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.

随着每秒可计算多达五千万次浮点运算的更快计算系统的快速发展和全球竞争，更新指导数据分析方式的计算机程序变得势在必行。然而，对于已建立的、经过时间考验的遗留科学代码来说，填充数百到数千行代码，适应和改变以利用这些系统丰富的计算能力一直是一个挑战。这很大程度上是一个人力问题，因为代码的适配需要应用程序开发人员不断地重新编写他们的程序代码。与复杂的硬件和不断发展的编程语言相关的陡峭的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“一次写入多次重用”的软件。随着所有人的目光都集中在百亿亿次机器的开发上——一种能够以人脑速度计算数据的机器——解决这一基本挑战势在必行。该项目的目的是设计高级抽象，使科学代码能够适应当前和即将推出的系统，从而增强这些机器的性能，从而确保这些“像人脑一样快”的系统是灵活的并且具有足够的适应性以鼓励更广泛的科学界。 该项目的目标是实现高性能、内存高效、便携和高效的软件框架，用于并行化复杂的并行模式，例如“波前”，常见于中子辐射传输、生物信息学和大气科学等大型科学应用中。为了实现这一目标，研究人员正在算法级别、编程框架级别和软件设计级别解决关键的性能可移植问题。该项目研究了经过充分探索的多面体变换框架以及基于任务的环境在新型硬件系统上的适用性，特别是在前和即将到来的百亿亿次系统上。这些研究还指出了当前编程模型的缺陷，为开发高级软件抽象的新颖见解铺平了道路，以便同时在不同/多样化的项目中使用。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

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: 2022-11
• 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: 2023-08
• 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: 2022-07
• 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;et al
• 通讯作者: et al

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: 2020-05
• 期刊: PLOS Computational Biology
• 作者: Wright, Eric;Ferrato, Mauricio H.;Bryer, Alexander J.;Searles, Robert;Perilla, Juan R.;Chandrasekaran, Sunita;Schneidman
• 通讯作者: Schneidman

ECP SOLLVE: Validation and Verification Testsuite Status Update and Compiler Insight for OpenMP

ECP SOLLVE：OpenMP 的验证和验证测试套件状态更新和编译器洞察

• DOI: 10.1109/p3hpc56579.2022.00017
• 发表时间: 2022-11
• 期刊: IEEE
• 作者: Huber, Thomas;Pophale, Swaroop;Baker, Nolan;Carr, Michael;Rao, Nikhil;Reap, Jaydon;Holsapple, Kristina;Davis, Joshua Hoke;Burnus, Tobias;Lee, Seyong;et al
• 通讯作者: et al