# Javelin: A Scalable Implementation for Sparse Incomplete LU Factorization

## TL;DR

Javelin is a scalable framework for incomplete LU factorization that significantly accelerates sparse linear system solutions on many-core architectures through optimized parallelization techniques.

## Contribution

It introduces a new parallel incomplete LU factorization framework that enhances performance on shared-memory systems using task co-design and efficient data formats.

## Key findings

- Achieves up to 42x speedup on Intel Knights Landing cores.
- Supports traditional fill-in and drop tolerance methods.
- Improves parallel factorization and triangular solves.

## Abstract

In this work, we present a new scalable incomplete LU factorization framework called Javelin to be used as a preconditioner for solving sparse linear systems with iterative methods. Javelin allows for improved parallel factorization on shared-memory many-core systems by packaging the coefficient matrix into a format that allows for high performance sparse matrix-vector multiplication and sparse triangular solves with minimal overheads. The framework achieves these goals by using a collection of traditional permutations, point-to-point thread synchronizations, tasking, and segmented prefix scans in a conventional compressed sparse row format. Moreover, this framework stresses the importance of co-designing dependent tasks, such as sparse factorization and triangular solves, on highly-threaded architectures. Using these changes, traditional fill-in and drop tolerance methods can be used, while still being able to have observed speedups of up to ~42x on 68 Intel Knights Landing cores and ~12x on 14 Intel Haswell cores.