Request processing method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides a request processing method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of data processing. The method comprisesthe steps of obtaining n requests, wherein each request corresponds to a cothread on the thread, and n is an integer greater than 0 and is less than or equal to a preset threshold; determining one dominant coroutine in the n coroutines according to a preset competition rule, and taking the other n-1 coroutines as following coroutines; merging the n requests through the dominant coroutine to obtainmerged request data; and submitting the merge request data to the computing device. According to the embodiment of the invention, when the request processing method is used for processing a pluralityof requests input by a user, the coroutines are introduced to serve as the execution units, the obtained n requests correspond to the coroutines respectively, the dominant coroutines are determined according to the preset competition rule to merge the n requests, server resource consumption caused by cross-thread competition is reduced, and therefore the computing performance of the server can bebrought into full play.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular, to a request processing method, device, electronic equipment, and computer-readable storage medium. Background technique [0002] With the emergence of massive data, people tend to use machine learning techniques to mine value from the data. Machine learning can continuously improve the performance of the system itself by simulating or realizing human learning behavior, acquiring new knowledge or skills, and reorganizing the existing knowledge structure. fields of intelligence. [0003] Since the use of machine learning is inseparable from the necessary machine learning framework, the explosive growth of machine learning has also promoted the development of many open source tools, making it easier for developers to learn its technology. For example, in the deep learning field of machine learning research, TensorFlow Serving launched by Google is one of the t...

AI Technical Summary

Problems solved by technology

[0004] However, when multiple concurrent requests are processed by multi-threading technology in the above-mentioned existing machine learning framework, a batch of requests will occupy multiple threads at the same time, and there are cross-core scheduling and cross-thread competition among different threads, which consumes server resources. A large number of resources, resulting in the server's computing performance can not be fully utilized

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