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The actor model of computation has been designed for a seamless support ofconcurrency and distribution. However, it remains unspecific about dataparallel program flows, while available processing power of modern many corehardware such as graphics processing units (GPUs) or coprocessors increases therelevance of data parallelism for general-purpose computation.  In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework(CAF). This offers a high level interface for accessing any OpenCL devicewithout leaving the actor paradigm. The new type of actor is integrated intothe runtime environment of CAF and gives rise to transparent message passing indistributed systems on heterogeneous hardware. Following the actor logic inCAF, OpenCL kernels can be composed while encapsulated in C++ actors, henceoperate in a multi-stage fashion on data resident at the GPU. Developers arethus enabled to build complex data parallel programs from primitives withoutleaving the actor paradigm, nor sacrificing performance. Our evaluations oncommodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linearscaling behavior when offloading larger workloads. For sub-second duties, theefficiency of offloading was found to largely differ between devices. Moreover,our findings indicate a negligible overhead over programming with the nativeOpenCL API.

OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF