Association for Computing Machinery
In this paper, the authors describe parallelization of Interior-Point Method (IPM) aimed at achieving high scalability on large-scale Chip Multi-Processors (CMPs). IPM is an important computational technique used to solve optimization problems in many areas of science, engineering and finance. IPM spends most of its computation time in a few sparse linear algebra kernels. While each of these kernels contains a large amount of parallelism, sparse irregular datasets seen in many optimization problems make parallelism difficult to exploit.