{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:47:25Z","timestamp":1787017645876,"version":"build-2736575974"},"reference-count":70,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T00:00:00Z","timestamp":1731974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Innovation Promotion Association CAS and Huawei Noah\u2019s Ark Lab"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Archit. Code Optim."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>As deep neural networks (DNNs) become increasingly large and complicated, pruning techniques are proposed for lower memory footprint and more efficient inference. The most critical kernel to execute pruned sparse DNNs on GPUs is Sparse-dense Matrix Multiplication (SpMM). To maximize the performance of SpMM, despite the high-performance implementation generated from advanced tensor compilers, they often take a long time to iteratively search tuning configurations. Such a long time slows down the cycle of exploring better DNN architectures or pruning algorithms. In this article, we propose LO-SpMM to efficiently generate high-performance SpMM implementations for sparse DNN inference. Based on the analysis of nonzero elements\u2019 layout, the characterization of the GPU architecture, and a rank-based cost model, LO-SpMM can effectively reduce the search space and eliminate possibly low-performance candidates. Besides, rather than generating complete SpMM implementations for evaluation, LO-SpMM constructs simplified proxies to quickly estimate performance, thereby substantially reducing compilation and execution costs. Experimental results show that LO-SpMM can reduce the search time by 281\u00d7 at most, while the performance of generated SpMM implementations is comparable to or better than the state-of-the-art sparse tensor compiling solutions.<\/jats:p>\n                  <jats:p\/>","DOI":"10.1145\/3685277","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T07:09:28Z","timestamp":1722236968000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["LO-SpMM: Low-cost Search for High-performance SpMM Kernels on GPUs"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1455-8725","authenticated-orcid":false,"given":"Junqing","family":"Lin","sequence":"first","affiliation":[{"name":"Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5098-1503","authenticated-orcid":false,"given":"Jingwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3516-8765","authenticated-orcid":false,"given":"Xiaolong","family":"Shi","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5707-0606","authenticated-orcid":false,"given":"Honghe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1497-5525","authenticated-orcid":false,"given":"Xianzhi","family":"Yu","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7452-6226","authenticated-orcid":false,"given":"Xinzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6924-2468","authenticated-orcid":false,"given":"Jun","family":"Yao","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0794-7681","authenticated-orcid":false,"given":"Guangzhong","family":"Sun","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2022. Basic Linear Algebra on NVIDIA GPUs. https:\/\/docs.nvidia.com\/cuda\/cublas\/index.html. Accessed: February 1 2022."},{"key":"e_1_3_1_3_2","unstructured":"2022. A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication. https:\/\/docs.nvidia.com\/cuda\/cusparse\/index.html. Accessed: February 2 2022."},{"key":"e_1_3_1_4_2","unstructured":"2024. CUDA C++ Programming Guide. https:\/\/docs.nvidia.com\/cuda\/cuda-c-programming-guide\/. Accessed: January 1 2024"},{"key":"e_1_3_1_5_2","unstructured":"2024. The sdk for high-performance deep learning inference. https:\/\/docs.nvidia.com\/deeplearning\/tensorrt\/. Accessed: July 3 2024."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2014.69"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3314221.3314615"},{"key":"e_1_3_1_9_2","volume-title":"Proceedings of the 10th International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022","author":"Bai Yue","year":"2022","unstructured":"Yue Bai, Huan Wang, Zhiqiang Tao, Kunpeng Li, and Yun Fu. 2022. Dual lottery ticket hypothesis. In Proceedings of the 10th International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/1654059.1654078"},{"key":"e_1_3_1_11_2","first-page":"4322","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event\/Montreal, Canada, 19-27 August 2021","author":"Benmeziane Hadjer","year":"2021","unstructured":"Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Sma\u00efl Niar, Martin Wistuba, and Naigang Wang. 2021. Hardware-aware neural architecture search: Survey and taxonomy. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event\/Montreal, Canada, 19-27 August 2021. ijcai.org, 4322\u20134329."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273513"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/71.780863"},{"key":"e_1_3_1_14_2","volume-title":"Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)."},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.5555\/3571885.3571927"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/PACT.2017.24","volume-title":"Proceedings of the 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT)","author":"Cummins Chris","year":"2017","unstructured":"Chris Cummins, Pavlos Petoumenos, Zheng Wang, and Hugh Leather. 2017. End-to-end deep learning of optimization heuristics. In Proceedings of the 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT). IEEE, 219\u2013232."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/800195.805928"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2015.98"},{"issue":"4","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2818693","article-title":"Minime-gpu: Multicore benchmark synthesizer for gpus","volume":"12","author":"Deniz Etem","year":"2015","unstructured":"Etem Deniz and Alper Sen. 2015. Minime-gpu: Multicore benchmark synthesizer for gpus. ACM Transactions on Architecture and Code Optimization 12, 4 (2015), 1\u201325.","journal-title":"ACM Transactions on Architecture and Code Optimization"},{"issue":"8","key":"e_1_3_1_20_2","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1109\/TC.2014.2349522","article-title":"Minime: Pattern-aware multicore benchmark synthesizer","volume":"64","author":"Deniz Etem","year":"2014","unstructured":"Etem Deniz, Alper Sen, Brian Kahne, and Jim Holt. 2014. Minime: Pattern-aware multicore benchmark synthesizer. IEEE Transactions on Computers 64, 8 (2014), 2239\u20132252.","journal-title":"IEEE Transactions on Computers"},{"key":"e_1_3_1_21_2","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers)","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 4171\u20134186."},{"key":"e_1_3_1_22_2","first-page":"1","volume-title":"Proceedings of the 57th ACM\/IEEE Design Automation Conference, DAC 2020, San Francisco, CA, USA, July 20-24, 2020","author":"Dong Peiyan","year":"2020","unstructured":"Peiyan Dong, Siyue Wang, Wei Niu, Chengming Zhang, Sheng Lin, Zhengang Li, Yifan Gong, Bin Ren, Xue Lin, and Dingwen Tao. 2020. RTMobile: Beyond real-time mobile acceleration of RNNs for speech recognition. In Proceedings of the 57th ACM\/IEEE Design Automation Conference, DAC 2020, San Francisco, CA, USA, July 20-24, 2020. IEEE, 1\u20136."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2021.10.003"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-020-09548-1"},{"key":"e_1_3_1_25_2","first-page":"17","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020, Virtual Event\/Atlanta, Georgia, USA, November 9-19, 2020","author":"Gale Trevor","year":"2020","unstructured":"Trevor Gale, Matei Zaharia, Cliff Young, and Erich Elsen. 2020. Sparse GPU kernels for deep learning. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020, Virtual Event\/Atlanta, Georgia, USA, November 9-19, 2020. IEEE\/ACM, 17."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1137\/0713023"},{"issue":"4","key":"e_1_3_1_27_2","first-page":"1","article-title":"Simpoint 3.0: Faster and more flexible program phase analysis","volume":"7","author":"Hamerly Greg","year":"2005","unstructured":"Greg Hamerly, Erez Perelman, Jeremy Lau, and Brad Calder. 2005. Simpoint 3.0: Faster and more flexible program phase analysis. Journal of Instruction Level Parallelism 7, 4 (2005), 1\u201328.","journal-title":"Journal of Instruction Level Parallelism"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","unstructured":"Yang He and Lingao Xiao. 2024. Structured pruning for deep convolutional neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 46 5 (2024) 2900\u20132919. 10.1109\/TPAMI.2023.333461","DOI":"10.1109\/TPAMI.2023.333461"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3623278.3624770"},{"key":"e_1_3_1_31_2","unstructured":"Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden and Alexandra Peste. 2021. Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research 22 241 (2021) 1\u2013124."},{"key":"e_1_3_1_32_2","first-page":"300","volume-title":"Proceedings of the 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2019, Washington, DC, USA, February 16-20, 2019","author":"Hong Changwan","year":"2019","unstructured":"Changwan Hong, Aravind Sukumaran-Rajam, Israt Nisa, Kunal Singh, and P. Sadayappan. 2019. Adaptive sparse tiling for sparse matrix multiplication. In Proceedings of the 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2019, Washington, DC, USA, February 16-20, 2019. ACM, 300\u2013314."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827595287997"},{"key":"e_1_3_1_34_2","doi-asserted-by":"crossref","unstructured":"Kimberly Keeton and David A Patterson. 2000. Towards a simplified database workload for computer architecture evaluations. Workload Characterization for Computer System Design 542 (2000) 49\u201371.","DOI":"10.1007\/978-1-4615-4387-9_3"},{"key":"e_1_3_1_35_2","first-page":"120","volume-title":"Proceedings of the 2014 IEEE International Symposium on Workload Characterization (IISWC)","author":"Kim Keunsoo","year":"2014","unstructured":"Keunsoo Kim, Changmin Lee, Jung Ho Jung, and Won Woo Ro. 2014. Workload synthesis: Generating benchmark workloads from statistical execution profile. In Proceedings of the 2014 IEEE International Symposium on Workload Characterization (IISWC). IEEE, 120\u2013129."},{"key":"e_1_3_1_36_2","first-page":"943","volume-title":"Proceedings of the 2017 32nd IEEE\/ACM International Conference on Automated Software Engineering (ASE)","author":"Kjolstad Fredrik","year":"2017","unstructured":"Fredrik Kjolstad, Stephen Chou, David Lugato, Shoaib Kamil, and Saman Amarasinghe. 2017. Taco: A tool to generate tensor algebra kernels. In Proceedings of the 2017 32nd IEEE\/ACM International Conference on Automated Software Engineering (ASE). 943\u2013948."},{"key":"e_1_3_1_37_2","first-page":"87","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020, Virtual Event\/Atlanta, Georgia, USA, November 9-19, 2020","author":"Kurt S\u00fcreyya Emre","year":"2020","unstructured":"S\u00fcreyya Emre Kurt, Aravind Sukumaran-Rajam, Fabrice Rastello, and P. Sadayappan. 2020. Efficient tiled sparse matrix multiplication through matrix signatures. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020, Virtual Event\/Atlanta, Georgia, USA, November 9-19, 2020. IEEE\/ACM, 87."},{"key":"e_1_3_1_38_2","volume-title":"Proceedings of the 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021","author":"Li Chaojian","year":"2021","unstructured":"Chaojian Li, Zhongzhi Yu, Yonggan Fu, Yongan Zhang, Yang Zhao, Haoran You, Qixuan Yu, Yue Wang, Cong Hao, and Yingyan Lin. 2021. HW-NAS-bench: Hardware-aware neural architecture search benchmark. In Proceedings of the 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3605573.3605632"},{"key":"e_1_3_1_40_2","first-page":"673","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020","author":"Lin Mingbao","year":"2020","unstructured":"Mingbao Lin, Rongrong Ji, Yuxin Zhang, Baochang Zhang, Yongjian Wu, and Yonghong Tian. 2020. Channel pruning via automatic structure search. In Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020. Christian Bessiere (Ed.), ijcai.org, 673\u2013679."},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1137\/0713020"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441621"},{"key":"e_1_3_1_43_2","first-page":"881","volume-title":"Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020, Virtual Event, November 4-6, 2020","author":"Ma Lingxiao","year":"2020","unstructured":"Lingxiao Ma, Zhiqiang Xie, Zhi Yang, Jilong Xue, Youshan Miao, Wei Cui, Wenxiang Hu, Fan Yang, Lintao Zhang, and Lidong Zhou. 2020. Rammer: Enabling holistic deep learning compiler optimizations with rTasks. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020, Virtual Event, November 4-6, 2020. USENIX Association, 881\u2013897."},{"key":"e_1_3_1_44_2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1007\/978-3-030-01264-9_8","volume-title":"Proceedings of the 15th European Conference on Computer Vision - ECCV 2018 - Munich, Germany, September 8-14, 2018, Proceedings, Part XIV","volume":"11218","author":"Ma Ningning","year":"2018","unstructured":"Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun. 2018. ShuffleNet V2: Practical guidelines for efficient CNN architecture design. In Proceedings of the 15th European Conference on Computer Vision - ECCV 2018 - Munich, Germany, September 8-14, 2018, Proceedings, Part XIV(Lecture Notes in Computer Science, Vol. 11218). Springer, 122\u2013138."},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2016.57"},{"key":"e_1_3_1_46_2","unstructured":"Daniel M\u00fcllner. 2011. Modern hierarchical agglomerative clustering algorithms. arXiv preprint arXiv:1109.2378 (2011)."},{"key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/PACT.2017.44","volume-title":"Proceedings of the 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT)","author":"Panda Reena","year":"2017","unstructured":"Reena Panda and Lizy Kurian John. 2017. Proxy benchmarks for emerging big-data workloads. In Proceedings of the 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT). IEEE, 105\u2013116."},{"key":"e_1_3_1_48_2","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga Alban Desmaison Andreas K\u00f6pf Edward Z. Yang Zachary DeVito Martin Raison Alykhan Tejani Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai and Soumith Chintala. 2019. PyTorch: An imperative style high-performance deep learning library. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 NeurIPS 2019 December 8-14 2019 Vancouver BC Canada Hanna M. Wallach Hugo Larochelle Alina Beygelzimer Florence d\u2019Alch\u00e9-Buc Emily B. Fox and Roman Garnett (Eds.). 8024\u20138035."},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.02.069"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3080702"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3427093"},{"key":"e_1_3_1_52_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, Virtual","author":"Sanh Victor","year":"2020","unstructured":"Victor Sanh, Thomas Wolf, and Alexander M. Rush. 2020. Movement pruning: Adaptive sparsity by fine-tuning. In Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, Virtual. Hugo Larochelle, Marc\u2019Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.)."},{"key":"e_1_3_1_53_2","doi-asserted-by":"crossref","unstructured":"Sebastian Schlag Tobias Heuer Lars Gottesb\u00fcren Yaroslav Akhremtsev Christian Schulz and Peter Sanders. 2023. High-quality hypergraph partitioning. ACM Journal of Experimental Algorithmics 27 (2023) 1\u201339.","DOI":"10.1145\/3529090"},{"key":"e_1_3_1_54_2","first-page":"386","volume-title":"Proceedings of the Design, Automation and Test in Europe Conference and Exhibition (DATE), 2017","author":"Sen Alper","year":"2017","unstructured":"Alper Sen, Etem Deniz, and Brian Kahne. 2017. MINIME-validator: Validating hardware with synthetic parallel testcases. In Proceedings of the Design, Automation and Test in Europe Conference and Exhibition (DATE), 2017. IEEE, 386\u2013391."},{"key":"e_1_3_1_55_2","first-page":"254","volume-title":"Proceedings of the 2005 Conference of the Centre for Advanced Studies on Collaborative Research","author":"Shao Minglong","year":"2005","unstructured":"Minglong Shao, Anastassia Ailamaki, and Babak Falsafi. 2005. DBmbench: Fast and accurate database workload representation on modern microarchitecture. In Proceedings of the 2005 Conference of the Centre for Advanced Studies on Collaborative Research. 254\u2013267."},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/605432.605403"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2857721"},{"key":"e_1_3_1_58_2","first-page":"1","volume-title":"Proceedings of the IEEE International Symposium on Workload Characterization (IISWC\u201910)","author":"Ertvelde Luk Van","year":"2010","unstructured":"Luk Van Ertvelde and Lieven Eeckhout. 2010. Benchmark synthesis for architecture and compiler exploration. In Proceedings of the IEEE International Symposium on Workload Characterization (IISWC\u201910). IEEE, 1\u201311."},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.08.004"},{"key":"e_1_3_1_60_2","first-page":"23","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI, Vienna, Austria","author":"Wang Huan","year":"2022","unstructured":"Huan Wang, Can Qin, Yue Bai, Yulun Zhang, and Yun Fu. 2022. Recent advances on neural network pruning at initialization. In Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI, Vienna, Austria. 23\u201329."},{"key":"e_1_3_1_61_2","first-page":"31","volume-title":"Proceedings of the PACT\u201920: International Conference on Parallel Architectures and Compilation Techniques, Virtual Event, GA, USA, October 3-7, 2020","author":"Wang Ziheng","year":"2020","unstructured":"Ziheng Wang. 2020. SparseRT: Accelerating unstructured sparsity on GPUs for deep learning inference. In Proceedings of the PACT\u201920: International Conference on Parallel Architectures and Compilation Techniques, Virtual Event, GA, USA, October 3-7, 2020. Vivek Sarkar and Hyesoon Kim (Eds.), ACM, 31\u201342."},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3591302"},{"key":"e_1_3_1_63_2","first-page":"106","volume-title":"Proceedings of the 2021 International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)","author":"Willemsen Floris-Jan","year":"2021","unstructured":"Floris-Jan Willemsen, Rob van Nieuwpoort, and Ben van Werkhoven. 2021. Bayesian optimization for auto-tuning GPU kernels. In Proceedings of the 2021 International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). IEEE, 106\u2013117."},{"key":"e_1_3_1_64_2","first-page":"1","volume-title":"Proceedings of the 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021, Waltham, MA, USA, September 20-24, 2021","author":"Xin Jie","year":"2021","unstructured":"Jie Xin, Xianqi Ye, Long Zheng, Qinggang Wang, Yu Huang, Pengcheng Yao, Linchen Yu, Xiaofei Liao, and Hai Jin. 2021. Fast sparse deep neural network inference with flexible SpMM optimization space exploration. In Proceedings of the 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021, Waltham, MA, USA, September 20-24, 2021. IEEE, 1\u20137."},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96983-1_48"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/3582016.3582047"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1137\/080733243"},{"issue":"1","key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3566054","article-title":"Polyhedral specification and code generation of sparse tensor contraction with co-iteration","volume":"20","author":"Zhao Tuowen","year":"2022","unstructured":"Tuowen Zhao, Tobi Popoola, Mary Hall, Catherine Olschanowsky, and Michelle Strout. 2022. Polyhedral specification and code generation of sparse tensor contraction with co-iteration. ACM Transactions on Architecture and Code Optimization 20, 1 (2022), 1\u201326.","journal-title":"ACM Transactions on Architecture and Code Optimization"},{"key":"e_1_3_1_69_2","first-page":"863","volume-title":"Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020, Virtual Event, November 4-6, 2020","author":"Zheng Lianmin","year":"2020","unstructured":"Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, and Ion Stoica. 2020. Ansor: Generating high-performance tensor programs for deep learning. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020, Virtual Event, November 4-6, 2020. USENIX Association, 863\u2013879."},{"key":"e_1_3_1_70_2","first-page":"213","volume-title":"Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2022, Carlsbad, CA, USA, July 11-13, 2022","author":"Zheng Ningxin","year":"2022","unstructured":"Ningxin Zheng, Bin Lin, Quanlu Zhang, Lingxiao Ma, Yuqing Yang, Fan Yang, Yang Wang, Mao Yang, and Lidong Zhou. 2022. SparTA: Deep-learning model sparsity via tensor-with-sparsity-attribute. In Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2022, Carlsbad, CA, USA, July 11-13, 2022. USENIX Association, 213\u2013232."},{"key":"e_1_3_1_71_2","first-page":"233","volume-title":"Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2022, Carlsbad, CA, USA, July 11-13, 2022","author":"Zhu Hongyu","year":"2022","unstructured":"Hongyu Zhu, Ruofan Wu, Yijia Diao, Shanbin Ke, Haoyu Li, Chen Zhang, Jilong Xue, Lingxiao Ma, Yuqing Xia, Wei Cui, Fan Yang, Mao Yang, Lidong Zhou, Asaf Cidon, and Gennady Pekhimenko. 2022. ROLLER: Fast and efficient tensor compilation for deep learning. In Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2022, Carlsbad, CA, USA, July 11-13, 2022. USENIX Association, 233\u2013248."}],"container-title":["ACM Transactions on Architecture and Code Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3685277","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3685277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:06:18Z","timestamp":1750277178000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3685277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,19]]},"references-count":70,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12,31]]}},"alternative-id":["10.1145\/3685277"],"URL":"https:\/\/doi.org\/10.1145\/3685277","relation":{},"ISSN":["1544-3566","1544-3973"],"issn-type":[{"value":"1544-3566","type":"print"},{"value":"1544-3973","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,19]]},"assertion":[{"value":"2024-01-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}