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In this work, we present a new scalable incomplete LU factorization frameworkcalled Javelin to be used as a preconditioner for solving sparse linear systemswith iterative methods. Javelin allows for improved parallel factorization onshared-memory many-core systems by packaging the coefficient matrix into aformat that allows for high performance sparse matrix-vector multiplication andsparse triangular solves with minimal overheads. The framework achieves thesegoals by using a collection of traditional permutations, point-to-point threadsynchronizations, tasking, and segmented prefix scans in a conventionalcompressed sparse row format. Moreover, this framework stresses the importanceof co-designing dependent tasks, such as sparse factorization and triangularsolves, on highly-threaded architectures. Using these changes, traditionalfill-in and drop tolerance methods can be used, while still being able to haveobserved speedups of up to ~42x on 68 Intel Knights Landing cores and ~12x on14 Intel Haswell cores.

Javelin: A Scalable Implementation for Sparse Incomplete LU Factorization