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A Case Study of HPC Metrics Collection and Analysis

Philip Johnson and Michael Paulding, University of Hawaii, Honolulu, Hawaii. . A Case Study of HPC Metrics Collection and Analysis. Goals of the case study Provide a complete implementation of one Purpose Based Benchmark problem definition, called Optimal Truss Design

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A Case Study of HPC Metrics Collection and Analysis

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  1. Philip Johnson and Michael Paulding, University of Hawaii, Honolulu, Hawaii. A Case Study of HPC Metrics Collection and Analysis • Goals of the case study • Provide a complete implementation of one Purpose Based Benchmark problem definition, called Optimal Truss Design • Implement the Optimal Truss Design system in C++ using MPI on a 240 node Linux cluster at the University of Hawaii • Develop and evaluate automated support for HPC process and product measurement using Hackystat • Assess the utility of the metrics for understanding HPC development Results: Process and Product Telemetry Charts Results: Daily Diary with CLI and Most Active File • Metrics Collected • Size (Number of files, Total SLOC, “Parallel” SLOC containing an MPI directive, “Serial” SLOC not containing an MPI directive, Test code) • Active Time (amount of time spent editing Optimal Truss Design files) • Performance (wall clock time on 1, 2, 4, 8, 16, and 32 processors) • Milestone Tests (indicates functional completeness) • Command Line Invocations • Insights and Lessons Learned • Productivity (22 LOC/hour) and test code density (27%) seem in line with traditional software engineering metrics. • Speedup data indicates almost linear speedup to 4 processors, then falls off sharply, indicating that current solution is not scalable. • Parallel and serial LOC were equal at start of project, then most effort was devoted to serial code, with some final enhancements to parallel code at end of project. • Performance data was not comparable over course of project (only final numbers available; no telemetry) • Hackystat provides effective infrastructure for collection of process and product metrics. • This case study provides useful baseline data to compare with future studies. • Future research: • Compare to OpenMP or JavaParty implementation. • Gather metrics while improving scalability of system • Compare metrics against other application types. • Analyze CLI data for patterns, bottlenecks Thanks to our sponsors • For More Information • Understanding HPCS development through automated process and product measurement with Hackystat, Philip M. Johnson and Michael G. Paulding, Proceedings of the Second Workshop on Productivity and Performance in High-End Computing.

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