Sparse matrix computations on manycore GPU’s

Abstract

Modern microprocessors are becoming increasingly parallel devices, and GPUs are at the leading edge of this trend. Designing parallel algorithms for manycore chips like the GPU can present interesting challenges, particularly for computations on sparse data structures. One particularly common example is the collection of sparse matrix solvers and combinatorial graph algorithms that form the core of many physical simulation techniques. Although seemingly irregular, these operations can often be implemented with data parallel operations that map very well to massively parallel processors.