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

随着更快的计算系统的快速和全球竞争的发展，每秒可以计算高达一千万亿分之一的浮点运算，更新指导如何分析数据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，要适应和改变以利用这些系统的丰富计算能力一直是一个挑战，这些代码填满了数百到数千行代码。这在很大程度上是人力问题，因为代码的适配需要应用程序开发人员不断地重写他们的程序代码。与错综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“一次编写、多次重用”的软件。在所有人都在关注亿级机器的发展--一种可以以人脑的速度计算数据的机器--的情况下，解决这一根本挑战是当务之急。该项目的目的是设计高级抽象，使科学代码能够以增强这些机器的性能的方式使科学代码适应当前和即将到来的系统，从而确保这些“像人脑一样快”的系统足够灵活和适应性，以鼓励更广泛的科学界。该项目的目标是实现高性能、高内存效率、可移植和多产的软件框架，用于并行化复杂的并行模式，如在中子辐射传输、生物信息学和大气科学等大型科学应用中常见的“波前”。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转换框架以及基于任务的环境在新硬件系统上的适用性，特别是在预置和即将到来的艾级系统上。这些研究还暗示了当前编程模型中的不足之处，为开发高层软件抽象的新见解铺平了道路，以便同时在不同/不同的项目中多用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.