Achieving High Performance on Supercomputers with a Sequential Task-based Programming Model

Emmanuel Agullo (1) , Olivier Aumage (2) , Mathieu Faverge (3) , Nathalie Furmento (2) , Florent Pruvost (1) , Marc Sergent (2) , Samuel Thibault (2)

1 HiePACS - High-End Parallel Algorithms for Challenging Numerical Simulations
2 STORM - STatic Optimizations, Runtime Methods
3 LaBRI - Laboratoire Bordelais de Recherche en Informatique

The emergence of accelerators as standard computing resources on supercomputers and the subsequent architectural complexity increase revived the need for high-level parallel programming paradigms. Sequential task-based programming model has been shown to efficiently meet this challenge on a single multicore node possibly enhanced with accelerators, which motivated its support in the OpenMP 4.0 standard. In this paper, we show that this paradigm can also be employed to achieve high performance on modern supercomputers composed of multiple such nodes, with extremely limited changes in the user code. To prove this claim, we have extended the StarPU runtime system with an advanced inter-node data management layer that supports this model by posting communications automatically. We illustrate our discussion with the task-based tile Cholesky algorithm that we implemented on top of this new runtime system layer. We show that it enables very high productivity while achieving a performance competitive with both the pure Message Passing Interface (MPI)-based ScaLAPACK Cholesky reference implementation and the DPLASMA Cholesky code, which implements another (non-sequential) task-based programming paradigm.

Mots clés: sequential task flow; runtime system; GPU; task-based programming; heterogeneous computing; distributed computing; Cholesky factorization; multicore

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Emmanuel Agullo, Olivier Aumage, Mathieu Faverge, Nathalie Furmento, Florent Pruvost, et al.. Achieving High Performance on Supercomputers with a Sequential Task-based Programming Model. IEEE Transactions on Parallel and Distributed Systems, inPress, ⟨10.1109/TPDS.2017.2766064⟩. ⟨hal-01618526⟩