title: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð±Ð°Ð·Ðµ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ¾Ð² ( LSTM) Ð´Ð»Ñ Ð½Ð°ÑÑÑÐ¾Ð¹ÐºÐ¸ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¿ÑÐ¸ÐºÐ»Ð°Ð´Ð½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼ Ð´Ð»Ñ Ð¸ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð² ÑÑÐµÐ´Ðµ Ð³ÐµÑÐµÑÐ¾Ð³ÐµÐ½Ð½ÑÑ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÐµÐ¹
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: Using Transformer-Based Machine Learning Methods (LSTM) to Customize Application Program Parameters for Execution in a Heterogeneous Computing Environment
Creators: name: ÐÐ¸ Ð§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: Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð³ÐµÑÐµÑÐ¾Ð³ÐµÐ½Ð½ÑÐ¹ ÑÑÐ¿ÐµÑÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»Ñ.
planning
heterogeneous supercomputer
machine learning
transformer.

description: Ð Ð°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¸ÐºÐ»Ð°Ð´Ð½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼ Ð¼ÐµÐ¶Ð´Ñ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½ÑÐ¼Ð¸ Ð±Ð»Ð¾ÐºÐ°Ð¼Ð¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½Ð¾Ð¹ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ (CPU, GPU, FPGA Ð¸ Ñ. Ð´.) Ð³ÐµÑÐµÑÐ¾Ð³ÐµÐ½Ð½ÑÑ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑ ÑÐ°ÑÑÐµÑÑ Ñ Ð½Ð°Ð¸Ð±Ð¾Ð»ÑÑÐµÐ¹ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑÑ. ÐÐ° ÑÐµÐ°Ð»ÑÐ½ÑÑ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ Ð³ÐµÑÐµÑÐ¾Ð³ÐµÐ½Ð½ÑÑ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»ÑÐ½ÑÑ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹ Ð² Ð½Ð°Ð¸Ð±Ð¾Ð»ÑÑÐµÐ¹ Ð¼ÐµÑÐµ Ð²Ð»Ð¸ÑÑÑ ÐºÐ¾Ð½ÑÐ¸Ð³ÑÑÐ°ÑÐ¸Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ, Ð¾ÑÑÐ°Ð¶Ð°ÑÑÐ°Ñ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸Â  Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐµÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¸ÐºÐ»Ð°Ð´Ð½ÑÑ Ð·Ð°Ð´Ð°Ñ. Ð ÑÑÐ¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ (Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»Ñ) Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹ Ð² Ð³ÐµÑÐµÑÐ¾Ð³ÐµÐ½Ð½ÑÑ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÑÐµÐ´Ð°Ñ. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾ÑÐ½Ð¾ÑÐ¸ÑÑÑ Ðº ÐºÐ»Ð°ÑÑÑ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ¾Ð² Ð½Ð° Ð±Ð°Ð·Ðµ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, ÑÐµÐ°Ð»Ð¸Ð·ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¿ÑÐ¸ÐºÐ»Ð°Ð´Ð½ÑÑ Ð·Ð°Ð´Ð°Ð½Ð¸Ð¹ Ñ ÑÐµÐ»ÑÑ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð¸Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ.
description: The distribution of application programs between hardware blocks of different architectures (CPU, GPU, FPGA, etc.) of heterogeneous computing platforms allows you to perform calculations with the highest performance. The real performance of heterogeneous parallel computing is most affected by the architecture configuration, which reflects the peculiarities of algorithms for solving applied problems. This paper considers the use of machine learning algorithms (transformer) for solving problems of planning computation processes in heterogeneous computing environments. The proposed model belongs to the class of transformers based on artificial neural networks that implement the attention mechanism for estimating the parameters of applied tasks in order to improve the accuracy of predicting their execution time.