Towards Data Partitioning for Parallel Computing on Three Interconnected Clusters

We present a new data partitioning strategy for parallel computing on three interconnected clusters. This partitioning has two advantages over existing partitionings. First it can reduce communication time due to a lower total volume of communication and a more efficient communication schedule. When the network topology is a linear array this partitioning always results in a lower total volume of communication compared to existing partitionings, provided the most powerful node is at the center of the array. When the topology is fully connected this partitioning results in a lower total volume of communication for all but a few power ratios. Second, it allows for the overlapping of communication and computation. These two inherent advantages work together to reduce overall execution time significantly.