Extending Hedgehog's Dataflow Graphs to Multi-node GPU Architectures.

Asynchronous task-based systems offer the possibility of making it easier to take advantage of scalable heterogeneous architectures. This paper extends the National Institute of Standards and Technology’s Hedgehog dataflow graph models, which target a single high-end compute node, to run on a cluster by borrowing aspects of Uintah’s cluster-scale task graphs and applying them to a sample implementation of matrix multiplication. These results are compared to implementations using the leading libraries, SLATE and DPLASMA, for illustrative purposes only. The motivation behind this work is to demonstrate that using general purpose high-level abstractions, such as Hedgehog’s dataflow graphs, does not negatively impact performance.