Profiling a Task-Based Molecular Dynamics Application with a Data Science Approach

Charm++ is a parallel programming framework based on task-driven execution and global object references. It has been used successfully in various high-performance computing (HPC) applications, including the molecular dynamics simulator NAMD. While Charm++ includes built-in support for performance tracing and visualization through its Projections tool, the existing system offers limited extensibility and has no support for modern data science workflows. This work presents a new visualization and analysis pipeline for Charm++ trace data that emphasizes modularity, openness, and composability. Our toolchain leverages standard scripting languages and data formats - producing output in CSV and Parquet formats - to facilitate integration with data analysis ecosystems. We demonstrate the effectiveness of this approach using LeanMD, a proxy application derived from NAMD, and highlight specific types of custom visualizations that are difficult to achieve with Projections. Our system enables custom visualizations and streamlined analysis of chare-level execution behavior, offering researchers and tool developers improved capabilities for understanding program performance and identifying load imbalance. We discuss the architecture of our tool, its application to real-world traces, and potential extensions for other task-based frameworks.