轨迹算法IV：用于创建优化点目录的网格计算系统的性能指标
The Locus Algorithm IV: Performance metrics of a grid computing system used to create catalogues of optimised pointings
摘要：
本文讨论了网格计算系统的要求和性能指标，用于实现轨迹算法，以确定最佳点的61，662，376恒星和23，779类星体的差分测光。最初的业务测试表明，需要一个软件系统来分析数据，并需要一个高性能计算系统来以可扩展的方式运行该软件。对串行计算环境中软件性能的实际评估被用来提供一个基准，可以与HPC解决方案的性能指标进行比较，并指出性能方面的任何瓶颈。这些性能指标表明，输入数据的差异比所用系统设计的差异更能说明性能的明显差异。这表明需要对系统性能进行实验分析，并表明算法复杂性分析可能会导致不正确或幼稚的结论，特别是在具有高数据I/O开销的系统中，如网格计算。此外，这意味着减少或消除这种瓶颈（如内存处理）的系统可能会导致性能的大幅提高。
英文摘要：
This paper discusses the requirements for and performance metrics of the the Grid Computing system used to implement the Locus Algorithm to identify optimum pointings for differential photometry of 61,662,376 stars and 23,779 quasars. Initial operational tests indicated a need for a software system to analyse the data and a High Performance Computing system to run that software in a scalable manner. Practical assessments of the performance of the software in a serial computing environment were used to provide a benchmark against which the performance metrics of the HPC solution could be compared, as well as to indicate any bottlenecks in performance. These performance metrics indicated a distinct split in the performance dictated more by differences in the input data than by differences in the design of the systems used. This indicates a need for experimental analysis of system performance, and suggests that algorithmic complexity analyses may lead to incorrect or naive conclusions, especially in systems with high data I/O overhead such as grid computing. Further, it implies that systems which reduce or eliminate this bottleneck such as in-memory processing could lead to a substantial increase in performance.
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