scheduler for Irregular Parallel Loops

Abstract : Workload-aware loop schedulers were introduced to deliver better performance than classical strategies, but they present limitations on work-load estimation, chunk scheduling and integrability with applications. Targeting these challenges, in this work we propose a novel workload-aware loop sched-uler that is called BinLPT and it is based on three features. First, it relies on some user-supplied estimation of the workload of the target parallel loop. Second , BinLPT uses a greedy bin packing heuristic to adaptively partition the iteration space in several chunks. The maximum number of chunks to be produced is a parameter that may be fine-tuned. Third, it schedules chunks of iterations using a hybrid scheme based on the LPT rule and on-demand scheduling. We integrated BinLPT in OpenMP, and we evaluated its performance in a large-scale NUMA machine using a synthetic kernel and 3D N-Body Simulations. Our results revealed that BinLPT improves performance over OpenMP's strategies by up to 45.13% and 37.15% in the synthetic and application kernels, respectively.
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Contributor : Pedro Henrique Penna <>
Submitted on : Wednesday, September 27, 2017 - 3:43:27 PM
Last modification on : Monday, July 8, 2019 - 3:10:12 PM
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Pedro Henrique Penna, Márcio Castro, Patrícia Plentz, Henrique Cota de Freitas, François Broquedis, et al.. BinLPT: A Novel Workload-Aware Loop Scheduler for Irregular Parallel Loops. Simpósio em Sistemas Computacionais de Alto Desempenho, Oct 2017, Campinas, Brazil. ⟨hal-01596427⟩



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