Parallelization of MD algorithms and load balancing

MD simulation in process engineering features enormous computational demands, and therefore requires efficient parallelization techniques. This chapter describes ls1 mardyn ’s parallelization approach for shared-memory and distributed-memory architectures. This is done by first defining today’s computing architectures and their governing design principles: Heterogeneity, massive amounts of cores and data parallelism. Based on this, we are then able to reengineer ls1 mardyn in such a way that it can optimally leverage important hardware features, and describe our parallelization approach for shared- and distributed-memory systems at the example of the Intel Xeon processor and the Intel Xeon Phi coprocessor, respectively. We close this section by describing load-balancing techniques in case of a distributed-memory parallelization and heterogeneous particle distributions in the computational domain.