Comparative Evaluation of Call Graph Generation by Profiling Tools

Call graphs generated by profiling tools are critical to dissecting the performance of parallel programs. Although many mature and sophisticated profiling tools record call graph data, each tool is different in its runtime overheads, memory consumption, and output data generated. In this work, we perform a comparative evaluation study on the call graph data generation capabilities of several popular profiling tools -- Caliper, HPCToolkit, TAU, and Score-P. We evaluate their runtime overheads, memory consumption, and generated call graph data (size and quality). We perform this comparison empirically by executing several proxy applications, AMG, LULESH, and Quicksilver on a parallel cluster. Our results show which tool results in the lowest overheads and produces the most meaningful call graph data under different conditions.

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Contributors:

• Abhinav Bhatele (University of Maryland, College Park)
• Onur Cankur (University of Maryland, College Park)