CSR: Small: Collaborative Research: Exploring Portable Data Placement on Massively Parallel Platforms with Heterogeneous Memory Architectures

CSR：小型：协作研究：探索具有异构内存架构的大规模并行平台上的便携式数据放置

• 批准号: 1617967
• 负责人: Dong Li
• 金额: $ 30万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617967&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Exploring

Heterogeneous computing is becoming crucial for many computational fields, including simulations of the galaxy, analysis of social networks, modeling of stock transactions, and so on. Programming heterogeneous memory systems is a grand challenge, and creates a major obstacle between heterogeneous hardware and applications because of the programming complexity and fast hardware evolution. This project aims to address this obstacle, and is expected to significantly relieve programmers from handling the underlying memory system heterogeneity. The outcome from this research will also enable continuous enhancement of the computing efficiency of a number of applications on future heterogeneous systems, which is a critical condition for sustained advancement of science, health, security and other aspects of humanity.To address the programming challenges on heterogeneous memory systems, the project investigates a software framework, consisting of a hardware specification language, a set of novel compiler and runtime techniques, and advanced memory performance modeling. The goal is to develop a systematic solution to automatically place data given a complex heterogeneous memory system, especially on massively parallel platforms. With the proposed framework, programmers are relieved from tailoring their programs to different memory systems, and at the same time, the sophisticated memory systems can get fully translated into high computing efficiency. The framework transforms the programs such that they are customized - in terms of where data are placed in memory, when and how to migrate, etc. - to the underlying heterogeneous memory system at runtime and attain a near optimal memory usage.

对于许多计算领域，包括银河系的模拟，社交网络分析，股票交易的建模等，异质计算变得至关重要。编程异质内存系统是一个巨大的挑战，由于编程复杂性和快速硬件的演变，因此在异质硬件和应用程序之间产生了一个主要障碍。该项目旨在解决这一障碍，并有望大大减轻程序员处理潜在的内存系统异质性。这项研究的结果还将能够连续提高许多对未来异质系统的应用的计算效率，这是对人类的持续发展，健康，健康和其他方面的持续发展的关键条件，以解决针对异质记忆系统的编程挑战，该项目的记忆框架调查了一种软件效果，由硬件效果组成的编写型构图和跑步技术组成，并构成了型号的构图，并构成了一组构图，并构成了一组构图。目的是开发系统的解决方案，以自动放置一个数据，给定一个复杂的异质存储系统，尤其是在大规模并行平台上。通过提出的框架，程序员可以从将程序量身定制到不同的内存系统中，同时，复杂的内存系统可以完全转化为高计算效率。 该框架将其自定义的程序转换为将数据放置在存储器中，何时以及如何迁移等方面的方面 - 在运行时将基础异质存储系统转换为基础的异质内存系统，并获得了几乎最佳的内存使用情况。