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Using multiple graphics cards as a general purpose parallel computer: applications to computer vision

机译：将多个图形卡用作通用并行计算机：计算机视觉的应用

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Pattern recognition and computer vision tasks are computationally intensive, repetitive, and often exceed the capabilities of the CPU, leaving little time for higher level tasks. We present a novel computer architecture which uses multiple commodity computer graphics devices to perform pattern recognition and computer vision tasks many times faster than the CPU. This is a parallel computing architecture that is quickly and easily constructed from the readily available hardware. It is based on parallel processing done on multiple graphics processing units (GPUs). An eigenspace image recognition approach is implemented on this parallel graphics architecture. This paper discusses methods of mapping computer vision algorithms to run efficiently on multiple graphics devices to maximally utilize the underlying graphics hardware. The additional memory and memory bandwidth provided by the graphics hardware provided for significant speedup of the eigenspace approach. We show that graphics devices parallelize well and provide significant speedup over a CPU implementation, providing an immediately constructible low cost architecture well suited for pattern recognition and computer vision.
机译：模式识别和计算机视觉任务是计算密集型的，重复性的，并且通常超过CPU的功能，从而几乎没有时间处理更高级别的任务。我们提出了一种新颖的计算机体系结构，该体系结构使用多个商用计算机图形设备执行模式识别和计算机视觉任务，其速度比CPU快许多倍。这是一种并行计算体系结构，可以从易于使用的硬件中快速轻松地构建它。它基于在多个图形处理单元（GPU）上完成的并行处理。本征图像识别方法是在此并行图形体系结构上实现的。本文讨论了映射计算机视觉算法以在多个图形设备上有效运行以最大程度利用基础图形硬件的方法。图形硬件提供的额外内存和内存带宽可显着提高本征空间方法的速度。我们证明了图形设备可以很好地并行化，并且在CPU实现上提供了显着的加速，提供了一种可立即构建的低成本体系结构，非常适合模式识别和计算机视觉。

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