.png){kind=icon}
返回
Parallel Branch-and-Bound in multi-core multi-CPU multi-GPU heterogeneous environments
.png){kind=icon}
OA
AI
DOI：10.1016/j.future.2015.10.009.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 a 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 distributed 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. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Parallel distributed B&B
Load balancing
Large scale heterogeneous distributed systems
Multi-core multi-CPU multi-GPU systems
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
F
IF:
6.1
论文数:
6.9K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
Combining multi-core and GPU computing for solving combinatorial optimization problems结合多核和GPU计算求解组合优化问题
PREAI
分享收藏
查看更多内容