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StreamMR: An Optimized MapReduce Framework for AMD GPUs

机译:StreamMR:针对AMD GPU的优化MapReduce框架

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MapReduce is a programming model from Google that facilitates parallel processing on a cluster of thousands of commodity computers. The success of MapReduce in cluster environments has motivated several studies of implementing MapReduce on a graphics processing unit (GPU), but generally focusing on the NVIDIA GPU. Our investigation reveals that the design and mapping of the MapReduce framework needs to be revisited for AMD GPUs due to their notable architectural differences from NVIDIA GPUs. For instance, current state-of-the-art MapReduce implementations employ atomic operations to coordinate the execution of different threads. However, atomic operations can implicitly cause inefficient memory access, and in turn, severely impact performance. In this paper, we propose Streamer, an OpenCL MapReduce framework optimized for AMD GPUs. With efficient atomic-free algorithms for output handling and intermediate result shuffling, Stream MR is superior to atomic-based MapReduce designs and can outperform existing atomic-free MapReduce implementations by nearly five-fold on an AMD Radeon HD 5870.
机译:MapReduce是Google的一种编程模型,可促进在成千上万台商用计算机的群集上进行并行处理。 MapReduce在群集环境中的成功激发了几项研究在图形处理单元(GPU)上实现MapReduce的研究,但通常集中在NVIDIA GPU上。我们的调查表明,由于AMD GPU与NVIDIA GPU的显着架构差异,因此需要重新审视MapReduce框架的设计和映射。例如,当前最新的MapReduce实现采用原子操作来协调不同线程的执行。但是,原子操作可能会隐式导致内存访问效率低下,进而严重影响性能。在本文中,我们提出了Streamer,这是一种针对AMD GPU优化的OpenCL MapReduce框架。凭借高效的无原子算法进行输出处理和中间结果转换,Stream MR优于基于原子的MapReduce设计,并且在AMD Radeon HD 5870上可比现有的无原子MapReduce实施高出近五倍。

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