Section: New Results

Resource aggregation for task-based Cholesky Factorization

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 a recent paper [8] 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. In addition, we realized an extended version of this paper submitted for review to the Parallel Computing journal special issue for HCW and HeteroPar 2016 workshops. In this new paper [19] we provide additional details on our contribution and propose a brand new study on the recent Intel Xeon Phi Knights Landing (KNL) where we show that we are able to outperform existing state of the art implementations on this platform thanks to our proposed technique.