Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

Pierre Huchant (1, 2, 3, 4) , Denis Barthou (1, 2, 3, 4) , Marie-Christine Counilh (1, 2, 3, 4)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 Bordeaux INP - Institut Polytechnique de Bordeaux
3 UB - Université de Bordeaux
4 STORM - STatic Optimizations, Runtime Methods

OpenCL defines a common parallel programming language for all devices, although writing tasks adapted to the devices, managing communication and load-balancing issues are left to the programmer. We propose in this paper a static/dynamic approach for the execution of an iterated sequence of data-dependent kernels on a multi-device heterogeneous architecture. The method allows to automatically distribute irregular kernels onto multiple devices and tackles, without training, both load balancing and data transfers issues coming from hardware heterogeneity, load imbalance within the application itself and load variations between repeated executions of the sequence.

Mots clés: Static Analysis; OpenCL; Dynamic Analysis; Load Balancing; Irregular Workload

Domaines: Informatique [cs]

Pierre Huchant, Denis Barthou, Marie-Christine Counilh. Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels. SBAC-PAD - 30th International Symposium on Computer Architecture and High Performance Computing, Sep 2018, Lyon, France. ⟨10.1109/SBAC-PAD.2018.00051⟩. ⟨hal-01888216⟩