High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers.
High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers.
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DOI：
10.1145/1810085.1810101
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Nolan GP
中科院分区：
文献类型：
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作者：
Linderman MD;Athalye V;Meng TH;Asadi NB;Bruggner R;Nolan GP
异常的细胞内信号传导在许多疾病中起重要作用。信号转导网络的因果结构可以建模为贝叶斯网络（BN），并从实验数据中计算学习。然而，学习贝叶斯网络（BN）的结构是一个NP难问题，即使使用快速算法，对于大型临床重要网络（20-50个节点）也太耗时。在本文中，我们提出了一种新的图形处理单元（GPU）加速实现的Monte Carlo马尔可夫链为基础的算法学习BN的速度高达7.5倍，比目前的通用处理器（GPP）为基础的实现。基于GPU的实现只是大型应用程序中的几种实现之一，每种实现都针对不同的输入或机器配置进行了优化。我们描述了我们用来构建可扩展应用程序的方法，该应用程序由这些变体组装而成，可以针对广泛的异构系统，例如，GPU，多核GPU。具体来说，我们展示了如何使用合并编程模型来有效地集成，测试和智能地选择不同的潜在实现。
Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20–50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations.