# PE2.2-I Profiling
# Background
# Aim
# Outcomes
  * Manage 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 into the runtime behavior of the program.
  * Use performance analysis tools like ScoreP, and Scalasca.

# Subskills
  * [[skill-tree:pe:2:2:e]]