Special Issue on New Parallel Distributed Technology for Big Data and AI
• 大类 : 工程技术 - 2区
• 小类 : 计算机：信息系统 - 1区
The improvement of computation power brings opportunities to big data and Artificial Intelligence (AI), however, new architectures, such as heterogeneous CPU-GPU, FPGA, etc., also bring great challenges to large-scale data and AI applications. Parallel Computing (PC), Machine Learning (ML), AI, and Big Data (BD) have grown substantially in popularity in recent years. Much research has been done in both academia and industry, with applications in many areas. For example, deep learning has achieved great success. AI/ML have been used to successfully play games such as Chess, Go, Atari, and Jeopardy. Many companies have been using AI and ML in areas including health care, natural resource management, and advertisement.
Most of the PC/ML/AI/BD technologies and applications require intensive use of high-performance computers and accelerators for efficient processing. Parallel computing, distributed computing, cloud computing, and high-performance computing (HPC) are key components of these systems. Clusters of computers and accelerators (e.g., GPUs) are routinely used to train and run models, both in research and industry. On the other hand, HPC, ML, AI, and BD have also led to key applications for parallel computing, distributed computing, and HPC. Consequently, these issues have driven much of research in this area.
The objective of this special issue is to bring together the parallel and distributed computing and PC/ML/AI/BD communities to present and discuss methodologies, solutions, and applications to performance issues, to present how PC/ML/AI/BD can be used to solve performance problems.
Topics of interest include, but are not limited to: