Supporting multiple accelerators in high-level programming models

Computational accelerators, such as manycore NVIDIA GPUs, Intel Xeon Phi and FPGAs, are becoming common in workstations, servers and supercomputers for scientific and engineering applications. Efficiently exploiting the massive parallelism these accelerators provide requires the designs and implementations of productive programming models. In this paper, we explore support of multiple accelerators in high-level programming models. We design novel language extensions to OpenMP to support offloading data and computation regions to multiple accelerators (devices). These extensions allow for distributing data and computation among a list of devices via easy-to-use annotation interfaces, including specifying the distribution of multi-dimensional arrays and declaring shared data regions among accelerators. Computation distribution is realized by partitioning a loop iteration space among accelerators. We implement mechanisms to marshal/unmarshal and to move data of noncontiguous array subre...

• Type: Journal
• Year: 2015
• Where: PPOPP
• Authors: Yonghong Yan 0001, Pei-Hung Lin, Chunhua Liao, Bronis R. de Supinski, Daniel J. Quinlan
• Keywords: Distributed And Parallel Computing, PPOPP 2015