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.

异构计算在银河系模拟、社交网络分析、股票交易建模等领域都有着重要的应用前景，但由于编程的复杂性和硬件的快速发展，异构存储系统的编程是一个巨大的挑战，也是异构硬件和应用之间的一个主要障碍。该项目旨在解决这一障碍，并有望大大减轻程序员处理底层内存系统异构性的负担。这项研究的成果也将使未来异构系统上的许多应用程序的计算效率不断提高，这是科学、健康、安全和人类其他方面持续进步的关键条件。为了解决异构存储系统上的编程挑战，该项目研究了一个软件框架，包括硬件规范语言，一组新颖的编译器和运行时技术，以及高级内存性能建模。我们的目标是开发一个系统的解决方案，自动放置数据给定一个复杂的异构内存系统，特别是在大规模并行平台上。通过该框架，程序员不必为不同的存储系统定制程序，同时，复杂的存储系统可以充分转化为高计算效率。 该框架转换的程序，使他们定制-在哪里数据被放置在内存中，何时以及如何迁移等-在运行时的底层异构存储器系统，并获得一个接近最佳的内存使用。