GPU cluster deep learning task parallelization method, device and electronic equipment

Abstract

The embodiment of the invention provides a GPU cluster deep learning task parallelization method, a GPU cluster deep learning task parallelization device and electronic equipment, and relates to the technical field of internet. The method comprises the following steps: firstly, analyzing the similarity between a to-be-processed deep learning task and each computing node of a GPU cluster; determining a target computing node of the to-be-processed deep learning task in the GPU cluster, reducing the possibility of computing node resource contention, and improving the utilization rate and the execution efficiency of deep learning task system resources; according to the number of GPUs required by the to-be-processed deep learning task, obtaining a to-be-processed deep learning task; dividing the to-be-processed deep learning task into a plurality of target sub-tasks, analyzing the interference level and the communication cost of the target subtask; determining the target GPU of the target sub-task in the target computing node, and avoiding unbalanced resource allocation on the GPU in the computing node. The high parallelization of the deep learning task is realized. The resource utilization rate of the GPU cluster is improved. Meanwhile, the execution efficiency of the deep learning task is improved.

Description

technical field [0001] The present application relates to the field of Internet technology, in particular to a parallelization method, device and electronic equipment for GPU cluster deep learning tasks. Background technique [0002] With the deepening of deep learning research, deep learning technology has achieved fruitful results in computer vision, speech recognition, text processing and other fields, bringing great convenience to people's lives. However, the complex structure of the neural network model and the huge amount of data put forward higher requirements for computing power. GPU (Graphic Processing Unit, Image Processor) cluster integrates multiple GPU computing resources, provides powerful and efficient parallel computing capabilities for computing-intensive deep learning tasks, and effectively solves the computing needs of multiple deep learning tasks. [0003] However, when a deep learning task runs on a resource-sharing GPU cloud platform, its execution eff...

AI Technical Summary

Problems solved by technology

[0005] Although this method realizes the parallelization of deep learning tasks to a certain extent, this method mainly considers the usage of resources and does not consider the physical characteristics of resources and the characteristics of the task itself. It cannot achieve efficient parallelization of deep learning tasks, which will reduce the Execution Efficiency of Deep Learning Workloads

At the same time, this method does not support the fine-grained multi-task allocation of the GPU, and cannot make full use of the GPU resources on the node, which will affect the efficient execution of deep learning tasks, reduce the GPU utilization of the node, and thus affect the resource utilization of the GPU cluster.

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