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Toward Easy Parallel Programming for Computational Fluid Dynamics

面向计算流体动力学的简单并行编程

基本信息

批准号：
2438840
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2438840
关键词：
Toward Easy Parallel Programming Computational

项目摘要

Computational scientists are typically not expert programmers and thus have to work with easy to use programming languages. However, they have very high-performance requirements due to their experimental setup and large datasets and rely on parallel computing machines for tasks like simulation runs. Unfortunately, the increasing specialisation and complexity in computing hardware have made parallel programming a daunting task for mainstream computational researchers. Without a viable solution, they cannot unlock the potential of parallel hardware and science and technology innovations will slow down.This project will find ways to make parallel programming accessible to every computational scientist. The PhD student will take representative computational fluid dynamics (CFD) problems as a case study to develop novel approaches to help CFD developers easily write and optimise code to take full advantages of parallel hardware. It will investigate how to best express the high-level algorithmic intent in the programming language and carry such information into the executable binary to optimise code. If successful, this project will usher in an era of change in how computational scientists could exploit parallel computing infrastructures, where, rather than deny and fear the increasing specialisation in computing hardware, we embrace and utilise it.

计算科学家通常不是专业程序员，因此必须使用易于使用的编程语言。然而，由于它们的实验设置和大型数据集，它们具有非常高的性能要求，并且依赖于并行计算机器来执行模拟运行等任务。不幸的是，计算硬件的日益专业化和复杂性使得并行编程成为主流计算研究人员的一项艰巨任务。如果没有一个可行的解决方案，他们就无法释放并行硬件的潜力，科学和技术创新将放缓。这个项目将找到方法，使每个计算科学家都能使用并行编程。这位博士生将以代表性的计算流体动力学（CFD）问题作为案例研究，开发新的方法来帮助CFD开发人员轻松编写和优化代码，以充分利用并行硬件的优势。它将研究如何在编程语言中最好地表达高级算法意图，并将这些信息携带到可执行的二进制文件中以优化代码。如果成功，这个项目将迎来一个变革的时代，在计算科学家如何利用并行计算基础设施，在那里，而不是否认和恐惧在计算硬件日益专业化，我们拥抱和利用它。