SHF: Small: Parallel Algorithms and Architectures Enabling Extreme-scale Graph Analytics for Biocomputing Applications

SHF：小型：并行算法和架构为生物计算应用提供超大规模图形分析

基本信息

批准号：
1815467
负责人：
Anantharaman Kalyanaraman
金额：
$ 50.97万
依托单位：
Washington State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815467&HistoricalAwards=false
关键词：
SHF Small Parallel Algorithms Architectures

项目摘要

Graph-theoretic modeling of biological data has a rich history of delivering foundational scientific knowledge and breakthrough discoveries. As data sets continue to explode both in size and complexity, the combination of graph analytics and scalable (parallel) computing has a critical role to play in shaping the future of data-driven discovery in many biological applications including national health. Yet, implementing such graph computations at scale continues to be a daunting challenge despite the growing availability of high-end parallel architectures. The goal of this project is to design efficient parallel algorithms and architectures that would enable extreme scaling of graph computations in biological applications. Other project activities integrate and leverage upon the research outcomes of this project, while preparing the next generation scientific workforce. The project is also leading to the development of curricular modules in parallel algorithms and applications, and related hardware design, and conference tutorials for broader outreach.The project is focused on developing core techniques in two problem spaces: i) performing graph analytics at scale for a host of generic graph operations that find prevalent use-cases in biological applications and also in many other data-driven domains; and ii) performing graph construction at scale using biological raw data. Taken together, the proposed effort embodies a systematic and holistic approach to enhance the reach and impact of parallel computing on large-scale graph applications and, in the process, usher in new generic data-driven design techniques and paradigms into parallel applications design. While the emphasis will be on biological applications, as a space for drawing scientific motivation and to demonstrate utility through validation and testing, it is expected that many of the developed techniques will extend beyond this realm and impact a broader class of applications that need extreme-scale processing of graphs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生物数据的图理论建模具有丰富的历史，可以提供基础科学知识和突破性发现。随着数据集的规模和复杂性继续爆炸，图形分析和可扩展（并行）计算的组合在塑造包括国家健康在内的许多生物学应用中塑造数据驱动发现的未来中起着至关重要的作用。然而，尽管高端平行体系结构的可用性日益增加，但在大规模实施此类图表仍然是一个艰巨的挑战。该项目的目的是设计有效的并行算法和体系结构，这些算法和体系结构将使生物应用中的图形计算极端缩放。其他项目活动在准备下一代科学劳动力的同时，将该项目的研究成果纳入并利用。该项目还导致了平行算法和应用程序以及相关的硬件设计以及针对更广泛范围的会议教程的开发。该项目的重点是在两个问题空间中开发核心技术：i）在大规模的图形操作中执行图形分析，以便在许多一般的生物学应用程序中，以及许多其他的生物学应用程序以及许多其他数据，以及许多其他数据。和ii）使用生物原始数据大规模执行图形结构。综上所述，提议的努力体现了一种系统和整体方法，以增强并行计算对大规模图应用应用程序的影响和影响，并在此过程中使用新的通用数据驱动的设计技术，并将范式纳入并行应用设计中。尽管重点将放在生物应用上，但作为吸引科学动机并通过验证和测试来证明实用性的空间，预计许多已发达的技术将超越这个领域，并影响更广泛的应用程序，需要更广泛的图形处理，这些奖项对NSF的法定任务进行了审查，反映了众所周知的Intelpriatia and Intelliacia and Infectia and Infectia and Infectiac and Infectiac and Intelliqual and the Interviac and the Interviac and the Internial。

项目成果

期刊论文数量（23）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

GraphIte: Accelerating Iterative Graph Algorithms on ReRAM Architectures via Approximate Computing

DOI：
10.23919/date56975.2023.10137001
发表时间：
2023-04
期刊：
2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
影响因子：
0
作者：
Dwaipayan Choudhury;A. Kalyanaraman;P. Pande
通讯作者：
Dwaipayan Choudhury;A. Kalyanaraman;P. Pande

High-Performance and Energy-Efficient 3D Manycore GPU Architecture for Accelerating Graph Analytics

DOI：
10.1145/3482880
发表时间：
2021-10
期刊：
ACM J. Emerg. Technol. Comput. Syst.
影响因子：
0
作者：
Dwaipayan Choudhury;Aravind Sukumaran-Rajam;Anantharaman Kalyanaraman;P. Pande
通讯作者：
Dwaipayan Choudhury;Aravind Sukumaran-Rajam;Anantharaman Kalyanaraman;P. Pande

Accelerating Graph Computations on 3D NoC-enabled PIM Architectures

加速支持 3D NoC 的 PIM 架构上的图形计算

DOI：
10.1145/3564290
发表时间：
2022
期刊：
ACM Transactions on Design Automation of Electronic Systems
影响因子：
1.4
作者：
Choudhury, Dwaipayan;Xiang, Lizhi;Rajam, Aravind Sukumaran;Kalyanaraman, Ananth;Pande, Partha Pratim
通讯作者：
Pande, Partha Pratim

PaKman: Scalable Assembly of Large Genomes on Distributed Memory Machines