2,529 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Faster inference from state space models via GPU computing

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    Funding: C.F.-J. is funded via a doctoral scholarship from the University of St Andrews, School of Mathematics and Statistics.Inexpensive Graphics Processing Units (GPUs) offer the potential to greatly speed up computation by employing their massively parallel architecture to perform arithmetic operations more efficiently. Population dynamics models are important tools in ecology and conservation. Modern Bayesian approaches allow biologically realistic models to be constructed and fitted to multiple data sources in an integrated modelling framework based on a class of statistical models called state space models. However, model fitting is often slow, requiring hours to weeks of computation. We demonstrate the benefits of GPU computing using a model for the population dynamics of British grey seals, fitted with a particle Markov chain Monte Carlo algorithm. Speed-ups of two orders of magnitude were obtained for estimations of the log-likelihood, compared to a traditional ‘CPU-only’ implementation, allowing for an accurate method of inference to be used where this was previously too computationally expensive to be viable. GPU computing has enormous potential, but one barrier to further adoption is a steep learning curve, due to GPUs' unique hardware architecture. We provide a detailed description of hardware and software setup, and our case study provides a template for other similar applications. We also provide a detailed tutorial-style description of GPU hardware architectures, and examples of important GPU-specific programming practices.Publisher PDFPeer reviewe

    Graduate Catalog of Studies, 2023-2024

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    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Undergraduate Catalog of Studies, 2022-2023

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    CUDA-Optimized GPU Acceleration of 3GPP 3D Channel Model Simulations for 5G Network Planning

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    Simulation of massive multiple-input multiple-output (MIMO) channel models is becoming increasingly important for testing and validation of fifth-generation new radio (5G NR) wireless networks and beyond. However, simulation performance tends to be limited when modeling a large number of antenna elements combined with a complex and realistic representation of propagation conditions. In this paper, we propose an efficient implementation of a 3rd Generation Partnership Project (3GPP) three-dimensional (3D) channel model, specifically designed for graphics processing unit (GPU) platforms, with the goal of minimizing the computational time required for channel simulation. The channel model is highly parameterized to encompass a wide range of configurations required for real-world optimized 5G NR network deployments. We use several compute unified device architecture (CUDA)-based optimization techniques to exploit the parallelism and memory hierarchy of the GPU. Experimental data show that the developed system achieves an overall speedup of about 240× compared to the original C++ model executed on an Intel processor. Compared to a design previously accelerated on a datacenter-class field programmable gate array (FPGA), the GPU design has 33.3 % higher single precision performance, but for 7.5 % higher power consumption. The proposed GPU accelerator can provide fast and accurate channel simulations for 5G NR network planning and optimization

    Evaluating Architectural Safeguards for Uncertain AI Black-Box Components

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    Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability
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