OpenMP中多目标工作共享的位置感知内存关联

T. Scogland, W. Feng
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引用次数: 0

摘要

异构性是计算领域不断增长的挑战。最明显的例子是gpu的日益普及,以及Intel Xeon Phi等专门设计的协处理器。即使不考虑协处理器,异构性也会随着CPU核数、自适应单核频率以及日益分层和复杂的内存系统的增加而继续增加。以具有四个内存节点的系统为例,每个节点与四个内核相关联,还有四个gpu,每个gpu都有不同的地址空间,并且像批量同步并行集群一样编程了数十到数百个内核。在这种情况下,我们在每个节点上有效地编程微型星座集群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Locality-aware memory association for multi-target worksharing in OpenMP
Heterogeneity is an ever-growing challenge in computing. The clearest example is the increasing popularity of GPUs, and purpose-designed coprocessors such as Intel Xeon Phi. Even disregarding coprocessors, heterogeneity continues to increase with the rise in CPU core counts, adaptive per-core frequencies, and increasingly hierarchical and complex memory systems. Take a system with four memory nodes, associated with four cores each, and four GPUs, each with a distinct address space and tens to hundreds of cores pro­grammed like a bulk-synchronous parallel cluster. In this case, we are effectively programming clusters of miniature constellations in every node.
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