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Dynamic workload balancing deques for branch and bound algorithms in the message passing interface

机译:消息传递界面中分支和绑定算法的动态工作负载平衡双端队列

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摘要

The message passing interface (MPI) is the standard in message passing parallel computation. MPI does not provide a canonical way to dynamically distribute run-time generated workload evenly across all the participating computer nodes. This paper proposes a MPI library to provide near-optimal dynamical workload balancing over branch and bound (B&B) algorithms; B&B potentially produces huge workload unbalance during a parallel execution. The library, named RaWSDM, provides a double ended queue (deque) data structure on which the programmer may pop, process, and later, pull back some parallel tasks; an underlying efficient system scheduler is responsible for keeping the workload balanced by exchanging elements among all deques. Theoretical bounds are traced and practical experiments are performed with the unlimited knapsack problem. Results show a performance gain up to 80% (best-case scenario) against a pure MPI implementation using round-robin scheduling, with near linear speedup and memory consumption.
机译:消息传递接口(MPI)是消息传递并行计算的标准。 MPI没有提供规范的方法来在所有参与的计算机节点之间平均动态分布运行时生成的工作负载。本文提出了一种MPI库,以通过分支定界(B&B)算法提供接近最佳的动态工作负载平衡。 B&B在并行执行期间可能会导致巨大的工作负载不平衡。这个名为RaWSDM的库提供了一个双端队列(双端队列)数据结构,程序员可以在该结构上弹出,处理以及稍后撤回一些并行任务。底层高效的系统调度程序负责通过在所有双端队列之间交换元素来保持工作负载平衡。追溯理论界限,并针对无限背包问​​题进行了实际实验。结果表明,与使用轮询调度的纯MPI实现相比,性能提升高达80%(最佳情况),并且线性加速和内存消耗接近线性。

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