Efficient Sparse LU Factorization with Lazy Space Allocation Bin Jiang, Steven Richman, Kai Shen, and Tao Yang Department of Computer Science University of California, Santa Barbara CA 93106 Static symbolic factorization coupled with 2D supernode partitioning and asynchronous computation scheduling is a viable approach for sparse LU with dynamic partial pivoting. Our previous implementation, called S+, uses those techniques and achieves high gigaflop rates on distributed memory machines. This paper studies the space requirement of this approach and proposes an optimization strategy called lazy space allocation which acquires memory on-the-fly only when it is necessary. This strategy can effectively control memory usage, especially when static symbolic factorization overestimates fill-ins excessively. Our experiments show that the improved S+ code, which combines this strategy with elimination-forest guided partitioning and scheduling, has sequential time and space cost competitive with SuperLU, is space scalable for solving problems of large sizes on multiple processors, and can deliver up to 10 GFLOPS on 128 Cray 450Mhz T3E nodes.