论文标题
基因座算法IV：网格计算系统的性能指标，用于创建优化点的目录
The Locus Algorithm IV: Performance metrics of a grid computing system used to create catalogues of optimised pointings
论文作者
论文摘要
本文讨论了用于实施基因座算法的网格计算系统的要求和性能指标，以识别61,662,376星和23,779种类星体的差分光度法的最佳点。初始操作测试表明，需要软件系统来分析数据和高性能计算系统以可扩展的方式运行该软件。对软件在串行计算环境中的性能进行的实际评估用于提供基准，可以比较HPC解决方案的性能指标，并指示性能的任何瓶颈。这些性能指标表明，通过输入数据的差异，与所使用系统的设计差异相比，在性能中的差异更大。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天真的结论，尤其是在具有高数据I/O高架（例如网格计算）的系统中。此外，这意味着减少或消除这种瓶颈的系统（例如内存处理）可能会导致性能大幅提高。
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.
