Parallel and Distributed Computing and Its Application in Artificial Intelligence

Dear Colleagues,

Artificial intelligence (AI) is increasingly being integrated into scientific discovery, creating a new paradigm known as AI for Science, which accelerates innovation at an unprecedented pace. New AI algorithms and models offer unparalleled capabilities to assist scientists in analyzing vast and complex datasets, identifying patterns that inform their decisions and experimental designs. Most AI technologies and applications require intensive use of high-performance computers and accelerators for efficient processing. Consequently, parallel and distributed computing systems have become essential components of computing infrastructure, combining heterogeneous computational patterns, algorithms, and hardware to achieve goals that extend beyond traditional supercomputing systems, particularly with AI for Science. In scientific research, clusters of computers and accelerators (e.g., GPUs, FPGAs) are routinely employed to train and execute various neural network models. Due to the time-consuming nature of iterative training processes and the enormity of training datasets, AI technologies have also emerged as a "killer application" for parallel computing, distributed computing, and high-performance computing (HPC). These challenges have driven much of the research in distributed and parallel computing. The objective of this Special Issue is to bring together the parallel and distributed computing communities with AI for Science experts to present their applications and solutions to performance issues, as well as to demonstrate how AI can be used to address performance challenges.

Dr. Agnieszka Konys
Prof. Dr. Xiaoying Zheng
Guest Editors

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