The project described in this paper explores the informative sonification of data received in real time from a supercomputer. These data capture the current activities in all the nodes of the computer, therefore, their sonification functions as a form of continuous monitoring of the nodes' behavior and, by extension, of the system as a whole. Because such monitoring is theoretically unending, the resulting sonification must be musically capable of conveying information through sound in a way that remains both intelligible and engaging over long durations. Rather than imposing a predefined musical style onto the data, we sought to identify one which the data themselves could plausibly support. From a small set of candidates, we selected EDM because it is a family of genres whose structural and temporal characteristics align well with continuous, data-driven processes and long-term listening. Through this style-based approach, this research builds on the long tradition of computer data sonification while uniquely combining three elements rarely addressed together: monitoring (rather than debugging) as the primary goal, real-time (rather than post-mortem) data interpretation, and generation of virtually infinite and stylistically coherent (rather than incongruous) music.

翻译：本文所述项目探索了将超级计算机实时接收的数据进行可听化展示的方法。这些数据捕捉了计算机所有节点的当前活动，因此其可听化功能可作为节点行为乃至整个系统行为的连续监测形式。由于此类监测在理论上永无止境，由此产生的可听化结果必须能够通过声音传递信息，并在长时间内保持清晰易懂且引人入胜。我们并未将预定义的音乐风格强加于数据，而是试图寻找一种数据本身能够合理支持的音乐风格。经过对少数候选风格的筛选，我们选择了电子舞曲（EDM），因为这类音乐流派的结构与时间特性与连续的数据驱动过程及长期聆听需求高度契合。通过这种基于风格的方法，本研究继承了计算机数据可听化的悠久传统，同时独特地融合了三个很少被同时讨论的要素：将监测（而非调试）作为首要目标，进行实时（而非事后）数据解读，以及生成近乎无限且风格一致（而非杂乱）的音乐。