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Massively parallel landscape-evolution modelling using general purpose graphical processing units

机译:使用通用图形处理单元的大规模并行景观演化建模

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As our expectations of what computer systems can do and our ability to capture data improves, the desire to perform ever more computationally intensive tasks increases. Often these tasks, comprising vast numbers of repeated computations, are highly interdependent on each other — a closely coupled problem. The process of Landscape-Evolution Modelling is an example of such a problem. In order to produce realistic models it is necessary to process landscapes containing millions of data points over time periods extending up to millions of years. This leads to non-tractable execution times, often in the order of years. Researchers therefore seek multiple orders of magnitude reduction in the execution time of these models. The massively parallel programming environment offered through General Purpose Graphical Processing Units offers the potential for multiple orders of magnitude speedup in code execution times. In this paper we demonstrate how the time dominant parts of a Landscape- Evolution Model can be recoded for a massively parallel architecture providing two orders of magnitude reduction in execution time.
机译:随着我们对计算机系统可以做什么的期望以及我们捕获数据的能力的提高,对执行更多计算密集型任务的需求也越来越高。通常,这些包含大量重复计算的任务彼此高度相关,这是一个紧密耦合的问题。景观演化建模的过程就是此类问题的一个示例。为了生成逼真的模型,有必要在长达数百万年的时间段内处理包含数百万个数据点的景观。这导致难以处理的执行时间,通常为数年左右。因此,研究人员寻求在这些模型的执行时间上减少多个数量级。通用图形处理单元提供的大规模并行编程环境为代码执行时间提供了多个数量级加速的潜力。在本文中,我们演示了如何将景观演化模型的时间主导部分重新编码为大规模并行体系结构,从而在执行时间上减少两个数量级。

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