get the base data for tuning the performance of parallel programs by profiling

detect performance issues and bottlenecks caused, for example, by inefficient programming, memory accesses, I/O operations, cache-misses, page-faults, and parallelization overheads

assess how different views of the profiling data (e.g. timeline graphs and communication matrices to illustrate the traffic between processes) can give insights in the runtime behavior of the program

use performance analysis tools like ScoreP, Scalasca

PE2.2-E Profiling