Graphics Processing Units (GPUs) are evolving into powerful general purpose computing platforms. At first, GPU performance was driven by the requirements of 3D graphics computer games. To fit this workload, a GPU is a many-core processor suitable for the data-parallel programming paradigm. Today, GPUs come with hundreds of processing elements and a theoretical single precision floating point performance in the teraflop range. Because of the computing power of modern GPUs, programmers are increasingly interested in making use of them for non-graphics applications. This desire has given rise to the research field that studies General Purpose Computations on GPUs (GPGPU). The manufacturers of GPUs are also acknowledging this trend and are tailoring their GPUs to meet both the desires of those playing games and the GPGPU community. CUDA is NVIDIA’s tool-set for GPGPU programming on their GPUs. CUDA is a big improvement for the GPGPU programmer compared to what was available before. In the early days, the GPGPU programmer was forced to express the algorithm being implemented as a computer graphics computation. CUDA provides a C compiler and a set

We present a novel high-level parallel programming model for graphics processing units (GPUs). We embed GPU kernels as data-parallel array computations in the purely functional language Haskell. GPU and CPU computations can be freely interleaved with the type system tracking the two different modes of computation. The embedded language of array computations is sufficiently limited that our system can automatically isolate and extract these computations and compile them to efficient GPU code. In this paper, we outline our approach and present the results of a few preliminary benchmarks. 1.