GPU internal energy consumption optimization method based on task balance scheduling

Abstract

The invention belongs to the technical field of task balance scheduling of equipment states and task characteristics. The invention discloses a GPU (Graphics Processing Unit) internal energy consumption optimization method based on task equalization scheduling, which comprises the following steps of: proposing an equalization influence factor and a CB-SM task scheduling algorithm of SM using frequency information by utilizing an equalization influence factor CB and an SM using frequency information PHB handle of a program to be executed; enabling the CB-HRV task scheduling algorithm to assigna group of tasks to the corresponding SM according to the task's equalization impact factor according to the task execution time; enabling a policy scheduler to distribute tasks to each SM; using SM using frequency information to increase using frequency of equipment resources; and achieving the task scheduling method with optimized energy consumption through integrated application of program to be executed; and resources information. Dynamic balance scheduling in a plurality of SM is realized, and the energy consumption optimization target of the GPU is achieved.

Description

technical field [0001] The invention belongs to the technical field of task balance scheduling of device states and task characteristics, and in particular relates to a method for optimizing the internal energy consumption of GPUs based on task balance scheduling. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: Graphics Processing Unit (English: Graphics Processing Unit, abbreviated: GPU), also known as display core, visual processor, display chip, is a kind of special equipment used in personal computers, workstations, game consoles, etc. And some mobile devices (such as tablet computers, smart phones, etc.) on the image computing work of the microprocessor. The purpose is to convert and drive the display information required by the computer system, provide line scan signals to the display, and control the correct display of the display. . As an important part of the computer host, the graphics card underta...

AI Technical Summary

Problems solved by technology

[0026] (1) The loss caused by the migration of tasks on each SM is not considered. For example, the scheduling algorithm based on device release (device history-based algorithm) considers the priority of using the released SM, but does not consider the migration of tasks on the SM; it is very likely Due to the repeated migration of multiple tasks between SMs, a large amount of energy consumption is generated, which weakens the energy consumption performance of the GPU

[0027] (2) There is no comprehensive consideration of equipment resources and tasks performed; although the SM utilization rate is considered in the algorithm strategy based on SM historical performance (Performance History Based), it does not combine the migration characteristics of tasks and cannot Balanced use of SM results in inefficient task execution

[0029] (1) The current task scheduling algorithm does not have a high utilization rate of the computing unit (stream processor SM) of the GPU, and cannot comprehensively consider the utilization efficiency of SM and the impact of task characteristics on energy consumption

[0030] (2) Migration phenomenon in task scheduling is a common problem. Migration of tasks in the stream processor will cause energy loss. The current scheduling strategy cannot effectively alleviate the phenomenon of task migration.

[0031] (3) At present, there is no effective energy consumption optimization task scheduling algorithm that can solve the above two problems, and algorithm design is needed

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