A team of researchers from various Chinese institutions has developed a method for implementing coded quantum chemical calculations using improved machine learning models. The team demonstrated that these enhanced machine learning-assisted calculations can improve load-balancing and cluster utilization, benefiting fault tolerance. The procedure is designed for automated quantum chemical calculations for both ground and excited states. The researchers also addressed the issue of straggler nodes in distributed systems, applying coding theory to reduce data exchange between nodes, thereby reducing communication time and load, and mitigating latency issues.
Introduction to Coded Quantum Chemical Calculations with Improved Machine Learning Models
A team of researchers from various institutions including Beijing University of Chemical Technology, Computer Network Information Center Chinese Academy of Sciences, Institute of Computing Technology Chinese Academy of Sciences, School of Computer Science and Technology University of Chinese Academy of Sciences, College of Chemistry and Materials Engineering Wenzhou University, and Institute of Chemistry Chinese Academy of Sciences have developed a procedure for implementing coded quantum chemical calculations with improved machine learning (ML) models. The team includes Kai Yuan, Shuai Zhou, Ning Li, Tianyan Li, Bowen Ding, Danhuai Guo, and Yingjin Ma.
The researchers have shown that the improved ML-assisted coded calculations can enhance load-balancing and cluster utilization, primarily profiting in fault tolerance. This procedure aims at automated quantum chemical calculations for both ground and excited states.
Quantum Chemical Calculations and Their Limitations
Quantum chemical calculations aim to provide reliable descriptions of various properties for different molecular systems. However, the complexity of traditional quantum mechanics methods limits their usage when dealing with large molecular systems. There’s a growing interest in using quantum chemical calculations for studying biological macromolecular systems. The quantum mechanics methods, while usually limited to relatively small systems, can be extended using fragment-based techniques or linear scaling strategies when combined with efficient load-balancing schemes.
Role of High-Performance Computing in Scientific Calculations
As exascale supercomputing advances, high-performance computing (HPC) is assuming an increasingly crucial role in scientific calculations. In quantum chemistry calculations, many algorithms can theoretically be parallelized effectively. Effective load-balancing in scientific computing on HPC systems becomes crucial to ensure efficient resource utilization. It involves distributing subtasks across computing units to optimize response times and prevent overloading some nodes while leaving others idle.
Predicting Job Computational Costs
Before load scheduling or distributing, the computational costs should be roughly predicted. There are two ways used to predict job computational costs. One assumes that similar tasks have similar costs, using time-series analysis and heuristic load balancing. The other relies on computer system architecture, using ML with component performance data for cost prediction.
Machine Learning-Assisted Parallelization
In the ML-assisted parallelization, the static load balancing can be pre-scheduled based on the predictions of computational times for subsystems that need to be calculated. The precision of predictions affects the parallel efficiency. Although the dynamical load balancing can be employed as the remedy for improving the efficiency, a more reliable static load balancing scheme remains the major factor concerning the parallel efficiency.
Dealing with Straggler Nodes in Distributed Systems
In distributed systems, some nodes may experience failures, referred to as straggler nodes. The presence of straggler nodes can lead to unpredictable delays, significantly impacting the overall efficiency of the distributed system. Researchers have ingeniously applied coding theory to the field of distributed computing, leading to the concept of coded distributed computing (CDC). CDC leverages the flexibility of coding to design redundant computations that reduce data exchange between nodes. This type of approach not only reduces communication time and load but also mitigates the latency issues caused by straggler nodes.
The article titled “Coded Quantum Chemical Calculations with Improved Machine-Learning Models” was published on January 16, 2024. The authors of this article are Kai Yuan, Shuai Zhou, Ning Li, T. Y. Li, Boyin Ding, Danhuai Guo, and Yingjin Ma. The article was sourced from arXiv, a repository of electronic preprints approved for publication after moderation, hosted by Cornell University.
