Implementation of a parallel algorithm for thermo-chemical nonequilibrium flow simulations
C.C. Wong, F.G. Blottner, Jeffrey Payne, Moeljo Soetrisno · 33rd Aerospace Sciences Meeting and Exhibit · 1995
Massively parallel (MP) computing is considered to be the future direction of high performance computing. When engineers apply this new MP computing technology to solve large-scale problems, one major interest is what is the maximum problem size that a MP computer can handle. To determine the maximum size, it is important to address the code scalability issue. Scalability implies whether the code can provide an increase in performance proportional to an increase in problem size. If the size of the problem increases, by utilizing more computer nodes, the ideal elapsed time to simulate a problem should not increase much. Hence one important task in the development of the MP computing technology is to ensure scalability. A scalable code is an efficient code. In order to obtain good scaled performance, it is necessary to first have the code optimized for a single node performance before proceeding to a large-scale simulation with a large number of computer nodes. This paper will discuss the implementation of a massively parallel computing strategy and the process of optimization to improve the scaled performance. Specifically, we will look at domain decomposition, resource management in the code, communication overhead, and problem mapping. By incorporating these improvements and adopting an efficient MP computing strategy, an efficiency of about 85% and 96%, respectively, has been achieved using 64 nodes on MP computers for both perfect gas and chemically reactive gas problems. A comparison of the performance between MP computers and a vectorized computer, such as Cray-YMP, will also be presented.