Optimising memory management for Belief Propagation in Junction Trees using GPGPUs
Optimising memory management for Belief Propagation in Junction Trees using GPGPUs
复制标题
使用 GPGPU 优化连接树中置信传播的内存管理
DOI：
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发表时间：
2014
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通讯作者：
N. Bombieri
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作者：
Filippo Bistaffa;A. Farinelli;N. Bombieri
连接树 (JT) 中的置信传播 (BP) 是计算贝叶斯网络 (BN) 后验的最流行方法之一。这种方法具有显着的计算要求，可以通过使用高度并行架构（即通用图形处理单元）来并行化 BP 的消息更新阶段来解决。在本文中，我们提出了一种使用 GPGPU 并行 BP 的新方法，其重点是优化 BN 表的内存布局，以便在提高加速、减少主机和 GPGPU 之间的数据传输以及可扩展性方面实现更好的性能。我们与标准数据集上最先进的方法进行的实证比较证实了加速率（高达 +594%）和可扩展性（因为我们的方法可以在潜在表超过 GPGPU 全局内存的网络上运行）的显着改进。
Belief Propagation (BP) in Junction Trees (JT) is one of the most popular approaches to compute posteriors in Bayesian Networks (BN). Such approach has significant computational requirements that can be addressed by using highly parallel architectures (i.e., General Purpose Graphic Processing Units) to parallelise the message update phases of BP. In this paper, we propose a novel approach to parallelise BP with GPGPUs, which focuses on optimising the memory layout of the BN tables so to achieve better performance in terms of increased speedup, reduced data transfers between the host and the GPGPU, and scalability. Our empirical comparison with the state of the art approach on standard datasets confirms significant improvements in speedups (up to +594%), and scalability (as our method can operate on networks whose potential tables exceed the global memory of the GPGPU).