Graphics Processing Units and High-Dimensional Optimization.
Graphics Processing Units and High-Dimensional Optimization.
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DOI：
10.1214/10-sts336
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发表时间：
2010-08-01
期刊：
影响因子：
--
通讯作者：
Suchard MA
中科院分区：
文献类型：
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作者：
Zhou H;Lange K;Suchard MA
本文讨论了图形处理单元（GPU）在高维优化问题中的潜力。一个具有数百个运算核心的GPU卡可以插入个人计算机中，并显着加速许多统计算法。为了充分利用这些设备，优化算法应该减少到多个并行任务，每个任务访问有限的数据量。这些标准有利于EM和MM算法，分离参数和数据。在较小程度上，阻滞松弛和协调下降和上升也是合格的。我们展示了GPU在非负矩阵分解、PET图像重建和多维缩放中的实用性。100倍的加速可以很容易地实现。在接下来的十年里，GPU将从根本上改变计算统计的格局。现在是更多统计学家加入的时候了。
This paper discusses the potential of graphics processing units (GPUs) in high-dimensional optimization problems. A single GPU card with hundreds of arithmetic cores can be inserted in a personal computer and dramatically accelerates many statistical algorithms. To exploit these devices fully, optimization algorithms should reduce to multiple parallel tasks, each accessing a limited amount of data. These criteria favor EM and MM algorithms that separate parameters and data. To a lesser extent block relaxation and coordinate descent and ascent also qualify. We demonstrate the utility of GPUs in nonnegative matrix factorization, PET image reconstruction, and multidimensional scaling. Speedups of 100 fold can easily be attained. Over the next decade, GPUs will fundamentally alter the landscape of computational statistics. It is time for more statisticians to get on-board.