Symbolic performance prediction of scalable parallel programs

Recent advances in the power of parallel computers have made them attractive for solving large computational problems. Scalable parallel programs are particularly well suited to Massively Parallel Processing (MPP) machines since the number of computations can be increased to match the available number of processors. Performance tuning can be particularly difficult for these applications since it must often be performed with a smaller problem size than that targeted for eventual execution. This research develops a performance prediction methodology that addresses this problem through symbolic analysis of program source code. Algebraic manipulations can then be performed on the resulting analytical model to determine performance for scaled up applications on different hardware architectures