Resource Aggregation for Task-Based Cholesky Factorization on Top of Heterogeneous Machines

Hybrid computing platforms are now commonplace, featuring a large number of CPU cores and accelerators. This trend makes balancing computations between these heterogeneous resources performance critical. In this paper we propose aggregating several CPU cores in order to execute larger parallel tasks and thus improve the load balance between CPUs and accelerators. Additionally, we present our approach to exploit internal parallelism within tasks. This is done by combining two runtime systems: one runtime system to handle the task graph and another one to manage the internal parallelism. We demonstrate the relevance of our approach in the context of the dense Cholesky factorization kernel implemented on top of the StarPU task-based runtime system. We present experimental results showing that our solution outperforms state of the art implementations.