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Exploring graphics processor performance for general purpose applications

机译:探索通用应用程序的图形处理器性能

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Graphics processors are designed to perform many floating-point operations per second. Consequently, they are an attractive architecture for high-performance computing at a low cost. Nevertheless, it is still not very clear how to exploit all their potential for general-purpose applications. In this work we present a comprehensive study of the performance of an application executing on the GPU. In addition, we analyze the possibility of using the graphics card to extend the life-time of a computer system. In our experiments we compare the execution on a mid-class GPU (NVIDIA GeForce FX 5700LE) with a high-end CPU (Pentium 4 3.2 GHz). The results show that to achieve high speedup with the GPU you need to: (1) format the vectors into two-dimensional arrays; (2) process large data arrays; and (3) perform a considerable amount of operations per data element. Finally, we study the performance when upgrading a low-end system by simply adding a GPU. This solution is cheaper, results in smaller power consumption and achieves higher speedup (8.1x versus 1.3x) than a full upgrade to a new high-end system.
机译:图形处理器被设计为每秒执行许多浮点运算。因此,它们是一种用于低成本高性能计算的有吸引力的体系结构。尽管如此,如何利用它们在通用应用程序中的全部潜力仍然不是很清楚。在这项工作中,我们对在GPU上执行的应用程序的性能进行了全面的研究。此外,我们分析了使用图形卡来延长计算机系统寿命的可能性。在我们的实验中,我们比较了中型GPU(NVIDIA GeForce FX 5700LE)和高端CPU(Pentium 4 3.2 GHz)的执行性能。结果表明,要使用GPU达到较高的速度,您需要:(1)将向量格式化为二维数组; (2)处理大数据数组; (3)每个数据元素执行大量操作。最后,我们仅通过添加GPU来研究升级低端系统时的性能。与完全升级到新的高端系统相比,该解决方案更便宜,功耗更小,并且提速更高(8.1倍对1.3倍)。

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