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The paper describes the development and performance of parallel algorithms for the discrete element method (DEM) software. Spatial domain decomposition strategy and message passing inter-processor communication have been implemented in the DEMMAT code for simulation of visco-elastic frictional granular media. The novel algorithm combining link-cells for contact detection, the static domain decomposition for parallelization and MPI data transfer for processors exchanging particles has been developed for distributed memory PC clusters. The parallel software DEMMAT_PAR has been applied to model compacting of spherical particles in the rectangular box. Two benchmark problems with different numbers of particles have been solved in order to measure parallel efficiency of the code. The inter-processor communication has been examined in order to improve domain decomposition topology and to achieve better load balancing. The speed-up equal to 11 has been obtained on 16 processors. The parallel performance study has been performed on the PC cluster

The pervasive use of cloud computing has led to many concerns, such as performance challenges in communication- and computation-intensive services on virtual cloud resources. Most evaluations of the infrastructural overhead are based on standard benchmarks. Therefore, the impact of communication issues and infrastructure services on the performance of parallel MPI-based computations remains unclear. This paper presents the performance analysis of communication- and computation-intensive software based on the discrete element method, which is deployed as a service (SaaS) on the OpenStack cloud. The performance measured on KVM-based virtual machines and Docker containers of the OpenStack cloud is compared with that obtained by using native hardware. The improved mapping of computations to multicore resources reduced the internode MPI communication by 34.4% and increased the parallel efficiency from 0.67 to 0.78, which shows the importance of communication issues. Increasing the number of parallel processes, the overhead of the cloud infrastructure increased to 13.7% and 11.2% of the software execution time on native hardware in the case of the Docker containers and KVM-based virtual machines of the OpenStack cloud, respectively. The observed overhead was mainly caused by OpenStack service processes that increased the load imbalance of parallel MPI-based SaaS.

The present paper describes the development and the performance of parallel FEM software for solving various CFD problems. Domain decomposition strategy and parallel iterative GM-RES solver have been adapted to the universal space-time FEM code FEMTOOL, which allows implementation of any partial differential equation with minor expenses. The developed data structures, the static load balancing and the inter-processor communication algorithms have been particularly suited for homogeneous distributed memory PC clusters. The universality of the considered parallel algorithms has been validated solving applications described by the Poisson equation, by the convective transport equation and by the Navier-Stokes equations. Three typical benchmark problems have been solved in order to perform the efficiency study. The performance of parallel computations, the speed-up and the efficiency have been measured on three BEOWULF PC clusters as well as on the cluster of IBM RISC workstations and on the IBM SP2 supercomputer.

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