high performance computing on graphics processing units: hgpu.org

The world of HPC is undergoing rapid changes and computer architectures capable to achieve high performance have broadened. The irruption in the scene of computational accelerators, like GPUs, is increasing performance while maintaining low cost per GFLOP, thus expanding the popularity of HPC. However, it is still difficult to exploit the new complex processor hierarchies. To adapt the message passing model to program heterogeneous CPU+GPUs environments is not an easy task. Furthermore, message passing does not seem to be the best choice from the programmer point of view. Traditional shared memory approaches like OpenMP are interesting to ease the popularization of these platforms, but the fact is that GPU devices are connected to the CPU through a bus and have a separate memory space. We need to find a way to deal with this issue at programming language level, otherwise, developers will spend most of their time focusing on low-level code details instead of algorithmic enhancements. The recent advent of the OpenACC standard for heterogeneous computing represents an effort in the direction of leveraging the development effort. This initiative, combined with future releases of the OpenMP standard, will converge into a fully heterogeneous framework that will cope the programming requirements of future computer architectures. In this work we present preliminary results of accULL, a novel implementation of the OpenACC standard, based on a source-to-source compiler and a runtime library. To our knowledge, our approach is the first providing support for both OpenCL and CUDA platforms under this new standard.