High-performance global optimization and signal processing on multicore, GPU, and hybrid architectures

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Computation is now considered as one of the three pillars of contemporary science, along with theory and experimentation. The proposed research will contribute to interdisciplinary computational science by exploiting the potential of graphics processing units (GPUs), multicore computer systems, and hybrid GPU/multicore architectures to solve complex problems in the related areas of global optimization and signal processing. Many investigators in diverse fields are exploring GPUs, originally developed to speed simple arithmetic operations for computer graphics, for general purpose computation. This research will concentrate on identifying and exploiting data- and task-level parallelism for optimization and signal processing problems that require the processing of large amounts of data. Optimization will be applied to multidimensional scaling, an important component in information visualization and analysis for geospatial problems, and to particle swarm techniques, which will be used to calibrate large mathematical models, such as those that are common in spatial econometrics. Global optimization will also be used in blind source separation, which has implications for the development of noise-cancelling hearing aids. Furthermore, signal processing on new architectures can facilitate geospatial research by enabling efficient analysis of streaming data from sensor networks and weather stations. These computational areas have great societal and economic importance, as improved high-dimensional global optimization approaches will facilitate high-fidelity simulations and models needed to solve important scientific and engineering problems. Signal processing algorithms utilizing the new architectures allow more efficient solutions to problems in biomedicine and real-time applications. The goal of the proposed research is to investigate and analyze the capabilities and limitations of multicore/GPU algorithms that can be incorporated into applications in simulation and modeling, biomedical signal processing, and in geospatial data analysis. As most desktop and laptop computers are now available with both multicore chips and GPUs, exploitation of these capabilities is increasingly relevant.