Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (1): 27-37.doi: 10.11871/jfdc.issn.2096-742X.2020.01.003

Previous Articles     Next Articles

Efficient Computing for Artificial Intelligence and Big Data

Li kenli1,2,*(),Yang Wangdong1,*(),Chen Cen1,2,Chen Jianguo1,Ding Yan1   

  1. 1.College of Information Science and Engineering, Hunan University, Changsha ,Hunan 410008, China
    2.National Super-computer Center in Changsha, Changsha ,Hunan 410008, China
  • Received:2019-10-28 Online:2020-02-20 Published:2020-03-28
  • Contact: Li kenli,Yang Wangdong E-mail:lkl@hnu.edu.cn;yangwangdong@163.com

Abstract:

[Objective] This paper mainly analyses the main challenges brought to computer system by the rapid increase of data scale of AI and big data application. In view of the development trend of computer system, some research directions of high-efficiency computing towards AI and big data are given. [Coverage] In this paper, the latest research results and challenges of big data and artificial intelligence computing on supercomputing and high performance computing platforms at home and abroad are extensively surveyed. [Methods] Big data not only provides an increasingly rich training data set for artificial intelligence, but also puts forward higher requirements for the computing power of computer systems. In recent years, China's supercomputer techniques are at the forefront of the world, which provides a powerful computing platform for large-scale applications of big data and artificial intelligence. [Results] At present, high-performance computing platforms represented by supercomputers mostly use heterogeneous parallel computing systems composed of CPUs and accelerators, where a large number of computing cores can provide powerful computing power for AI and big data applications. [Limitations]However, due to the complex architecture, there are major challenges in making full use of computing power and improving computing efficiency. The parallel computing efficiency is more difficult to improve, especially in the artificial intelligence and big data domains which are different from scientific computing. [Conclusions] Therefore, it is required to conduct research on high-performance computing from underlying resource management, task scheduling, basic algorithm design, and communication optimization to the upper level of model parallelization, so that the computational efficiency of artificial intelligence and big data applications on high-performance computing platforms can be improved.

Key words: artificial intelligence, big data, heterogeneous systems, high efficiency computing, supercomputing