title: ÐÐµÑÐ¾Ð´Ñ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ð½Ð¸Ð¹ Ð² Ð³ÐµÑÐµÑÐ¾Ð³ÐµÐ½Ð½Ð¾Ð¹ ÑÑÐµÐ´Ðµ ÑÑÐ¿ÐµÑÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½ÑÑ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Job allocation methods in a heterogeneous supercomputing environment
Creators: name: Ð§Ð¶ÑÐ½ Ð¡Ð¸ÑÐ°Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¼Ð°ÑÑÐ¾Ð²Ð¾Ðµ Ð¾Ð±ÑÐ»ÑÐ¶Ð¸Ð²Ð°Ð½Ð¸Ðµ
Ð¼Ð½Ð¾Ð³Ð¾Ð·Ð°Ð´Ð°ÑÐ½Ð¾ÑÑÑ
Ð¿Ð¾Ð²ÑÐ¾ÑÐ½Ð¾Ðµ Ð±ÑÐ¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ Ð¿ÑÐ¸Ð¾ÑÐ¸ÑÐµÑÐ°
Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²ÑÐ¸Ðº
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑÐ¿ÐµÑÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÑ
Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÑÑÑÑ
Ð¾ÑÐµÑÐµÐ´Ð¸ Ð·Ð°Ð´Ð°Ð½Ð¸Ð¹
mass maintenance
multitasking
oversubscription
priority probability
scheduler
machine learning
supercomputers
computing resources
job queues

description: Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑÂ  ÑÐ»ÑÑÑÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ð½Ð¸Ð¹ Ð² Ð³ÐµÑÐµÑÐ¾Ð³ÐµÐ½Ð½ÑÑ ÑÑÐ¿ÐµÑÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½ÑÑ ÑÑÐµÐ´Ð°Ñ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÑÑÑÐ¾Ð². ÐÐ»Ñ Ð´Ð¾ÑÑÐ¸Ð¶ÐµÐ½Ð¸Ñ ÑÐµÐ»Ð¸ Ð² ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸ÑÑÑ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÑÑÑÐ¾Ð², Ð²ÐºÐ»ÑÑÐ°ÑÂ  Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÑÐ¾Ð²Ð¾Ð³Ð¾ Ð¾Ð±ÑÐ»ÑÐ¶Ð¸Ð²Ð°Ð½Ð¸Ñ Ñ Ð¿Ð¾Ð²ÑÐ¾ÑÐ½Ð¾Ð¼ Ð±ÑÐ¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¸ Ð²ÑÑÐµÑÐ½ÑÑÑÐ¸Ð¼ Ð¿ÑÐ¸Ð¾ÑÐ¸ÑÐµÑÐ¾Ð¼, Ð° ÑÐ°ÐºÐ¶Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÑÑÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ½ÑÐ¼Ð¸ Ð¿ÑÐ¸Ð¾ÑÐ¸ÑÐµÑÐ°Ð¼Ð¸. Ð Ð¿Ð¾ÑÐ»ÐµÐ´Ð½ÐµÐ¼ ÑÐ»ÑÑÐ°Ðµ, ÐºÐ°Ð¶Ð´Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐµ Ð¿ÑÐ¸ÑÐ²Ð°Ð¸Ð²Ð°ÐµÑÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ðµ Ð¿ÑÐ¸Ð¾ÑÐ¸ÑÐµÑÐ° Ð¸ Ð·Ð°Ð´Ð°ÑÐ¸ Ñ Ð±Ð¾Ð»ÐµÐµ Ð²ÑÑÐ¾ÐºÐ¸Ð¼ Ð¿ÑÐ¸Ð¾ÑÐ¸ÑÐµÑÐ¾Ð¼ Ð¸Ð¼ÐµÑÑ Ð±Ð¾Ð»ÐµÐµ Ð²ÑÑÐ¾ÐºÑÑ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ Ð±ÑÑÑ Ð²ÑÐ±ÑÐ°Ð½Ð½ÑÐ¼Ð¸ Ð´Ð»Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð² ÑÐµÐºÑÑÐ¸Ð¹ Ð¼Ð¾Ð¼ÐµÐ½Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸. ÐÐ»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ Ð¿ÑÐ¸Ð¾ÑÐ¸ÑÐµÑÐ¾Ð² Ð·Ð°Ð´Ð°Ð½Ð¸Ð¹, Ð½Ð°ÑÐ¾Ð´ÑÑÐ¸ÑÑÑ Ð² Ð¾ÑÐµÑÐµÐ´Ð¸ ÑÑÐ¿ÐµÑÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ° Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾ÑÐµÐ½Ð¾Ðº ÑÑÐ½ÐºÑÐ¸Ð¸ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð°Ñ Ð½Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÑÑÑÑÑ Ð¼ÐµÑÐ¾Ð´ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÐµ Ð´Ð»Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¾ÑÐ¸ÑÐµÑÐ¾Ð². Ð­ÑÐ¾ Ð¼ÐµÑÐ¾Ð´ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÑÂ  Ð´Ð°Ð½Ð½ÑÑ, ÑÐ°Ð½ÐµÐµ ÑÑÐ¿ÐµÑÐ½Ð¾ ÑÐµÑÐµÐ½Ð½ÑÑ Ð·Ð°Ð´Ð°Ñ. ÐÐ»Ð°Ð³Ð¾Ð´Ð°ÑÑ ÑÐ¾Ð²Ð¼ÐµÑÑÐ½Ð¾Ð¼Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð², ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑ, ÑÑÐ¾ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÐ¹ Ð½Ð°Ð¼Ð¸ Ð¼ÐµÑÐ¾Ð´ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ð½Ð¸Ð¹ Ð¼Ð¾Ð¶ÐµÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ ÑÐ»ÑÑÑÐ¸ÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÑÑÑÐ¾Ð² Ð¸ Ð¿Ð¾Ð²ÑÑÐ¸ÑÑ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ ÑÑÐ¿ÐµÑÐ½Ð¾Ð³Ð¾ Ð·Ð°Ð²ÐµÑÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ð½Ð¸Ð¹.
description: The aim of the work is to improve job allocation methods in heterogeneous supercomputing environments to increase the efficiency of computational resources utilization. To achieve the goal, the paper analyzes various methods for scheduling computational resources, including the application of mass service methods with rebooking and preemptive priority, and also analyzes models with probabilistic priorities. In the latter case, each task is assigned a priority value and tasks with higher priority have a higher probability of being selected for execution at the current time. To improve the accuracy of calculating the priorities of the tasks in the supercomputer queue, a model for estimating the distribution function based on the use of machine learning techniques is proposed. The paper analyzes the machine learning method used to calculate the probability of priorities. It is a learning method based on experimental data from previously successfully solved problems. Through the combined application of these models and methods, the results of the study show that our proposed task allocation method can effectively improve the use of computational resources and increase the probability of successful task completion.