Toward Easy Parallel Programming for Computational Fluid Dynamics

计算科学家通常不是专业的程序员，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，它们具有非常高性能的要求，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望而生畏的任务。如果没有可行的解决方案，他们就无法释放并行硬件的潜力，科技创新也将放缓。这个项目将找到让每个计算科学家都能使用并行编程的方法。该博士生将以典型的计算流体动力学（CFD）问题为案例研究，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。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二进制文件中以优化代码。如果成功，这个项目将开启一个计算科学家如何利用并行计算基础设施的变革时代，而不是否认和害怕计算硬件的日益专业化，我们拥抱并利用它。

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.