面向 SEAGrid 科学网关的 Gaussian 化学代码运行时间估计
Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway
AI总结：
本文针对 SEAGrid 科学网关中的 Gaussian 计算化学任务，分析历史运行数据并比较多种回归方法，以支持运行时间预测和资源调度。
摘要：
准确估计计算代码的运行时间对科学计算具有多项显著优势。它是实现最优资源分配、缩短周转时间以及提高科学网关利用率所需的信息。此外，它还允许用户更好地规划和安排其研究，简化工作流，并提高网络基础设施的整体生产率。然而，预测运行时间颇具挑战性。科学代码的输入可能复杂且具有高维特征。这些输入与运行时间之间的关系可能高度非线性，而且在最一般的情况下完全任意，因而不可预测（即仅仅是从输入到运行时间的随机映射）。不过，大多数代码并非如此任意，并且此前已有大量关于预测应用程序和工作负载运行时间的研究。然而，此类预测通常是针对特定应用的。本文聚焦于 Gaussian 计算化学代码。我们通过多项不同研究，对来自 SEAGrid 科学网关的运行数据集进行特征刻画。我们还探索了多种潜在的回归方法，并提出了有前景的未来方向。
英文摘要：
Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocation, improving turnaround times and utilization of science gateways. Furthermore, it allows users to better plan and schedule their research, streamlining workflows and improving the overall productivity of cyberinfrastructure. Predicting run time is challenging, however. The inputs to scientific codes can be complex and high dimensional. Their relationship to the run time may be highly non-linear, and, in the most general case is completely arbitrary and thus unpredictable (i.e., simply a random mapping from inputs to run time). Most codes are not so arbitrary, however, and there has been significant prior research on predicting the run time of applications and workloads. Such predictions are generally application-specific, however. In this paper, we focus on the Gaussian computational chemistry code. We characterize a data set of runs from the SEAGrid science gateway with a number of different studies. We also explore a number of different potential regression methods and present promising future directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
