Collaborative Research:XPS:CLCCA: Performance Portable Abstractions for Large-Scale Irregular Computations

合作研究：XPS：CLCCA：大规模不规则计算的性能便携式抽象

• 批准号: 1337224
• 负责人: Sandhya Dwarkadas
• 金额: $ 44.99万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1337224&HistoricalAwards=false
• 关键词: Collaborative; Research; XPS; CLCCA; Performance

Ongoing technology trends are accelerating scientific discovery by allowing researchers to generate enormous quantities of data, in domains ranging from computational biology to social networks. There is an urgent need to make it easy and fast to extract useful content from this data using appropriate abstractions and parallel runtimes. Work conducted under this project aims to make "big data" computing more readily available to applications with dynamic structure and irregular dependencies, thereby enabling advances in scientific computing in general and computational biology in particular.This project extends the state of the art in scientific computing by developing programming abstractions to expose -- and run-time optimizations to exploit -- the parallelism available in large, irregular applications. Parallelism is essential for the extraction of useful information from ever increasing volumes of scientific data, but the irregularity of data structure and access in many problem domains makes efficient parallelization difficult. At the level of the programming model, the project addresses the challenge of irregularity by identifying design patterns for important new classes of applications -- in particular, those that use trees and graphs for data representation and access but demonstrate some structure in the traversal. At the level of the run-time system, it is developing computational engines that support and exploit the new patterns, leveraging the structure exposed to automatically and dynamically map computational tasks to hardware nodes.

当前的技术趋势正在加速科学发现，使研究人员能够在从计算生物学到社交网络的各个领域生成大量数据。 迫切需要使用适当的抽象和并行运行时，使其容易和快速地从这些数据中提取有用的内容。 该项目旨在使“大数据”计算更容易用于具有动态结构和不规则依赖关系的应用程序，从而促进一般科学计算，特别是计算生物学的发展。该项目通过开发编程抽象来暴露--和运行时优化来利用--大规模并行性，不规则的应用 并行化对于从不断增加的科学数据中提取有用的信息是必不可少的，但是在许多问题领域中数据结构和访问的不规则性使得高效的并行化变得困难。在编程模型层面，该项目通过为重要的新应用程序类别确定设计模式来解决不规则性的挑战-特别是那些使用树和图进行数据表示和访问，但在遍历中展示了一些结构的应用程序。 在运行时系统层面，它正在开发支持和利用新模式的计算引擎，利用暴露的结构自动和动态地将计算任务映射到硬件节点。

项目成果

Interval-based memory reclamation

基于间隔的内存回收

• DOI: 10.1145/3178487.3178488
• 发表时间: 2018
• 期刊: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
• 作者: Wen, Haosen;Izraelevitz, Joseph;Cai, Wentao;Beadle, H. Alan;Scott, Michael L.
• 通讯作者: Scott, Michael L.

iDO: Compiler-Directed Failure Atomicity for Nonvolatile Memory

• DOI: 10.1109/micro.2018.00029
• 发表时间: 2018-10
• 期刊: 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)
• 作者: Qingrui Liu;Joseph Izraelevitz;Se Kwon Lee;M. Scott;S. Noh;Changhee Jung

Leveraging hardware TM in Haskell

在 Haskell 中利用硬件 TM

• DOI: 10.1145/3293883.3295711
• 发表时间: 2019
• 期刊: Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming
• 作者: Yates, Ryan;Scott, Michael L.
• 通讯作者: Scott, Michael L.