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The GPU on irregular computing: Performance issues and contributions

机译:关于不规则计算的GPU:性能问题和贡献

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The paper describes a set of strategies for mapping irregular codes onto commodity graphics hardware. We start identifying the resources that current GPUs contain for solving indirect array accesses entirely on hardware, like vertices, textures and color tables. We then show how multiple indirections can be mapped onto the graphics pipeline, basically taking advantage of its streaming architecture for sequencing the indirections through subsequent pipeline stages. Our techniques are applied over typical irregular kernels like the sparse matrix-vector multiply and the Euler solver. Execution times on the GeForce Series consistently outperform the Pentium 4 and Athlon 64 processors, with performance depending on floating-point precision.
机译:本文描述了一组用于将不规则代码映射到商品图形硬件上的一组策略。我们开始识别当前GPU的资源,用于解决完全在硬件上的间接数组访问,如顶点,纹理和颜色表。然后,我们展示了如何将多个间接映射到图形管道上,基本上利用其流式架构,用于通过后续流水线级进行排序。我们的技术应用于典型的不规则内核,如稀疏矩阵矢量乘法和欧拉求解器。在GeForce系列上的执行时间始终优于奔腾4和Athlon 64处理器,这取决于浮点精度。

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