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.

中文翻译：

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轨迹算法 IV：用于创建优化指向目录的网格计算系统的性能指标

本文讨论了网格计算系统的要求和性能指标，该系统用于实现轨迹算法以识别 61,662,376 颗恒星和 23,779 颗类星体的差分测光的最佳指向。初始操作测试表明需要一个软件系统来分析数据和一个高性能计算系统以可扩展的方式运行该软件。在串行计算环境中对软件性能的实际评估被用来提供一个基准，用于比较 HPC 解决方案的性能指标，并指出性能中的任何瓶颈。这些性能指标表明性能的明显分裂更多是由输入数据的差异而不是所用系统设计的差异决定的。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错误或幼稚的结论，尤其是在具有高数据 I/O 开销的系统中，例如网格计算。此外，这意味着减少或消除这种瓶颈的系统（例如内存处理）可能会导致性能的显着提高。