Fine-grained Communication Phase based Analytical Performance Modeling and Analysis - Deka, Vishal; Malakar, Preeti (Anais do International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2025)
High-performance computing is essential for scientific innovation. With the advent of exascale computing and the growing scale of scientific workloads, novel tools and methodologies are extremely important to analyze and model the performance of large-scale scientific applications. Existing profiling and tracing tools have certain limitations: profiles do not allow fine-grained performance modeling, whereas trace-based simulation or performance modeling is expensive and often infeasible for large applications. In this work, we propose fine-grained communication phases as a level of abstraction for analyzing and modeling application performance. Given the iterative nature of HPC applications, we propose a methodology to automatically group MPI communication events into phases and identify the unique and repeating phases. Our approach enables modeling only the unique phases, in contrast to a complete trace simulation. Further, we address the limitation of existing analytical communication models to model runtime delays in communication time. We propose a new delay-aware communication model (DACM) that achieves a best-case prediction error of 10.11% across six diverse HPC benchmarks and applications, in contrast to 53% using existing analytical models.