Efficient sparse Cholesky factorization on a parallel SIMD computer


We investigate the effect of load balancing when performing Cholesky factorization on a SIMD computer. In particular we describe a supernodal algorithm for performing sparse Cholesky factorization. The way the matrix is mapped onto the processors has significant effect on its efficiency. We show that this assignment problem can be modeled as a graph coloring problem in a weighted graph. By a simple greedy algorithm, we obtain substantial speed-up compared to previously suggested data mapping schemes. Experimental runs have been made on a 16K processor MasPar MP-2 parallel computer using symmetric test matrices with irregular sparsity structure. On these problems our implementation achieves performance rates of well above 200 Mflops in double precision arithmetic.