Computing power resource dynamic adjustment-based reasoning acceleration method and device for intelligent computing center cloud platform

By dynamically adjusting data reading parameters and computing resources in the intelligent computing center, processing data in batches and merging intermediate results, the problem of low computing efficiency caused by video memory overflow is solved, and efficient computing resource utilization and accurate inference results are achieved.

CN120706547AActive Publication Date: 2025-09-26DATACANVAS LTD
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Patent Information

Application Number
CN202510764476.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-09
Publication Date
2025-09-26
Estimated Expiration
2045-06-09

AI Technical Summary

Technical Problem

Existing intelligent computing centers are prone to memory overflow (OOM) when processing large amounts of data, resulting in low computing efficiency. Especially in long-sequence data inference and prediction tasks, computing power operations are forced to be interrupted.

Method used

By obtaining data reading parameters for multiple batches, including data sequence length and batch size parameters, computing resources are dynamically adjusted, data is read and inferred in batches to avoid loading all data at once. Half-precision floating-point numbers (FP16) and video memory optimization strategies are used to merge intermediate results to improve computing efficiency.

Benefits of technology

It effectively avoids the problem of video memory overflow, improves computing efficiency, increases GPU utilization and overall inference throughput, and maintains the accuracy of the results.

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Abstract

The invention provides an intelligent computing center cloud platform reasoning acceleration method and device based on computing power resource dynamic adjustment, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure. The method comprises the following steps: S1, obtaining data reading parameters corresponding to each batch in a plurality of batches, the data reading parameters comprise a data sequence length parameter and / or a batch size parameter, and the batches are the number of times of reading the data file; s2, sequentially reading data files of a first computing power operation task based on the data reading parameters to obtain data corresponding to each batch; s3, reasoning the data corresponding to each batch in sequence to obtain a plurality of intermediate results; and S4, combining the plurality of intermediate results to obtain a reasoning result. The calculation efficiency of the intelligent calculation center is greatly improved.
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