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首页> 外文期刊>Journal of Computers >High-accuracy Optimization by Parallel Iterative Discrete Approximation and GPU Cluster Computing
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High-accuracy Optimization by Parallel Iterative Discrete Approximation and GPU Cluster Computing

机译：并行迭代离散逼近和GPU集群计算的高精度优化

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High-accuracy optimization is the key component of time-sensitive applications in computer sciences such as machine learning, and we develop single-GPU Iterative Discrete Approximation Monte Carlo Optimization (IDAMCS) and multi-GPU IDA-MCS in our previous research. However, because of the memory capability constrain of GPUs in a workstation, single-GPU IDA-MCS and multi- GPU IDA-MCS may be in low performance or even functionless for optimization problems with complicated shapes such as large number of peaks. In this paper, by the novel idea of parallelizing Iterative Discrete Approximation with CUDA-MPI programming, we develop the GPU cluster version (GPU-cluster) of IDA-MCS with two different parallelization strategies: Domain Decomposition and Local Search, under the style of Single Instruction Multiple Data by CUDA 5.5 and MPICH2, and we exhibit the performance of GPU-cluster IDA-MCS by optimizing complicated cost functions. Computational results show that, by the same number of iterations, for the cost function with millions of peaks, the accuracy of GPU-cluster IDA-MCS is approximately thousands of times higher than that of the conventional method Monte Carlo Search. Computational results also show that, the optimization accuracy from Domain Decomposition IDA-MCS is much higher than that of Local Search IDA-MCS.
机译：高精度优化是诸如机器学习之类的计算机科学中时间敏感型应用程序的关键组成部分，在我们先前的研究中，我们开发了单GPU迭代离散近似蒙特卡洛优化（IDAMCS）和多GPU IDA-MCS。然而，由于工作站中GPU的存储能力的限制，单GPU IDA-MCS和多GPU IDA-MCS可能会因为性能复杂的形状（例如，大量的峰）而导致性能低下甚至无法工作。在本文中，通过将迭代离散近似与CUDA-MPI编程并行化的新颖思想，我们开发了IDA-MCS的GPU群集版本（GPU群集），具有两种不同的并行化策略：域分解和局部搜索，其样式为CUDA 5.5和MPICH2的单指令多数据，并且通过优化复杂的成本函数，我们展示了GPU群集IDA-MCS的性能。计算结果表明，通过相同的迭代次数，对于具有数百万个峰值的代价函数，GPU群集IDA-MCS的精度比传统方法Monte Carlo Search的精度大约高数千倍。计算结果还表明，域分解IDA-MCS的优化精度远高于本地搜索IDA-MCS。

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