Northup: Divide-and-Conquer Programming in Systems with Heterogeneous Memories and Processors

2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2019-05-20 DOI:10.1109/IPDPS.2019.00043

Shuai Che, Jieming Yin

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引用次数: 2

Abstract

In recent years we have seen rapid development in both frontiers of emerging memory technologies and accelerator architectures. Future memory systems are becoming deeper and more heterogeneous. Adopting NVM and die-stacked DRAM on each HPC node is a new trend of development. On the other hand, GPUs and many-core processors have been widely deployed in today's supercomputers. However, software for programming and managing a system that consists of heterogeneous memories and processors is still in its very early stage of development. How to exploit such deep memory hierarchy and heterogeneous processors with minimal programming effort is an important issue to address. In this paper, we propose Northup, a programming and runtime framework, using a divide-and-conquer approach to map an application efficiently to heterogeneous systems. The proposed solution presents a portable layer that abstracts the system architecture, providing flexibility to support easy integration of new memories and processor nodes. We show that Northup out-of-core execution with SSD is only an average of 17% slower than in-memory processing for the evaluated applications.

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具有异构存储器和处理器的系统中的分而治之编程

近年来，我们看到了新兴存储技术和加速器架构的快速发展。未来的存储系统将变得更加深入和异构。在每个高性能计算节点上采用NVM和模堆叠DRAM是一个新的发展趋势。另一方面，gpu和多核处理器已经广泛应用于今天的超级计算机中。然而，用于编程和管理由异构存储器和处理器组成的系统的软件仍处于开发的早期阶段。如何以最少的编程努力利用这种深层内存层次结构和异构处理器是需要解决的重要问题。在本文中，我们提出了Northup，一个编程和运行时框架，使用分而治之的方法将应用程序有效地映射到异构系统。提出的解决方案提出了一个抽象系统架构的可移植层，提供灵活性以支持新存储器和处理器节点的轻松集成。我们表明，在评估的应用程序中，使用SSD执行Northup的核外处理只比内存内处理平均慢17%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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