面向可伸缩并行计算的高效多维数据重分布
Efficient Multidimensional Data Redistribution for Resizable Parallel Computations
摘要：
在集群超级计算机上运行的传统并行处理器仅支持静态调度，其中分配给应用程序的处理器数量在作业的执行过程中保持固定。这导致空闲系统资源的利用不足，从而降低了整体系统吞吐量。在我们的研究中，我们已经开发了一个原型框架称为ReSHAPE，它支持在分布式内存平台上执行的并行MPI应用程序的动态编译。ReSHAPE中的MySQL库支持释放和获取处理器，并有效地将应用程序状态重新分发到一组新的处理器。在本文中，我们推导出一个算法重新分配二维块循环数组从$P$到$Q$处理器，组织为2-D处理器网格。如果$P_r \leq Q_r$和$P_c \leq Q_c$，则该算法确保用于数据重新分配的无竞争通信调度。在其他情况下，该算法在通信调度上实现循环行和列移位，以最小化节点争用。
英文摘要：
Traditional parallel schedulers running on cluster supercomputers support only static scheduling, where the number of processors allocated to an application remains fixed throughout the execution of the job. This results in under-utilization of idle system resources thereby decreasing overall system throughput. In our research, we have developed a prototype framework called ReSHAPE, which supports dynamic resizing of parallel MPI applications executing on distributed memory platforms. The resizing library in ReSHAPE includes support for releasing and acquiring processors and efficiently redistributing application state to a new set of processors. In this paper, we derive an algorithm for redistributing two-dimensional block-cyclic arrays from $P$ to $Q$ processors, organized as 2-D processor grids. The algorithm ensures a contention-free communication schedule for data redistribution if $P_r \leq Q_r$ and $P_c \leq Q_c$. In other cases, the algorithm implements circular row and column shifts on the communication schedule to minimize node contention.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
