INAM: Cross-stack Profiling and Analysis of Communication in MPI-based Applications
Pouya Kousha, Kamal Raj Sankarapandian Dayala Ganesh Ram, Mansa Kedia, Hari Subramoni, Arpan Jain, Aamir Shafi, Dhabaleswar K. Panda, Trey Dockendorf, Heechang Na, Karen Tomko
Abstract
Understanding the full-stack performance trade-offs and interplay among HPC applications, MPI libraries, the communication fabric, and the job scheduler is a challenging endeavor. Unfortunately, existing profiling tools are disjoint and only focus on profiling one or a few levels of the HPC stack limiting the insights they can provide. In this paper, we propose a standardized approach to facilitate near real-time, low overhead performance characterization, profiling, and evaluation of communication of high-performance communication middleware as well as scientific applications using a cross-stack approach by INAM. The profiling capabilities are supported in two modes of with and without modifications to the application depending on the scope of the profiling session. We design and implement our designs using an MPI_T-based standardized method to obtain near real-time insights for MPI applications at scales of up to 4,096 processes with less than 5% overhead. Through experimental evaluations of increasing batch size for DL training, we demonstrate novel benefits of INAM for cross-stack communication analysis in real-time to detect bottlenecks and resolve them, achieving up to 3.6x improvements for the use-case study. The proposed solutions have been publicly released with the latest version of INAM and currently being used in production at various HPC supercomputers.