Effective Kernel Mapping for OpenCL Applications in Heterogeneous Platforms

Many core accelerators are being deployed in many systems to improve the processing capabilities. In such systems, application mapping need to be enhanced to maximize the utilization of the underlying architecture. Especially in GPUs mapping becomes critical for multi-kernel applications as kernels may exhibit different characteristics. While some of the kernels run faster on GPU, others may refer to stay in CPU due to the high data transfer overhead. Thus, heterogeneous execution may yield to improved performance compared to executing the application only on CPU or only on GPU. In this paper, we propose a novel profiling-based kernel mapping algorithm to assign each kernel of an application to the proper device to improve the overall performance of an application. We use profiling information of kernels on different devices and generate a map that identifies which kernel should run on where to improve the overall performance of an application. Initial experiments show that our approach can effectively map kernels on CPU and GPU, and outperforms to a CPU-only and GPU-only approach.