• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 83 matches

• 2021
• 
  Zhuohan Li, Siyuan Zhuang, Shiyuan Guo, Danyang Zhuo, Hao Zhang, Dawn Song, Ion Stoica:
  TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models. CoRR abs/2102.07988 (2021)
• 2020
• 
  Quan M. Nguyen, Daniel Sanchez:
  Pipette: Improving Core Utilization on Irregular Applications through Intra-Core Pipeline Parallelism. MICRO 2020: 596-608
• 
  Isabelly Rocha, Nathaniel Morris, Lydia Y. Chen, Pascal Felber, Robert Birke, Valerio Schiavoni:
  PipeTune: Pipeline Parallelism of Hyper and System Parameters Tuning for Deep Learning Clusters. Middleware 2020: 89-104
• 
  Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi:
  HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism. USENIX Annual Technical Conference 2020: 307-321
• 
  Chiheon Kim, Heungsub Lee, Myungryong Jeong, Woonhyuk Baek, Boogeon Yoon, Ildoo Kim, Sungbin Lim, Sungwoong Kim:
  torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models. CoRR abs/2004.09910 (2020)
• 
  Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi:
  HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism. CoRR abs/2005.14038 (2020)
• 
  Isabelly Rocha, Nathaniel Morris, Lydia Y. Chen, Pascal Felber, Robert Birke, Valerio Schiavoni:
  PipeTune: Pipeline Parallelism of Hyper and System Parameters Tuning for Deep Learning Clusters. CoRR abs/2010.00501 (2020)
• 
  Letian Zhao, Rui Xu, Tianqi Wang, Teng Tian, Xiaotian Wang, Wei Wu, Chio-in Ieong, Xi Jin:
  BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training. CoRR abs/2012.12544 (2020)
• 2019
• 
  Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen:
  GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. NeurIPS 2019: 103-112
• 
  Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia:
  PipeDream: generalized pipeline parallelism for DNN training. SOSP 2019: 1-15
• 
  Lei Guan, Wotao Yin, Dongsheng Li, Xicheng Lu:
  XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training. CoRR abs/1911.04610 (2019)
• 2018
• 
  Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Zhifeng Chen:
  GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. CoRR abs/1811.06965 (2018)
• 2017
• 
  Junchang Wang, Yangfeng Tian, Tao Li, Xiong Fu:
  A Flexible Communication Mechanism for Pipeline Parallelism. ISPA/IUCC 2017: 778-785
• 
  Yang Wang, Kenneth B. Kent:
  A Region-Based Approach to Pipeline Parallelism in Java Programs on Multicores. PDP 2017: 124-131
• 2016
• 
  Hadi Mardani Kamali, Shaahin Hessabi:
  A Fault Tolerant Parallelism Approach for Implementing High-Throughput Pipelined Advanced Encryption Standard. J. Circuits Syst. Comput. 25(9): 1650113:1-1650113:14 (2016)
• 
  Gwangsun Kim, Jiyun Jeong, John Kim, Mark Stephenson:
  Automatically Exploiting Implicit Pipeline Parallelism from Multiple Dependent Kernels for GPUs. PACT 2016: 341-352
• 
  Jongsok Choi, Ruolong Lian, Stephen Dean Brown, Jason Helge Anderson:
  A unified software approach to specify pipeline and spatial parallelism in FPGA hardware. ASAP 2016: 75-82
• 
  Jinsu Park, Woongki Baek:
  HAP: A Heterogeneity-Conscious Runtime System for Adaptive Pipeline Parallelism. Euro-Par 2016: 518-530
• 
  Peter Koek, Stefan J. Geuns, Joost P. H. M. Hausmans, Henk Corporaal, Marco Jan Gerrit Bekooij:
  CSDFa: A Model for Exploiting the Trade-Off between Data and Pipeline Parallelism. SCOPES 2016: 30-39
• 2015
• 
  Chen Chen, Kai Lu, Xiaoping Wang, Xu Zhou, Zhendong Wu:
  A Load-Balanced Deterministic Runtime for Pipeline Parallelism. IEICE Trans. Inf. Syst. 98-D(2): 433-436 (2015)
• 
  Shinichi Yamagiwa, Guyue Wang, Koichi Wada:
  Development of an Algorithm for Extracting Parallelism and Pipeline Structure from Stream-based Processing flow with Spanning Tree. Int. J. Netw. Comput. 5(1): 159-179 (2015)
• 
  Yu Zhang, Zhaopeng Li, Hui-Fang Cao:
  System-Enforced Deterministic Streaming for Efficient Pipeline Parallelism. J. Comput. Sci. Technol. 30(1): 57-73 (2015)
• 
  I-Ting Angelina Lee, Charles E. Leiserson, Tao B. Schardl, Zhunping Zhang, Jim Sukha:
  On-the-Fly Pipeline Parallelism. ACM Trans. Parallel Comput. 2(3): 17:1-17:42 (2015)
• 
  Nam-Luc Tran, Thomas Peel, Sabri Skhiri:
  Distributed frank-wolfe under pipelined stale synchronous parallelism. BigData 2015: 184-192
• 2014
• 
  Joost P. H. M. Hausmans, Stefan J. Geuns, Maarten Wiggers, Marco Jan Gerrit Bekooij:
  Unified dataflow model for the analysis of data and pipeline parallelism, and buffer sizing. MEMOCODE 2014: 12-21
• 
  Nader Khammassi, Jean-Christophe Le Lann:
  A high-level programming model to ease pipeline parallelism expression on shared memory multicore architectures. SpringSim (HPS) 2014: 9
• 2013
• 
  Thomas Preud'homme:
  Communication inter-cœurs optimisée pour le parallélisme de flux. (Optimized inter-core communication for pipeline parallelism). Pierre and Marie Curie University, Paris, France, 2013
• 
  Shuai Mu, Dongdong Li, Yubei Chen, Yangdong Deng, Zhihua Wang:
  Exploiting the Task-Pipelined Parallelism of Stream Programs on Many-Core GPUs. IEICE Trans. Inf. Syst. 96-D(10): 2194-2207 (2013)
• 
  Chih-Sheng Lin, Chao-Sheng Lin, Yu-Shin Lin, Pao-Ann Hsiung, Chihhsiong Shih:
  Multi-objective exploitation of pipeline parallelism using clustering, replication and duplication in embedded multi-core systems. J. Syst. Archit. 59(10-C): 1083-1094 (2013)
• 
  Daniel Cordes, Michael Engel, Olaf Neugebauer, Peter Marwedel:
  Automatic Extraction of pipeline parallelism for embedded heterogeneous multi-core platforms. CASES 2013: 4:1-4:10

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