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
- 如何把统计推断任务拆分为可独立执行的部分，并选择合适的任务粒度，使经以太网通信的处理器网络高效运行？
- 方法与思路
- 作者讨论按计算完成次序调度的数据流算法，结合离散有限推断、大样本分析、时间序列及层级模型等例子考察任务拆分。
- 材料陈述的贡献
- 摘要展示不同统计应用的任务粒度选择及其性能影响，并说明适当条件下并行计算的潜在加速；未报告统一效率保证。
- 阅读关注点
- 应核对各应用的独立任务划分、通信与调度成本、负载分配及性能测量，并辨明硬件和粒度变化如何影响加速效果。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，加速取决于未详述的条件；所述历史硬件实例不能直接推断当前平台性能或刊本变化。
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.