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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

基本信息

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

项目摘要

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，如纳米技术、天体物理学、气候、生物信息学和高能物理学，正变得比以往任何时候都更加数据密集。随着时间的推移，数据量和对支持数据密集型操作的运行时系统能力的压力显著增加。该项目引入并优化了内存中的数据处理能力。该项目为数据管理和程序优化提供了根本性的改变，并带来了有前途的性能和能源效益。该项目将大大提高科学应用的模拟能力，特别是那些需要密集数据处理的应用。该项目的目标是实现高性能、高能效和灵活的内存处理设计，以适应数据密集型科学应用中不规则、多样和不断变化的行为。为了实现这一目标，本文提出了一种由固定功能内存处理器和通用可编程内存处理器组成的异构内存处理器设计。该项目探讨了构建新兴内存处理的一系列关键问题，包括异构内存处理架构，内存处理编程模型，运行时设计以及内存处理对高性能科学应用的影响。该项目将大大推进构建内存处理的知识，并为将其集成到现有和未来的系统中铺平道路。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
作者：
Bang Di;Jianhua Sun;Dong Li;Hao Chen;Zhe Quan
通讯作者：
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)
影响因子：
0
作者：
Jiawen Liu;Dong Li;Gokcen Kestor;J. Vetter
通讯作者：
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
影响因子：
0
作者：
Wenqian Dong;Jie Liu;Zhen Xie;Dong Li
通讯作者：
Wenqian Dong;Jie Liu;Zhen Xie;Dong Li

Runtime Data Management on Non-Volatile Memory-based Heterogeneous Memory for Task-Parallel Programs

DOI：
10.1109/sc.2018.00034
发表时间：
2018-11
期刊：
SC18: International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子：
0
作者：
Kai Wu;J. Ren;Dong Li
通讯作者：
Kai Wu;J. Ren;Dong Li

A simple analytical model of complex wall in multibody dissipative particle dynamics