Performance evaluation and visualization of scientific applications using PMlib

The computational performance of scientific applications on HPC systems is often much lower than user expectation based on the system's maximum performance specifications. To understand the basis for this performance gap, a multi-perspective evaluation is important. For instance, from the user perspective, correlating the theoretical computation coded as a source program with the actual computation workload produced by the compilers is valuable. From the system perspective, evaluating the characteristics of microarchitecture elements such as processor core and memory is of significance. An open source library called PMlib was developed to address these types of synthetic evaluations. PMlib provides an avenue for reporting the arithmetic/application workload explicitly coded in the source program, as well as the actually executed system workload. It also provides detailed utilization reports of processor-specific hardware including the categorized SIMD instruction statistics, the layered cache hit/miss rate, and the effective memory bandwidth, which are captured via hardware performance counters (HWPC). Using PMlib, users can conduct a synthetic analysis of application performance, and obtain useful feedback for further optimized execution of applications.