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

机译:SharP统一内存分配器:用于超大规模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 MP/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:百亿年前级的系统将很快部署有由许多异构内存组成的深层次,复杂的内存层次结构。这给用户带来了许多挑战,其中包括:如何在这些系统中的各种存储器(包括DRAM,高带宽存储器(HBM)和非易失性随机存取存储器(NVRAM))的存储器和设备之间分配具有局部性的数据对象,以及如何执行这些分配,同时为其应用程序提供可移植性。当前,用户可以使用多个不相交的库在这些存储器上分配数据对象。但是,当使用彼此不知道的库时,很难在存储器和设备之间获得局部性。本文介绍了SHARed以数据结构为中心的编程抽象(SharP)库的统一内存分配器(UMA),该库为DRAM,HBM和NVRAM上的内存分配提供了统一的接口,并且可扩展以支持将来的内存类型。此外,SharP UMA通过支持显式和隐式的基于意图的内存分配,从而允许在系统之间进行可移植性。为了演示SharP UMA的易用性,我们扩展了Open MP /和OpenSHMEM-X以支持SharP。我们通过使用综合基准以及对Graph500基准的改编评估性能影响和基于意图的方法来验证这项工作。

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