T. S. Munson and P. D. Hovland, "The FeasNewt Benchmark," Preprint ANL/MCS-P1284-0805, August 2005. [pdf]
We describe the FeasNewt mesh-quality optimization benchmark. The performance of the code is dominated by three phases---gradient evaluation, Hessian evaluation and assembly, and sparse matrix-vector products---that have very different mixtures of floating-point operations and memory access patterns. The code includes an optional runtime data- and iteration-reordering phase, making it suitable for research on irregular memory access patterns. Mesh-quality optimization (or ``mesh smoothing'') is an important ingredient in the solution of nonlinear partial differential equations (PDEs) as well as an excellent surrogate for finite-element or finite-volume PDE solvers.