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Parallel Branch-and-Bound in Multi-core Multi-CPU Multi-GPU Heterogeneous Environments

Trong-Tuan Vu 1 Bilel Derbel 1 
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : We investigate the design of parallel B&B in large scale heterogeneous compute environments where processing units can be composed of a mixture of multiple shared memory cores, multiple distributed CPUs and multiple GPUs devices. We describe two approaches addressing the critical issue of how to map B&B workload with the different levels of parallelism exposed by the target compute platform. We also contribute a throughout large scale experimental study which allows us to derive a comprehensive and fair analysis of the proposed approaches under different system configurations using up to 16 GPUs and up to 512 CPU-cores. Our results shed more light on the main challenges one has to face when tackling B&B algorithms while describing efficient techniques to address them. In particular, we are able to obtain linear speed-ups at moderate scales where adaptive load balancing among the heterogeneous compute resources is shown to have a significant impact on performance. At the largest scales, intra-node parallelism and hybrid decentralized load balancing is shown to have a crucial importance in order to alleviate locking issues among shared memory threads and to scale the distributed resources while optimizing communication costs and minimizing idle times
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Submitted on : Tuesday, September 23, 2014 - 9:21:23 PM
Last modification on : Friday, July 8, 2022 - 10:07:35 AM
Long-term archiving on: : Friday, April 14, 2017 - 4:19:30 PM


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Trong-Tuan Vu, Bilel Derbel. Parallel Branch-and-Bound in Multi-core Multi-CPU Multi-GPU Heterogeneous Environments. Future Generation Computer Systems, 2016, 56, pp.95-109. ⟨10.1016/j.future.2015.10.009⟩. ⟨hal-01067662⟩



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