Runtime analysis of parallel applications for industrial software development

Utilizing the parallelism offered by multicore CPUs is hard, though profiling and tracing are well-established techniques to understand, debug, engineer, and optimize codes. While many tools are available to capture profiles and traces, these tools are often difficult to use in industrial contexts. Tool development often started with sequential applications in mind to transition to parallelism not until later, resulting in improper feature sets and usability. In contrast, parallel tools are often targeted towards HPC with a strong focus on MPI and OpenMP. As this turns these tools less suitable for codes using alternative threading models (POSIX, Qt, and ACE), this talk presents extensions to the open-source profiling and tracing tools Score-P and Scalasca. Score-P captures detailed program execution data allowing Scalasca to perform a sophisticated performance and wait-state analysis