Triangulating molecular surfaces on multiple GPUs

Current GPU-based workstations are inadequate to triangulate and rendering large molecular datasets with thousands and hundreds of thousands, not to say millions, of atoms. The problem is not so the lack of processing power, but the memory limitations of current GPU graphics cards. For example, the NVidia GeForce GTX 590 graphics card comes with two 1.5GB GPUs. We tackle here this problem using a OpenMP-CUDA solution that runs on a loosely-coupled GPU cluster. Basically, we propose a fast, scalable, parallel triangulation algorithm for molecular surfaces that takes advantage of multicore processors of CPUs and GPUs of modern hardware architectures, where each CPU core works as the master of a single GPU, being the processing burden distributed over the CPU cores available in a single computer or a cluster. As much as we know, this is the first marching cubes algorithm that triangulates molecular surfaces on multiple GPUs using CUDA and OpenMP.