Applications of Parallel Computation to Statistical Inference
并行计算在统计推断中的应用AI 解读 ↓
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并行计算在统计推断中的应用
- 研究问题
- 如何把大型统计推断任务分解成可独立执行的部分，使通过 Ethernet 通信的处理器网络获得有效的并行加速？
- 方法与思路
- 作者采用由任务完成顺序驱动的数据流算法，通过多类推断应用讨论任务划分，并比较不同计算粒度对网络性能的影响。
- 材料陈述的贡献
- 摘要展示离散有限推断、大样本分析、时间序列及层级模型等应用，指出任务粒度选择会带来不同的并行计算表现。
- 阅读关注点
- 应核对任务独立性、调度机制、通信开销和负载分配，以及各示例的计算粒度如何决定相对串行执行的实际加速。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，不能推断刊本变化；按处理器数量加速需要合适条件，摘要未量化条件，也不足以推断现代硬件表现。
原文摘要
Recent advances in parallel computation (e.g., Eddy and Schervish 1986; Gardner, Gerard, Mowers, Nemeth, and Schnabel 1986) have made it possible for a network of microcomputers to act together as a parallel processor using data-flow algorithms (see O'Leary and Stewart 1985). A data-flow algorithm is one in which the sequence of computations is not scheduled a priori, but rather is determined by the order in which computations are completed. Many statistical problems can be adapted to dataflow algorithms. One requirement is that the computation can be broken into independent parts, that is, parts that can be performed in any order. For example, calculating the average of a large number of data values requires adding them up, which can be done in any order. Suppose that an application can be broken into independent parts and one has p processors available to perform the computations. Then each of the p processors can work on one of the parts at a time. When one processor finishes, it can work on the next part of the computation. Under the right conditions, this can allow a speedup of as much as a factor of p in the computation. The problems that must be solved are approximating the right conditions and breaking the application up accordingly. This article discusses the problem of subdividing a large task in such a way that it can run efficiently on a network of processors communicating over an Ethernet. The discussion centers on several examples. The first example concerns a mode of inference called discrete-finite inference (see Eddy and Schervish 1986). The second example is large-sample data analysis (see Kim and Schervish, in press). The third example concerns multiprocess time series models (see Schervish and Tsay 1988). The fourth example (Lehoczky and Schervish 1987) is a hierarchical model for the responses to a particular item in a national victimization survey. Each of the applications has a different degree of coarseness or “granularity” to the subdivision of the work. Some problems offer several choices of different granularities. For example, in a multiple integration, each of the integrations (or all of them) could be performed in parallel. These choices and differences lead to different levels of performance of the network of processors.