High Precision BLAS on FPGA enhanced Computers
The emergence of high density reconfigurable hardware devices gives scientists and engineers an option to accelerating their numerical computing applications on low cost but powerful "FPGA enhanced computers" In this paper, we introduced our efforts towards improving the computational performance of Basic Linear Algebra Subprograms (BLAS) by FPGA specific algorithms/methods. Our study focus on three BLAS subroutines: floating point summation, matrix vector multiplication, and matrix matrix multiplication. They represent all three levels of BLAS functionalities, and their sustained computational performances are either memory bandwidth bounded or computation bounded. By proposing the group alignment based floating point summation method and applying this technique to other subroutines, we significantly improved their sustained computational performance and reduced numerical errors with moderate FPGA resources consumed. Comparing with existing FPGA based implementations, our designs are efficient and compact with improved numerical accuracy and stability.
ERSA