Application instrumentation for performance analysis and tuning with focus on energy efficiency

Abstract

Profiling and tuning of parallel applications is an essential part of HPC. Analysis and elimination of application hot spots can be performed using many available tools, which also provides resource consumption measurements for instrumented parts of the code. Since complex applications show different behavior in each part of the code, it is essential to be able to insert instrumentation to analyse these parts. Because each performance analysis or autotuning tool can bring different insights into an application behavior, it is valuable to analyze and optimize an application using a variety of them. We present our on request inserted shared C/C++ API for the most common open-source HPC performance analysis tools, which simplify the process of the manual instrumentation. Besides manual instrumentation, profiling libraries provide different methods for instrumentation. Of these, the binary patching is the most universal mechanism, and highly improves the user-friendliness and robustness of the tool. We provide an overview of the most commonly used binary patching tools, and describe a workflow for how to use them to implement a binary instrumentation tool for any profiler or autotuner. We have also evaluated the minimum overhead of the manual and binary instrumentation.