In this work, we describe ParSy, a framework that uses a novel inspection strategy
along with a simple code transformation to optimize parallel sparse algorithms for shared
memory processors. Unlike existing approaches that can suffer from load imbalance and
excessive synchronization, ParSy uses a novel task coarsening strategy to create well-balanced
tasks that can execute in parallel, while maintaining locality of memory accesses.
Code using the ParSy inspector and transformation outperforms existing
highly-optimized sparse matrix algorithms such as Cholesky factorization on
multi-core processors with speedups of 2.8× and 3.1× over the MKL Pardiso and PaStiX
libraries respectively.