面向人工智能和大数据的高效能计算

【目的】本文主要分析人工智能和大数据应用随着迅速增大的数据规模,给计算机系统带来的主要挑战,并针对计算机系统的发展趋势给出了一些面向人工智能和大数据亟待解决的高效能计算的若干研究方向。【文献范围】本文广泛查阅国内外在超级计算和高性能计算平台进行大数据和人工智能计算的最新研究成果及解决的挑战性问题。【方法】大数据既为人工智能提供了日益丰富的训练数据集合,但也给计算机系统的算力提出了更高的要求。近年来我国超级计算机处于世界的前列,为大数据和人工智能的大规模应用提供了强有力的计算平台支撑。【结果】而目前以超级计算机为代表的高性能计算平台大多采用CPU+加速器构成的异构并行计算系统,其数量众多的计算核心能够为人工智能和大数据应用提供强大的计算能力。【局限性】由于体系结构复杂,在充分发挥计算能力和提高计算效率方面存在较大挑战。尤其针对有别于科学计算的人工智能和大数据领域,其并行计算效率的提升更为困难。【结论】因此需要从底层的资源管理、任务调度、以及基础算法设计、通信优化,到上层的模型并行化和并行编程等方面展开高效能计算的研究,全面提升人工智能和大数据应用在高性能计算平台上的计算能效。

Efficient Computing for Artificial Intelligence and Big Data

[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.