Impact of Kernel-Assisted MPI Communication over Scientific Applications: CPMD and FFTW

Collective communication is one of the most powerful message passing concepts, enabling parallel applications to express complex communication patterns while allowing the underlying MPI to provide efficient implementations to minimize the cost of the data movements. However, with the increase in the heterogeneity inside the nodes, more specifically the memory hierarchies, harnessing the maximum compute capabilities becomes increasingly difficult. This paper investigates the impact of kernel-assisted MPI communication, over two scientific applications: 1) Car-Parrinello molecular dynamics(CPMD), a chemical molecular dynamics application, and 2) FFTW, a Discrete Fourier Transform (DFT). By focusing on the usage of Message Passing Interface (MPI), we found the communication characteristics and patterns of each application. Our experiments indicate that the quality of the collective communication implementation on a specific machine plays a critical role on the overall application performance.