This paper researches on the power consumption of AIGC applications based on LLM with different parameter scales across different hardware platforms. Artificial Intelligence Generated Content (AIGC) represents a leading-edge application of AI technology, primarily driven by large language models (LLMs) and their associated technologies. The deployment of LLM typically relies on critical facilities with three layers, i.e., the hardware, model, and application layers. This empirical study aims to identify key factors in power consumption when a large model is serving in the inference stage, which will hint the insights for improving the energy efficiency of computational infrastructures. In the context of the "dual carbon" goals, i.e., carbon peaking and carbon neutrality, this study aims to find an effective way to reduce the energy cost of AIGC applications, thereby supporting sustainable AI development in industry.

The DaaS (Data as a Service) is an Internet-perception big data system based on AI, which is built by "Think Tank 2861 Project Team". This is an Internet-area, data-based, and neural feedback system for the Internet information in China. It takes Internet activities as the input, and processes through AI algorithms and machine learning framework to generate the output, based on which building the real-time macro economics and society big data for about 9.8 million grids in China and its intelligent applications. DaaS covers all 2,861 administrative districts and counties in the country and is refined to geography grid of one square kilometer granularity. The real-time objective information generated by distributed AI algorithms, that are constantly trained and calibrated, is of great value in scientific research and commercial applications.

This article is composed in the background of the study of scientific field of coauthors phenomenon factual basis. By the study of massive amounts of relational data, it provides us with major significances theoretically and practically on retrieving and obtaining professionally academic information and getting knowing of academic development trend of miscellaneous fields. In process of studying this type of project, the problem of cluttering for coauthors that are in the data is involved. However, it is hard to meet the need of implementing the analysis of massive amounts of data cluttering by the existing cluttering software and algorithms, for this reason, finding an approach to deal with this kind of question is toughly important. To solve this question, this article presents an optimized Bisecting K-Means (BKM) clustering algorithm based on Hadoop and states the fashion of how to optimize the algorithm and the key point of implementing in details after analyzing the status quo related to this study. Estimating the complexity of the algorithm by experiments indicates the current problems and the direction for the future study.

With the development of cloud computing technologies, big data processing is becoming more and more important. How to mine and analyze massive data is facing a very big challenge. In this paper, we proposed an efficient massive data mining and analysis framework Data Cloud on large clusters. The most important part of Data Cloud is the Rabbit. It is a kind of massive data mining and analysis processing plan framework on the large clusters like the Pig and Hive. We make a detail analysis about the Rabbit plan.