This paper examines an algorithm using dual OpenCL image buffers to optimize data streaming for ensemble processing and visualization. Image buffers were utilized because they allow cached memory access, unlike simple data buffers, which are more commonly used. OpenCL image object performance was improved by allowing upload and mapping into one buffer to occur concurrently with mapping and/or processing of data in another buffer. This technique was applied in an interactive application allowing multiple flood extent maps to be combined into a single image, and allowing users to vary input image sets in real time. The efficiency of this technique was tested by varying both dimensions of input images and number of iterations; computation scaled linearly with number of input images, with best results achieved using ~4k images. Tests were performed to determine the rate at which data could be moved from data buffers to image buffers, examining a large range of possible image buffer dimensions. Additional tests examined kernel runtimes with different image and buffer variants. Limitations of the algorithm and possible applications are discussed.

中文翻译：

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图像与数据缓冲区的功效：利用OpenCL优化交互式应用程序以实现科学可视化

本文研究了一种使用双OpenCL图像缓冲区来优化数据流以进行整体处理和可视化的算法。之所以使用图像缓冲区，是因为它们允许缓存的内存访问，这与更常用的简单数据缓冲区不同。通过允许将上载和映射到一个缓冲区与另一缓冲区中的数据的映射和/或处理同时发生，提高了OpenCL图像对象的性能。这项技术应用于交互式应用程序中，该应用程序可以将多个泛洪范围图组合到一个图像中，并允许用户实时更改输入图像集。通过改变输入图像的尺寸和迭代次数来测试该技术的效率。计算与输入图像的数量成线性比例关系，使用约4k图像可获得最佳结果。进行了测试以确定数据从数据缓冲区移动到图像缓冲区的速率，并检查了可能的图像缓冲区尺寸范围。其他测试检查了具有不同映像和缓冲区变体的内核运行时。讨论了算法的局限性和可能的​​应用。