NumaMMA: NUMA MeMory Analyzer
NumaMMA: NUMA MeMory Analyzer
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NumaMMA：NUMA 内存分析器
DOI：
10.1145/3225058.3225094
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
K. Marquet
中科院分区：
文献类型：
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作者：
François Trahay;Manuel Selva;L. Morel;K. Marquet
非统一内存访问(NUMA)体系结构目前普遍用于运行高性能计算(HPC)应用程序。在这样的体系结构中，几个不同的物理内存被组合在一起，以创建单个共享内存。然而，由于存在多个物理存储器，因此根据执行存储器请求的核的位置和目标存储器的位置，对这些存储器的访问时间并不一致。因此，线程和数据放置对于有效利用此类体系结构至关重要。为了帮助做出关于此放置的决策，需要分析工具。在这项工作中，我们提出了NUMA内存分析器(NumaMMA)，这是一个新的分析工具，用于了解高性能计算应用程序的内存访问模式。NumaMMA将使用硬件机制的高效内存跟踪收集与原始可视化方法相结合，允许查看内存访问模式如何随时间演变。NumaMMA报告的信息允许理解应用程序分配的每个对象中这些访问模式的性质。我们展示了NumaMMA如何帮助了解几个HPC应用程序的内存模式，以便对它们进行优化，并比标准的非优化版本获得高达28%的加速。
Non Uniform Memory Access (NUMA) architectures are nowadays common for running High-Performance Computing (HPC) applications. In such architectures, several distinct physical memories are assembled to create a single shared memory. Nevertheless, because there are several physical memories, access times to these memories are not uniform depending on the location of the core performing the memory request and on the location of the target memory. Hence, threads and data placement are crucial to efficiently exploit such architectures. To help in taking decision about this placement, profiling tools are needed. In this work, we propose NUMA MeMory Analyzer (NumaMMA), a new profiling tool for understanding the memory access patterns of HPC applications. NumaMMA combines efficient collection of memory traces using hardware mechanisms with original visualization means allowing to see how memory access patterns evolve over time. The information reported by NumaMMA allows to understand the nature of these access patterns inside each object allocated by the application. We show how NumaMMA can help understanding the memory patterns of several HPC applications in order to optimize them and get speedups up to 28% over the standard non optimized version.