Fluidic Kernels

Abstract

Programming heterogeneous computing systems with Graphics Processing Units (GPU) and multi-core CPUs in them is complex and time-consuming. OpenCL has emerged as an attractive programming framework for heterogeneous systems. But utilizing multiple devices in OpenCL is a challenge because it requires the programmer to explicitly map data and computation to each device. The problem becomes even more complex if the same OpenCL kernel has to be executed synergistically using multiple devices, as the relative execution time of the kernel on different devices can vary significantly, making it difficult to determine the work partitioning across these devices a priori. Also, after each kernel execution, a coherent version of the data needs to be established.