A general predictive performance model for wavefront algorithms on clusters of SMPs

We propose and validate a closed-end, analytical, general, predictive performance model for applications based on wavefront algorithms on clusters of SMPs. Wavefront algorithms are ubiquitous in parallel computing, since they represent a means of enabling parallelism in computations that contain recurrences. Our particular interest in wavefront algorithms derives from their use in discrete ordinates neutral particle transport computations representative of ASCI, but other important uses are well known. The proposed model captures the tradeoff between processor utilization and communication requirements characteristics of wavefront algorithms. The general model can predict the performance of this class of applications on distributed architectures with a network of lower dimensionality compared to that of an MPP, of which clusters of SMPs are one example. We validate the model using a compact-application from the ASCI workload on a large-scale cluster of SGI Origin 2000s in existence at the Los Alamos National Laboratory. The proposed model validates well on all clusters configurations utilized.