SciPAL

Abstract

We present SciPAL (scientific parallel algorithms library), a C ++-based, hardware-independent open-source library. Its core is a domain-specific embedded language for numerical linear algebra. The main fields of application are finite element simulations, coherent optics and the solution of inverse problems. Using SciPAL algorithms can be stated in a mathematically intuitive way in terms of matrix and vector operations. Existing algorithms can easily be adapted to GPU-based computing by proper template specialization. Our library is compatible with the finite element library deal .II and provides a port of deal.II's most frequently used linear algebra classes to CUDA (NVidia's extension of the programming languages C and C ++ for programming their GPUs). SciPAL 's operator-based API for BLAS operations particularly aims at simplifying the usage of NVidia's CUBLAS. For non-BLAS array arithmetic SciPAL 's expression templates are able to generate CUDA kernels at compile time. We demonstrate the benefits of SciPAL using the iterative principal component analysis as example which is the core algorithm for the spike-sorting problem in neuroscience.