计算机工程与科学2026，Vol.48Issue(6)：983-996,14.DOI:10.3969/j.issn.1007-130X.2026.06.003
多参数融合的深度学习算子及模型性能预测方法
A multi-parameter fusion-based performance prediction method for deep learning operators and models
摘要
Abstract
Efficient resource allocation is a critical factor for enhancing the resource utilization of deep learning models in cloud computing data centers.Static resource allocation methods,which allocate re-sources by analyzing the computational demands of models,have emerged as one of the effective approa-ches to improve resource utilization.However,they still suffer from issues of insufficient efficiency and flexibility.To address this,we propose a multi-parameter fusion-based performance prediction method for deep learning operators and models.Firstly,an automated operator performance testing method is proposed based on operator parameters.Subsequently,by integrating data dependencies among opera-tors and hardware performance parameters,the method enables inference performance prediction for convolutional neural networks(CNN)and recurrent neural networks(RNN).Experimental results demonstrate that,on a single platform(either a single CPU or GPU),the proposed method achieves an average prediction error of 6.2%for CNN and RNN inference performance,representing a 2.3 percen-tage point reduction compared to SLAPP.On heterogeneous platforms(CPU+GPU),the maximum error does not exceed 10%.The proposed method enhances the accuracy of inference performance pre-diction,providing valuable insights for resource allocation in cloud computing data centers.关键词
深度学习/资源分配/模型推理/参数提取/性能预测Key words
deep learning/resource allocation/model inference/parameter extraction/performance prediction分类
信息技术与安全科学引用本文复制引用
盛明威,蒋林,李远成,尚绍法,朱筠..多参数融合的深度学习算子及模型性能预测方法[J].计算机工程与科学,2026,48(6):983-996,14.基金项目
新一代人工智能国家科技重大专项(2022ZD0119005) （2022ZD0119005）
陕西省自然科学基础研究计划(2024JC-YBQN-0288) （2024JC-YBQN-0288）
陕西省自然科学基金(2024JC-YBMS-539) （2024JC-YBMS-539）
