🤖 AI Summary
This work addresses the high overhead of processing massive telemetry data in exascale supercomputing systems by proposing a heterogeneous acceleration–enabled, high-performance diagnostic framework. Integrating high-throughput C++ APIs with GPU-parallelized computation, the framework supports scalable MPI trace analysis and seamless integration with external tools. It introduces a novel topology-aware workflow that maps logical performance anomalies onto the physical coordinates of the Slingshot interconnect and pioneers a three-dimensional performance model to iteratively reconstruct application behavior, enabling precise identification of performance headroom. Evaluated on Aurora, the system ingests traces from 100,000 MPI ranks in just 9.69 seconds, achieving up to a 314× speedup over CPU-based analysis. On Frontier, it uncovers 32.28% potential acceleration for the GAMESS application.
📝 Abstract
As exascale systems reach unprecedented concurrency, traditional performance analysis tools struggle with the overhead of massive-scale telemetry. We present an accelerated infrastructure for the hpcanalysis framework that leverages a high-performance C++ API and GPU parallelism to enable high-throughput diagnostics. Our C++ API achieves a 9.69-second ingestion time for 100,000 MPI ranks on Aurora. Furthermore, our GPU-accelerated layer achieves up to 314x speedup over CPU-based processing when analyzing 100,000 execution traces. Finally, we implement a topology-aware workflow that maps logical performance outliers to physical Slingshot interconnect coordinates, localizing network congestion across 22 distinct racks on Aurora.
We also demonstrate how the framework's advanced interface seamlessly integrates with external tools to provide sophisticated analytical models. We introduce a novel tri-dimensional performance model that "re-materializes" iterative behavior from execution traces; using this model, we identified a 32.28% potential speedup for a GAMESS workload on Frontier.