Abstract
With each technology improvement, parallel systems get larger, and the impact of interconnection networks becomes more prominent. Random topologies and their variants received more and more attention lately due to their low diameter, low average shortest path length and high scalability. However, existing supercomputers still prefer torus and fat-tree topologies, because a number of existing parallel algorithms are optimized for them and the interconnect implementation is more straight-forward in terms of floor layout. In this paper, we investigate the performance of traditional and emerging parallel workloads on these network topologies, using a event-discrete simulation called SimGrid. We observe that random topology is better for Fourier Transform (FT), Graph500, Himeno benchmarks, and its improvement over the counterpart torus is 18 percent in average. Through this study, our recommendation is to use random topology in current and future supercomputers for these scientific and big-data analysis parallel applications.