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Analyzing and Improving Memory Access Patterns of Large Irregular Applications on NUMA Machines

机译:分析和改善NUMA机器上大型不规则应用程序的内存访问模式

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Improving the memory access behavior of parallel applications is one of the most important challenges in high-performance computing. Non-Uniform Memory Access (NUMA) architectures pose particular challenges in this context: they contain multiple memory controllers and the selection of a controller to serve a page request influences the overall locality and balance of memory accesses, which in turn affect performance. In this paper, we analyze and improve the memory access pattern and overall memory usage of large-scale irregular applications on NUMA machines. We selected HashSieve, a very important algorithm in the context of lattice-based cryptography, as a representative example, due to (1) its extremely irregular memory pattern, (2) large memory requirements and (3) unsuitability to other computer architectures, such as GPUs. We optimize HashSieve with a variety of techniques, focusing both on the algorithm itself as well as the mapping of memory pages to NUMA nodes, achieving a speedup of over 2x.
机译:改善并行应用程序的内存访问行为是高性能计算中最重要的挑战之一。在这种情况下,非统一内存访问(NUMA)架构提出了特殊的挑战:它们包​​含多个内存控制器,为页面请求提供服务的控制器的选择会影响内存访问的整体位置和平衡,进而影响性能。在本文中,我们分析并改善了NUMA计算机上大型不规则应用程序的内存访问模式和整体内存使用率。我们选择HashSieve(在基于格的​​密码学中非常重要的算法)作为代表示例,原因是:(1)它的存储模式极其不规则,(2)内存需求大,(3)不适合其他计算机体系结构,例如作为GPU。我们使用多种技术优化HashSieve,同时将重点放在算法本身以及将内存页映射到NUMA节点上,从而实现超过2倍的加速。

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