- 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.
skill-tree/pe/2/2/i.txt · Last modified: 2020/07/19 19:51 by lucy