Probability Analysis to Improve the Confidence in Profiling Accuracy

Abstract

Performance profiling for the system is necessary and has already been widely supported by hardware performance counters (HPC). HPC is based on the registers to count the number of events in a time interval and uses system interruption to read the number from registers to a recording file. The profiled result approximates the actual running states and is not accurate since the profiling technique uses sampling to capture the states. We do not know the actual running states before, which makes the validation on profiling results complex. Jianwei YinSome experiments-based analysis compared the running results of benchmarks running on different systems to improve the confidence of the profiling technique. But they have not explained why the sampling technique can represent the actual running states. We use the probability theory to prove that the expectation value of events profiled is an unbiased estimation of the actual states, and its variance is small enough. For knowing the actual running states, we design a simulation to generate the running states and get the profiled results. We refer to the applications running on production data centers to choose the parameters for our simulation settings. Comparing the actual running states and the profiled results shows they are similar, which proves our probability analysis is correct and improves our confidence in profiling accuracy.