The graphics processing unit (GPU) has emerged as a computational accelerator that dramatically reduces the time to discovery in high-end computing (HEC). However, while today’s state-of-the-art GPU can easily reduce the execution time of a parallel code by many orders of magnitude, it arguably comes at the expense of significant power and energy consumption. For example, the NVIDIA GTX 280 video card is rated at 236 watts, which is as much as the rest of a compute node, thus requiring a 500-W power supply. As a consequence, the GPU has been viewed as a “nongreen” computing solution. This paper seeks to characterize, and perhaps debunk, the notion of a “power-hungry GPU ” via an empirical study of the performance, power, and energy characteristics of GPUs for scientific computing. Specifically, we take an important biological code that runs in a traditional CPU environment and transform and map it to a hybrid CPU+GPU environment. The end result is that our hybrid CPU+GPU environment, hereafter referred to simply as GPU environment, delivers an energy-delay product that is multiple orders of magnitude better than a traditional CPU environment, whether unicore or multicore. 1.

Abstract. Efficiency and accuracy are two major concerns in numerical solutions of the Poisson-Boltzmann equation for applications in chemistry and biophysics. Recent developments in boundary element methods, interface methods, adaptive methods, finite element methods, and other approaches for the Poisson-Boltzmann equation as well as related mesh generation techniques are reviewed. We also discussed the challenging problems and possible future work, in particular, for the aim of biophysical applications.

PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations

PDB_Hydro: incorporating dipolar solvents with variable density in the Poisson–Boltzmann treatment of macromolecule electrostatics

PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations