SHF: Small: PAW: Novel Functionality in Programming Models to Productively Abstract Wavefront Parallel Pattern

SHF：小：PAW：编程模型中的新颖功能，可有效抽象波前并行图案

• 批准号: 1814609
• 负责人: Sunita Chandrasekaran
• 金额: $ 39.97万
• 依托单位: University of Delaware
• 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: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814609&HistoricalAwards=false
• 关键词: SHF; Small; PAW; Novel; Functionality

With the rapid and globally competitive development of faster computing systems that can compute up to one quintillion floating-point operations/second, it becomes imperative to update the computer programs that direct how data is analyzed. However, it has been a challenge for established and time-tested legacy scientific code, filling up hundreds to thousands of lines of code, to adapt and alter to exploit the rich computing capacity of these systems. This is largely a manpower issue as the adaptation of codes requires application developers to constantly re-write their program codes. The steep learning curve associated with both the intricacies of hardware and the ever evolving programming languages puts pressure on the developers and impedes the progress of science. The biggest challenge developers face is the inability to maintain a "write-once reuse multiple times" software. With all eyes on the development of an exascale machine - one that can compute data at the speed of the human brain - it is imperative to address this fundamental challenge. The aim of this project is to design high-level abstractions that can adapt scientific code to current and upcoming systems in a manner that enhances the performance of these machines, thus ensuring that these "fast-as-the human-brain" systems are flexible and adaptable enough to encourage the broader scientific community. The goal of this project to enable high performance, memory-efficient, portable and productive software framework for parallelizing complex parallel patterns such as 'wavefronts', commonly found in large scientific applications such as neutron radiation transport, bioinformatics and atmospheric science. To achieve this goal, the investigator is addressing critical performance portable questions at the algorithmic-level, programming framework-level and at the software design level. The project studies the applicability of well-explored polyhedral transformation frameworks along with task-based environments on novel hardware systems, importantly on pre- and upcoming exascale systems. The studies are also suggestive of shortcomings in current programming models paving the way to developing novel insights towards high-level software abstractions for multi-use in different/diverse projects simultaneously.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.

随着更快的计算系统的快速且具有全球竞争性的开发，该系统可以计算高达一千亿个浮点操作/秒，必须更新指导数据分析方式的计算机程序。但是，对于已建立且经过时间测试的旧式科学代码，填充数百至数千条代码，适应和更改以利用这些系统的丰富计算能力的挑战是一个挑战。这在很大程度上是一个人力问题，因为代码的改编要求应用程序开发人员不断重写其程序代码。与硬件的复杂性和不断发展的编程语言相关的陡峭学习曲线给开发人员带来了压力，并阻碍了科学的进步。开发人员面临的最大挑战是无法维护“多次写入重复使用”软件。所有人的眼睛都注视着exascale机器的开发 - 可以以人脑的速度计算数据 - 必须应对这一基本挑战。该项目的目的是设计高级抽象，以增强这些机器的性能，以确保这些“快速的人类脑”系统具有灵活性和适应能力，以鼓励更广泛的科学界。 该项目的目的是实现高性能，记忆效率，便携式和生产性软件框架，用于并行化复杂的平行模式，例如“波前”，在大型科学应用中通常发现，例如中子辐射传输，生物信息信息和大气科学。为了实现这一目标，研究者正在算法级别，编程框架级别以及软件设计级别上解决关键的性能便携式问题。该项目研究了经过验证的多面体转换框架的适用性，以及针对新型硬件系统的基于任务的环境，重要的是在预先和即将到来的Exascale系统上。这项研究还暗示了当前的编程模型中的缺点，这为在不同/多样性项目的高级软件摘要中开发了新的见解，以同时在不同/多样化的项目中进行多用途。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查审查的审查标准来通过评估来通过评估来支持的。

项目成果

Analysis of Validating and Verifying OpenACC Compilers 3.0 and Above

分析验证OpenACC编译器3.0及以上版本

• DOI: 10.1109/waccpd56842.2022.00006
• 发表时间: 2022
• 期刊: IEEE
• 作者: Jarmusch, Aaron;Liu, Aaron;Munley, Christian;Horta, Daniel;Ravichandran, Vaidhyanathan;Denny, Joel;Friedline, Kyle;Chandrasekaran, Sunita
• 通讯作者: Chandrasekaran, Sunita

Implementing OpenMP’s SIMD Directive in LLVM’s GPU Runtime

在 LLVM GPU 运行时中实施 OpenMP SIMD 指令

• DOI: 10.1145/3605573.3605640
• 发表时间: 2023
• 期刊: ACM
• 作者: Wright, Eric;Doerfert, Johannes;Tian, Shilei;Chapman, Barbara;Chandrasekaran, Sunita
• 通讯作者: Chandrasekaran, Sunita

SPEChpc 2021 Benchmark Suites for Modern HPC Systems

SPEChpc 2021 现代 HPC 系统基准套件

• DOI: 10.1145/3491204.3527498
• 发表时间: 2022
• 期刊: Companion of the 2022 ACM/SPEC International Conference on Performance Engineering
• 作者: Li, Junjie;Bobyr, Alexander;Boehm, Swen;Brantley, William;Brunst, Holger;Cavelan, Aurelien;Chandrasekaran, Sunita;Cheng, Jimmy;Ciorba, Florina M.;Colgrove, Mathew
• 通讯作者: Colgrove, Mathew

Accelerating prediction of chemical shift of protein structures on GPUs: Using OpenACC

在 GPU 上加速预测蛋白质结构的化学位移：使用 OpenACC

• DOI: 10.1371/journal.pcbi.1007877
• 发表时间: 2020
• 期刊: PLOS Computational Biology
• 影响因子: 4.3
• 作者: Wright, Eric;Ferrato, Mauricio H.;Bryer, Alexander J.;Searles, Robert;Perilla, Juan R.;Chandrasekaran, Sunita;Schneidman-Duhovny, Dina
• 通讯作者: Schneidman-Duhovny, Dina

First Experiences in Performance Benchmarking with the New SPEChpc 2021 Suites

使用新的 SPEChpc 2021 套件进行性能基准测试的初次体验

• DOI: 10.1109/ccgrid54584.2022.00077
• 发表时间: 2022
• 期刊: Cloud and Internet Computing (CCGrid
• 作者: Brunst, Holger;Chandrasekaran, Sunita;Ciorba, Florina M.;Hagerty, Nick;Henschel, Robert;Juckeland, Guido;Li, Junjie;Vergara, Veronica G.;Wienke, Sandra;Zavala, Miguel
• 通讯作者: Zavala, Miguel