Schur complement matrices emerge in many domain decomposition methods that can utilize supercomputers to solve complex engineering problems. As most of today’s high-performance clusters’ performance lies in GPUs, these methods should also be accelerated.

This paper presents a workflow for parallel loading of database files containing sequentially stored unstructured meshes that are not considered to be efficiently read in parallel. In such a file consecutive elements are not spatially located and their respective nodes are at unknown positions in the file. This makes parallel loading challenging since adjacent elements are on different MPI processes, and their respective nodes are on unknown MPI processes. These two facts lead to a high communication overhead and very poor scalability if not addressed properly. In a standard approach, a sequentially stored mesh is sequentially converted to a particular parallel format accepted by a solver. This represents a significant bottleneck. Our proposed algorithm demonstrates that this bottleneck can be overcome, since it is able to (i) efficiently recreate an arbitrary stored sequential mesh in the distributed memory of a supercomputer without gathering the information into a single MPI rank, and (ii) prepare the mesh for massively parallel solvers.

Parallel computing can be very useful for meta-heuristic algorithms, because it can reduce time to find a solution with given properties. On the other hand parallel programming is generally more difficult than the sequential one and we need more development time to get a working parallel solution. We are developing a tool named Kaira which is intended for modelling, debugging, and automated generation of parallel applications. The modelling is based on our own variant of Coloured Petri Nets. In this paper we present how to create parallel programs from an existing sequential implementation of Ant Colony Optimization Algorithm using Kaira. This approach simplifies the development process and thus saves the development time and costs.