GpuC: Data parallel language extension to CUDA

In recent years, Graphics Processing Units (GPUs) have emerged as a powerful accelerator for general-purpose computations. Current approaches to program GPUs are still relatively low-level programming models such as Compute Unified Device Architecture (CUDA), a programming model from NVIDIA, and Open Compute Language (OpenCL), created by Apple in cooperation with others. These two programming models have all the complexity of parallel programming such as breaking up the task into smaller tasks, assigning the smaller tasks to multiple CPUs to work on simultaneously, and coordinating the CPUs. There is a growing need to lower the complexity of programming these devices. In this paper, we propose a data-parallel loop (forall) extension to the CUDA programming model. We describe our prototype compiler named GpuC.