Establishing a base of trust with performance counters for enterprise workloads
Understanding the performance of large, complex enterprise-class applications is an important, yet nontrivial task. Methods using hardware performance counters, such as profiling through event-based sampling, are often favored over instrumentation for analyzing such large codes, but rarely provide good accuracy at the instruction level. This work evaluates the accuracy ofmultiple eventbased sampling techniques and quantifies the impact of a range of improvements suggested in recent years. The evaluation is performed on instances of three modern CPU architectures, using designated kernels and full applications. We conclude that precisely distributed events considerably improve accuracy, with further improvements possible when using Last Branch Records. We also present practical recommendations for hardware architects, tool developers and performance engineers, aimed at improving the quality of results.