NSF Award
2219542
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.<br/><br/>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.<br/><br/>This project is jointly funded by the CISE MSI program and the Established Program to Stimulate Competitive Research (EPSCoR).<br/><br/>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.
