Sparse direct solvers play a vital role in large-scale high performance computing in science and engineering. Existing distributed sparse direct methods employ multifrontal/supernodal patterns to aggregate columns of nearly identical forms and to exploit dense basic linear algebra subprograms (BLAS) for computation. However, such a data layout may bring more unevenness when the structure of the input matrix is not ideal, and using dense BLAS may waste many floating-point operations on zero fill-ins.

Sparse LU factorization is one of the key building blocks of sparse direct solvers and often dominates the computing time of circuit simulation programs. Existing GPU-accelerated sparse LU factorization methods either offload relatively small dense matrix-matrix multiplications to GPU cores, or extract level-set information to parallelize elimination operations in each level. However, because of the insufficient parallelism, neither of the methods can saturate a large amount of compute units on modern GPUs.We in this paper propose a synchronization-free sparse LU factorization algorithm called SFLU. To saturate GPU cores, our method lets each thread block eliminate a column and runs all the thread blocks at the same time. Through communicating dependency information stored on global memory, all the thread blocks either busy wait to run or get updated by their previous columns. Because elimination of all the columns work concurrently, our method avoids any barrier synchronization and saturates GPU resources. By benchmarking over 1000 sparse matrices on an NVIDIA Titan RTX GPU, our SFLU outperforms SuperLU and GLU by a factor of on average 155.71 and 8.21 (up to 3585.62 and 252.66), respectively.