New Tridiagonal Systems Solvers on GPU Architectures

Modern GPUs (Graphics Processing Units) offer very high computing power at relatively low cost. Nevertheless, designing efficient algorithms for the GPUs usually requires additional time and effort, even for experienced programmers. On the other hand, tridiagonal systems solvers are an important building block for a wide range of applications. In this paper, we present a new tuning parallel proposal in order to generate new tridiagonal systems solvers. This proposal is based on the combination of a new reduction algorithm (Redundant Reduction-RR) with a tuning proposal to generate efficient parallel prefix algorithms on the GPU. Specifically, we present two new solvers combining RR with two GPU efficient parallel prefix patterns. The performance of the resulting proposals was analyzed using three different CUDA GPUs, obtaining an improvement of up to 20.5x over the CUSPARSE library and 28.9x over CUDPP.