CCF:Small:Collaborative Research: Taowu: A Heterogeneous Processing-in-Memory for High Performance Scientific Applications

CCF:Small：合作研究：Taowu：用于高性能科学应用的异构内存处理

• 批准号: 1718194
• 负责人: Dong Li
• 金额: $ 28.2万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718194&HistoricalAwards=false
• 关键词: CCF; Small; Collaborative; Research; Taowu

Many scientific computing applications in critical areas of research, such as nanotechnology, astrophysics, climate, bioinformatics, and high-energy physics, are becoming more data intensive than ever before. The volume of the data and the pressure on the runtime system capability of supporting data intensive operations substantially increases over the time. This project introduces and optimizes data processing capabilities within memory. The project provides a fundamental change to data management and program optimization, and brings promising performance and energy benefits. The project will significantly advance simulation capabilities of scientific applications, especially those with intensive data processing. The goal of the project is to enable high-performance, energy-efficient, and flexible processing-in-memory design, which is adaptive to the irregular, diverse, and changing behaviors among data intensive scientific applications. To achieve the goal, a heterogeneous processing-in-memory design, built up with fixed-function processing-in-memory and general programmable processing-in-memory, is introduced. The project explores a series of critical questions for building emerging processing-in-memory, including heterogeneous processing-in-memory architecture, processing-in-memory programming models, runtime design, and the implications of processing-in-memory on high performance scientific applications. The project will significantly advance the knowledge to build processing-in-memory, and pave the way to integrate it into the existing and future systems.

在关键研究领域的许多科学计算应用，例如纳米技术，天体物理学，气候，生物信息学和高能物理学，都比以往任何时候都变得更加密集。数据密集操作的数据量和运行时系统的压力在整个时间内显着增加。该项目在内存中介绍并优化了数据处理功能。该项目为数据管理和计划优化提供了根本性的更改，并带来了有希望的绩效和能源收益。该项目将大大提高科学应用的模拟功能，尤其是具有密集数据处理的应用程序。 该项目的目的是实现高性能，节能和灵活的内存处理设计，该设计适应数据密集型科学应用之间的不规则，多样化和不断变化的行为。为了实现目标，介绍了由固定功能的处理和通用可编程处理中的内存过程中的异构处理设计。该项目探讨了一系列有关构建新兴过程的关键问题，包括内存架构中的异质处理，内存编程模型，运行时设计以及内存处理对高性能科学应用的含义。该项目将大大提高知识，以构建内存处理，并为将其集成到现有系统和未来系统中铺平道路。

项目成果

MD-HM: Memoization-based Molecular Dynamics Simulations on Big Memory System

• DOI: 10.1145/3447818.3460365
• 发表时间: 2021-01-01
• 期刊: PROCEEDINGS OF THE 2021 ACM INTERNATIONAL CONFERENCE ON SUPERCOMPUTING, ICS 2021
• 作者: Xie，Zhen;Dong，Wenqian;Li，Dong
• 通讯作者: Li，Dong

GMOD: a dynamic GPU memory overflow detector

• DOI: 10.1145/3243176.3243194
• 发表时间: 2018-11
• 期刊: Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques
• 作者: Bang Di;Jianhua Sun;Dong Li;Hao Chen;Zhe Quan

Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training

• DOI: 10.1109/ipdps.2019.00029
• 发表时间: 2018-10
• 期刊: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Jiawen Liu;Dong Li;Gokcen Kestor;J. Vetter

Adaptive neural network-based approximation to accelerate eulerian fluid simulation

• DOI: 10.1145/3295500.3356147
• 发表时间: 2019-11
• 期刊: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Wenqian Dong;Jie Liu;Zhen Xie;Dong Li

Optimizing large-scale plasma simulations on persistent memory-based heterogeneous memory with effective data placement across memory hierarchy

通过跨内存层次结构的有效数据放置，优化基于持久内存的异构内存的大规模等离子体模拟

• DOI: 10.1145/3447818.3460356
• 发表时间: 2021
• 期刊: ACM International Conference on Supercomputing
• 作者: Ren, Jie;Luo, Jiaolin;Peng, Ivy;Wu, Kai;Li, Dong
• 通讯作者: Li, Dong