On improving the performance of the linear
solver restarted GMRES
Allison
Baker
Applied Math Department
Abstract:
Two approaches to improving
the performance, i.e. time to solution, of an iterative linear solver algorithm
are particularly viable. First, algorithmic changes that improve convergence
properties result in faster convergence due to fewer overall floating-point
operations. Second, modifications to an algorithm that reduce the movement of
data through memory greatly impact performance because of the growing gap
between CPU performance and memory access time. Ideally, a balance is
achieved between improving the efficiency of an iterative linear solver from a
memory-usage standpoint and maintaining favorable numerical properties. We
discuss the restarted generalized minimum residual (GMRES) method in the
context of both approaches to improving performance.