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SharP Unified Memory Allocator: An Intent-Based Memory Allocator for Extreme-Scale Systems

机译:尖锐的统一内存分配器:用于极度级系统的基于意图的内存分配器

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The pre-exascale systems will soon be deployed with a deep, complex memory hierarchy composed of many heterogeneous memories. This presents multiple challenges for users including: how to allocate data objects with locality between memories and devices for the various memories in these systems, which includes DRAM, High-bandwidth Memory (HBM), and non-volatile random access memory (NVRAM), and how to perform these allocations while providing portability for their application. Currently, the user can make use of multiple, disjoint libraries to allocate data objects on these memories. However, it is difficult to obtain locality between memories and devices when using libraries that are unaware of each other. This paper presents the Unified Memory Allocator (UMA) of the SHARed data-structure centric Programming abstraction (SharP) library, which provides a unified interface for memory allocations across DRAM, HBM, and NVRAM and is extensible to support future memory types. In addition, the SharP UMA allows for portability between systems by supporting both explicit and implicit, intent-based memory allocations. To demonstrate the ease of use of the SharP UMA, we have extended both Open MPI and OpenSHMEM-Xto support SharP. We validate this work by evaluating the performance implications and intent-based approach with synthetic benchmarks as well as adaptations of the Graph500 benchmark.
机译:exaSAsale系统将很快与由许多异构存储器组成的深层复杂的内存层次进行部署。这为用户提供了多种挑战,包括:如何将数据对象与这些系统中的各种存储器之间的存储器和设备之间的位置分配数据对象,其包括DRAM,高带宽存储器(HBM)和非易失性随机存取存储器(NVRAM),以及如何在为其应用程序提供可移植性的同时执行这些分配。目前,用户可以使用多个不相交的库来在这些存储器上分配数据对象。然而,当使用彼此不知道的库时,难以获得存储器和设备之间的局部性。本文介绍了共享数据结构中心编程抽象(SHARP)库的统一内存分配器(UMA),它提供了DRAM,HBM和NVRAM的内存分配的统一接口,并且可扩展以支持未来的内存类型。此外,夏普UMA允许通过支持显式和隐式的基于意图的内存分配来实现系统之间的可移植性。为了展示夏普UMA的易用性,我们展开了Open MPI和OpenShmem-XTo支持夏普。我们通过评估具有合成基准的绩效影响和基于意图的方法以及Graph500基准测试的适应来验证这项工作。

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