Supercomputer resources management using machine learning methods under constraints

Artificial intelligence and machine learning technologies are among the most promising in the field of computer science. They make it possible to obtain solutions to problems that until recently were the exclusive prerogative of humans. However, when solving practical problems, it is necessary to implement machine learning models taking into account the restrictions on available resources. Such resources can be both computational and temporary (i.e. the problem must be solved in a certain time and using certain hardware, most often it is about various mobile platforms), and informational, when it comes to small, censored, incomplete or noisy data. The paper examines machine learning methods used to solve practical problems in application areas, such as comparing the shape of three-dimensional objects and intellectualizing resource dispatching, within the framework of the concept of “Supercomputer for AI and AI for a Supercomputer”. In the field of solving problems with limited data volume, a method is proposed that allows training a multilayer neural network using an ultra-small training sample to solve the problem of quantitatively assessing the proximity of the shape of arbitrary three-dimensional objects. In the field of applying machine learning models with limited resources, a method has been developed that ensures asynchronous operation of the machine learning model and the executable process, which allows for the effective use of machine learning methods under constraints.