{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/opara-exploiting-operator-parallelism-for","title":"Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUs","arxiv_id":"2312.10351","date":"2023-12-16","proceeding":null,"authors":["Aodong Chen","Fei Xu","Li Han","Yuan Dong","Li Chen","Zhi Zhou","Fangming Liu"],"abstract":"GPUs have become the \\emph{defacto} hardware devices for accelerating Deep Neural Network (DNN) inference workloads. However, the conventional \\emph{sequential execution mode of DNN operators} in mainstream deep learning frameworks cannot fully utilize GPU resources, even with the operator fusion enabled, due to the increasing complexity of model structures and a greater diversity of operators. Moreover, the \\emph{inadequate operator launch order} in parallelized execution scenarios can lead to GPU resource wastage and unexpected performance interference among operators. In this paper, we propose \\emph{Opara}, a resource- and interference-aware DNN \\underline{Op}erator \\underline{para}llel scheduling framework to accelerate DNN inference on GPUs. Specifically, \\emph{Opara} first employs \\texttt{CUDA Streams} and \\texttt{CUDA Graph} to \\emph{parallelize} the execution of multiple operators automatically. To further expedite DNN inference, \\emph{Opara} leverages the resource demands of operators to judiciously adjust the operator launch order on GPUs, overlapping the execution of compute-intensive and memory-intensive operators. We implement and open source a prototype of \\emph{Opara} based on PyTorch in a \\emph{non-intrusive} manner. Extensive prototype experiments with representative DNN and Transformer-based models demonstrate that \\emph{Opara} outperforms the default sequential \\texttt{CUDA Graph} in PyTorch and the state-of-the-art operator parallelism systems by up to $1.68\\times$ and $1.29\\times$, respectively, yet with acceptable runtime overhead.","url_abs":"https://arxiv.org/abs/2312.10351v2","url_pdf":"https://arxiv.org/pdf/2312.10351v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"opara-exploiting-operator-parallelism-for","repo_url":"https://github.com/icloud-ecnu/opara","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"scheduling","task_name":"Scheduling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}