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首页> 外文期刊>Concurrency and computation: practice and experience >Performance study on CUDA GPUs for parallelizing the local ensemble transformed Kalman filter algorithm
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Performance study on CUDA GPUs for parallelizing the local ensemble transformed Kalman filter algorithm

机译：CUDA GPU用于并行化局部集成变换卡尔曼滤波算法的性能研究

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摘要

Modern graphics cards provide computational capabilities that exceed current CPUs. As one of the computational intensive problems, numerical weather prediction has the opportunity to benefit from the massive number of threads and large memory throughput in the graphics architecture. In this paper, we present the key steps to integrate the Compute Unified Device Architecture (CUDA) programming framework for one key component in numerical weather prediction, the data assimilation algorithm, which incorporates the observational data into the model to produce the best initial condition in the next prediction. The data assimilation algorithm we studied in this paper exhibits good localization and favors parallelism. To maximize the throughput of the graphics card, over a million CUDA threads, global memory coalescing, and fast graphics shared memory are utilized. We also demonstrate the differences in the advancement of GPU architectures from the GTX 200 series to Fermi. The experiments are carried out separately on a GTX 260 (GTX 200 series) and a GTX 460 (Fermi) graphics card. Results show an improvement of 72.1 × speedup running on the GTX 260 and 92.7× speedup on the GTX 460. The results provide attractive evidence for applying CUDA GPUs to high demanding scientific computation realms.
机译：现代图形卡提供的计算能力超过了当前的CPU。作为计算密集型问题之一，数值天气预报有机会受益于图形体系结构中大量的线程和大量的内存吞吐量。在本文中，我们提出了将数值天气预报中的一个关键组件集成到计算统一设备体系结构（CUDA）编程框架的关键步骤，即数据同化算法，该算法将观测数据整合到模型中以产生最佳的初始条件。下一个预测。本文研究的数据同化算法具有良好的定位性，并支持并行性。为了最大化图形卡的吞吐量，使用了超过一百万个CUDA线程，全局内存合并和快速图形共享内存。我们还将展示从GTX 200系列到Fermi的GPU体系结构的进步差异。实验分别在GTX 260（GTX 200系列）和GTX 460（Fermi）图形卡上进行。结果显示，GTX 260的运行速度提高了72.1倍，GTX 460的运行速度提高了92.7倍。结果为将CUDA GPU应用于高要求的科学计算领域提供了诱人的证据。

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