Performance Optimization of HPC Applications in Large-Scale Cluster Systems
Longxiang Li · 2022
In modern HPC clusters, the performance of an application is a combination of several aspects. To successfully improve the application performance, all performance aspects should be analyzed and optimized. In particular, as modern CPUs contain more and more cores, the speed of floating-point computations has increased rapidly, making data access one of the main bottlenecks in most HPC applications. Furthermore, performance diagnosing for HPC applications can be extremely complex, and the performance bottlenecks of HPC applications may vary with the scale of parallelism. In this presentation, a multi-layered data access (MLDA) optimization methodology is introduced. Developers could follow this methodology to optimize the HPC applications. We provide several examples of applying the MLDA method on real-world HPC applications, including the weather, ocean, material science, CFD, and MHD areas.