GPGPU计算引擎上分类树的推测并行计算
Speculative Parallel Evaluation Of Classification Trees On GPGPU Compute Engines
摘要：
我们研究的问题，通过使用GPU的在线和实时应用程序的优化分类树评价。针对图像分割中经常使用的连续属性树，我们首先将现有的串行和数据并行评价算法建立在坚实的基础上。然后，我们介绍了一个推测性的并行算法设计的单指令，多数据（SIMD）架构中常见的GPU。理论分析表明，如何运行时间的数据和推测分解比较假设独立的处理器。为了比较SIMD环境中的算法，我们实现了CUDA 2.0架构的机器和比较时序的串行CPU实现。各种优化及其效果进行了讨论，并给出了所有算法的结果。我们的具体测试表明，与数据分解相比，推测算法将运行时间提高了25%。
英文摘要：
We examine the problem of optimizing classification tree evaluation for on-line and real-time applications by using GPUs. Looking at trees with continuous attributes often used in image segmentation, we first put the existing algorithms for serial and data-parallel evaluation on solid footings. We then introduce a speculative parallel algorithm designed for single instruction, multiple data (SIMD) architectures commonly found in GPUs. A theoretical analysis shows how the run times of data and speculative decompositions compare assuming independent processors. To compare the algorithms in the SIMD environment, we implement both on a CUDA 2.0 architecture machine and compare timings to a serial CPU implementation. Various optimizations and their effects are discussed, and results are given for all algorithms. Our specific tests show a speculative algorithm improves run time by 25% compared to a data decomposition.
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