{"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/-cudnn-accelerating-deep-learning-frameworks","title":"μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching","arxiv_id":"1804.04806","date":"2018-04-13","proceeding":null,"authors":["Yosuke Oyama","Tal Ben-Nun","Torsten Hoefler","Satoshi Matsuoka"],"abstract":"NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used\nin deep learning. Specifically, cuDNN implements several equivalent convolution\nalgorithms, whose performance and memory footprint may vary considerably,\ndepending on the layer dimensions. When an algorithm is automatically selected\nby cuDNN, the decision is performed on a per-layer basis, and thus it often\nresorts to slower algorithms that fit the workspace size constraints. We\npresent {\\mu}-cuDNN, a transparent wrapper library for cuDNN, which divides\nlayers' mini-batch computation into several micro-batches. Based on Dynamic\nProgramming and Integer Linear Programming, {\\mu}-cuDNN enables faster\nalgorithms by decreasing the workspace requirements. At the same time,\n{\\mu}-cuDNN keeps the computational semantics unchanged, so that it decouples\nstatistical efficiency from the hardware efficiency safely. We demonstrate the\neffectiveness of {\\mu}-cuDNN over two frameworks, Caffe and TensorFlow,\nachieving speedups of 1.63x for AlexNet and 1.21x for ResNet-18 on P100-SXM2\nGPU. These results indicate that using micro-batches can seamlessly increase\nthe performance of deep learning, while maintaining the same memory footprint.","url_abs":"http://arxiv.org/abs/1804.04806v1","url_pdf":"http://arxiv.org/pdf/1804.04806v1.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":"-cudnn-accelerating-deep-learning-frameworks","repo_url":"https://github.com/spcl/ucudnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":null,"task_name":"GPU"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}