Collaborative Research: CISE-MSI: DP: FET: Modernizing Numerical Flow Solvers with Heterogeneous Computing

今天的计算系统通常是异构的，即配备多核中央处理单元（cpu）和多核图形处理单元（gpu）。许多研究人员试图将自己的内部数值求解器并行化，以同时利用多个处理器的计算能力进行大规模模拟。然而，并行编程涉及一个陡峭的学习曲线，并且通常需要对底层硬件体系结构有深入的了解才能实现最佳性能。当涉及到GPU编程时，这就变成了一个更大的问题。该项目利用了杰克逊州立大学（JSU）研究人员在基于cpu的并行计算方面的专业知识，以及密西西比大学（UM）研究人员在基于gpu的并行计算方面的专业知识。此项目创建了一个通用库，其中打包了用于并行化的优化函数和例程。这个图书馆在公共领域免费提供。该库极大地减轻了基于网格的数值求解的并行化负担。其他研究人员可以调用库函数，以最少的代码更改和开发人员的努力，在异构系统上快速、轻松地并行化他们基于网格的数值求解器。这项合作研究在促进研究能力的发展和在JSU和UM建立高性能科学计算的可持续教育方面产生了协同效应。在研究方面，本项目通过充分利用异构系统上cpu和gpu的并行计算能力来加速和优化数值流求解器。该项目将使用OpenCL（开放计算语言）的GPU加速集成到MPI（消息传递接口）并行计算范例中。在这种组合中，MPI在多个cpu上提供粗粒度并行性，而OpenCL在cpu / gpu上提供细粒度并行性。这种混合以及通过共享虚拟内存实现的细粒度数据共享和同步，使数值求解器能够充分利用当今异构并行系统的计算能力。目标系统为配备CPU-GPU异构计算节点的集群。研究重点包括MPI-OpenCL混合模式下的高效通信和负载平衡。在教育方面，该项目通过在JSU举办高性能计算（HPC）暑期学院和在UM举办高性能计算日活动，向学生传授现代并行计算和编程的必备技能，从而吸引和培养未来的劳动力。本项目由CISE MSI计划和既定计划共同资助，以刺激竞争研究（EPSCoR）。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Today’s computing systems are often heterogeneous, i.e., equipped with both multi-core central processing units (CPUs) and many-core graphics processing units (GPUs). Many researchers try to parallelize their in-house numerical solvers by themselves to simultaneously utilize the computing power of multiple processors to make large scale simulation possible. However, parallel programming involves a steep learning curve and often requires the deep understanding of underlying hardware architecture to achieve optimal performance. This becomes an even bigger issue when GPU programming gets involved. This project leverages the expertise of researchers at Jackson State University (JSU) in CPU-based parallel computing and the expertise of researchers at the University of Mississippi (UM) in GPU-based parallel computing. This project creates a general-purpose library that packages optimized functions and routines used for parallelization. This library is freely available in public domain. The library greatly eases the burden of parallelization of mesh-based numerical solvers. Other researchers can call the library functions to parallelize their mesh-based numerical solvers quickly and easily on heterogeneous systems with the minimum level of code change and developers' efforts. This collaborative research generates a synergistic effect on catalyzing the development of research capabilities and establishing sustainable education in high performance scientific computing at JSU and UM.On the research side, this project accelerates and optimizes numerical flow solvers by fully exploiting the parallel computing power of both CPUs and GPUs on heterogeneous systems. This project incorporates GPU acceleration using OpenCL (Open Computing Language) into the MPI (Message Passing Interface) parallel computing paradigm. In this combination, MPI provides coarse grained parallelism on multiple CPUs and OpenCL provides fine-grained parallelism on CPUs/GPUs. This blending along with fine-grained data sharing and synchronization via shared virtual memory allows the numerical solvers to take full advantage of the computing power of today’s heterogeneous parallel systems. Targeted systems are clusters equipped with CPU-GPU heterogeneous computing nodes. Research focuses include efficient communication and load balancing on the hybrid MPI-OpenCL paradigm. On the education side, the project attracts and builds future workforce by teaching students must-have skills on modern parallel computing and programming through hosting a High Performance Computing (HPC) Summer Institute at JSU and the HPC-day event at UM.This project is jointly funded by the CISE MSI program and the Established Program to Stimulate Competitive Research (EPSCoR).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.