Статьи

Effective output from data centers are determined by many complementary factors. Often, attention is paid to only a few, at first glance, the most significant of them. For example, this is the efficiency of the scheduler, the efficiency of resource utilization by user tasks. At the same time, a more general view of the problem is often missed: the level at which the interconnection of work processes in the HPC center is determined, the organization of effective work as a whole. missions at this stage can negate any subtle optimizations at a low level. This paper provides a scheme for describing workflows in the supercomputer center and analyzes the experience of large HPC facilities in identifying the bottlenecks in this chain. A software implementation option that gives the possibility of optimizing the organization of work at all stages is also proposed in the form of a support system for the functioning of the HPC site.

HPC systems are complex in architecture and contain millions of components. To ensure reliable operation and efficient output, functioning of most subsystems should be supervised. This is done on the basis of collected data from various logging and monitoring systems. This means that different data sources are used, and accordingly, data analysis can face multiple issues processing this data. Some of the data subsets can be incorrect due to the malfunctioning of used sensors, monitoring system data aggregation errors, etc. This is why it is crucial to preprocess such monitoring data before analyzing it, taking into the consideration the analysis goals. The aim of this paper is, being based on the MSU HPC Center monitoring data, to propose an approach to data preprocessing of HPC monitoring systems, giving some real life examples of issues that may be faced, and recommendations for further analysis of similar datasets.