Modern graphics processing units (GPUs) contain hundreds of arithmetic units and can be harnessed to provide tremendous acceleration for many numerically intensive scientific applications. The increased flexibility of the most recent generation of GPU hardware combined with high level GPU programming languages such as CUDA have unlocked this computational power and made it much more accessible to computational scientists. The key to effective utilization of GPUs for scientific computing is the design and implementation of efficient data-parallel algorithms that can scale to hundreds of tightly coupled processing units. Many molecular modeling applications are well suited to GPUs, due to their extensive computational requirements, and because they lend themselves to parallel processing implementations. The use of multiple GPUs can bring even more computational power to bear on highly parallelizable computational problems. 

GPU-accelerated applications: Molecular dynamics; Molecular orbital display; Ion placement; Multi-GPU Coulomb summation; Microscopy.