My first experience with the Vampir trace visualiser was in 2010 during my studies at EPCC. While working on the exercises and samples, I was excited by the possibility of finding out what every process or thread (from thousands) is doing at any point in time. Over the years I have used TAU + Score-P + Vampir toolset with different applications on various systems. When it comes to trace visualisation for scientific applications at scale, Vampir is very impressive. If you haven't used it before, give a try!
If you are working in the area of scientific computing, in academia or industry, most likely you are using Python in some form. Traditionally Python is described as slow when it comes to performance and there are number of discussions about speed compared to native C/C++ applications 1 2. The goal of this post is not to argue about performance but to summarise various tools that can help to find out performance bottlenecks before coming to such conclusions. In the previous post, I summarised more than 90 profiling tools that can be used for analysing performance of C/C++/Fortran applications.… Read the rest