A GPU-Accelerated Hybridizable Discontinuous Galerkin Method for Linear Elasticity (Communications in Computational Physics, 2019)
We design and analyze an efficient GPU-accelerated hybridizable discontinuous Galerkin method for linear elasticity. Performance analysis of the method isdone using the state-of-the-art Time-Accuracy-Size (TAS) spectrum. TAS is a new performance measure which takes into account the accuracy of the solution. Standardperformance measures, like floating point operations or run-time, are not completelyappropriate for gauging the performance of approximations of continuum mechanicsproblems, as they neglect the solutions accuracy. A standard roofline model demonstrates that our method is utilizing computational resources efficiently, and as such,significant speed ups over a serial implementation are obtained. By combining traditional performance measures and the novel time-accuracy measures [7] into our performance model, we are able to draw more complete conclusions about which discretizations are best suited for an application. Several numerical experiments validateand verify our numerical scheme.