A large-grained parallel algorithm for nonlinear eigenvalue problems and its implementation using OmniRPC

The nonlinear eigenvalue problem plays an important role in various fields such as nonlinear elasticity, electronic structure calculation and theoretical fluid dynamics. We recently proposed a new algorithm for the nonlinear eigenvalue problem, which reduces the original problem to a smaller generalized linear eigenvalue problem with Hankel coefficient matrices through complex contour integral. This algorithm has a unique feature that it can find all the eigenvalues in a closed curve on the complex plane. Moreover, it has large-grain parallelism and is suited for execution in a Grid environment. In this paper, we study the numerical properties of our algorithm theoretically. In particular, we analyze the effect of numerical integration to the computed eigenvalues and give a guideline on how to choose the size of the Hankel matrices properly. Also, we show the parallel performance of our algorithm implemented on a PC cluster using OmniRPC, a Grid RPC system. Parallel efficiency of 75% is achieved when solving a nonlinear eigenvalue problem of order 1000 using 14 processors.