Accelerating Electrostatic Surface Potential Calculation with Multiscale Approximation on Graphics Processing Units

Tools that compute and visualize biomolecular electrostatic surface potential have been used extensively for studying biomolecular function. However, determining the surface potential for large biomolecules on a typical desktop computer can take days or longer using currently available tools and methods. This paper demonstrates how one can take advantage of graphic processing units (GPUs) available in today’s typical desktop computer, together with a multiscale approximation method, to significantly speedup such computations. Specifically, the electrostatic potential computation, using an analytical linearized Poisson Boltzmann (ALPB) method, is implemented on an ATI Radeon 4870 GPU in combination with the hierarchical charge partitioning (HCP) multiscale approximation. This implementation delivers a combined 1800-fold speedup for a 476,040 atom viral capsid

Heterogeneous Task Scheduling for Accelerated OpenMP

Abstract not provide

Complex systems analysis and environmental modeling

Through the algorthmic design patterns of data parallelism and task parallelism, the graphics processing unit (GPU) offers the potential to vastly accelerate discovery and innovation across a multitude of disciplines. For example, the exponential growth in data volume now presents an obstacle for high-throughput data mining in fields such as neuroinformatics and bioinformatics. As such, we present a characterization of a MapReduce-based data-mining application on a general-purpose GPU (GPGPU). Using neuroscience as the application vehicle, the results of our multi-dimensional performance evaluation show that a “one-size-fits-all” approach maps poorly across different GPGPU cards. Rather, a high-performance implementation on the GPGPU should factor in the 1) problem size, 2) type of GPU, 3) type of algorithm, and 4) data-access method when determining the type and level of parallelism. To guide the GPGPU programmer towards optimal performance within such a broad design space, we provide eight general performance characterizations of our data-mining application