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Modern analytics and recommendation systems are increasingly based on graph data that capture the relations between entities being analyzed.
The PageRank Citation Ranking: Bringing Order to the Web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
Earlier work this paper cites.
UbiCrawler: a scalable fully distributed Web crawler
Paolo Boldi, Bruno Codenotti, Massimo Santini, and Sebastiano Vigna. 2004a · 2004
Earlier work this paper cites.
UbiCrawler: A Scalable Fully Distributed Web Crawler
Paolo Boldi, Bruno Codenotti, Massimo Santini, and Sebastiano Vigna. 2004b · 2004
Earlier work this paper cites.
The WebGraph Framework I: Compression Techniques. In Proceedings of the Thirteenth International World Wide Web Conference (WWW 2004) . ACM Press, Manhattan, USA, 595–601
Paolo Boldi and Sebastiano Vigna. 2004 · 2004
Earlier work this paper cites.
Accelerating Large Graph Algorithms on the GPU Using CUDA. In Proceedings of the 14th International Conference on High Performance Computing (Goa, India) (HiPC’07) . Springer-Verlag, Berlin, Heidelberg, 197–208
Pawan Harish and P. J. Narayanan. 2007 · 2007
Earlier work this paper cites.
Parallel Graph Component Labelling with GPUs and CUDA
K. A. Hawick, A. Leist, and D. P. Playne. 2010 · 2010
Earlier work this paper cites.
Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory. In SC ’10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis . 1–11
R. Pearce, M. Gokhale, and N. M. Amato. 2010 · 2010
Earlier work this paper cites.
Layered Label Propagation: A MultiResolution Coordinate-Free Ordering for Compressing Social Networks. In Proceedings of the 20th international conference on World Wide Web , Sadagopan Srinivasan, Krithi Ramamritham, Arun Kumar, M. P. Ravindra, Elisa Bertino, and Ravi Kumar (Eds.). ACM Press, 587–596
Paolo Boldi, Marco Rosa, Massimo Santini, and Sebastiano Vigna. 2011 · 2011
Earlier work this paper cites.
The University of Florida Sparse Matrix Collection
Timothy A. Davis and Yifan Hu. 2011 · 2011
Earlier work this paper cites.
A Yoke of Oxen and a Thousand Chickens for Heavy Lifting Graph Processing. In Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques (Minneapolis, Minnesota, USA) (PACT ’12) . Association for Computing Machinery, New York, NY, USA, 345–354
Abdullah Gharaibeh, Lauro Beltrão Costa, Elizeu Santos-Neto, and Matei Ripeanu. 2012 · 2012
Earlier work this paper cites.
Defining and Evaluating Network Communities based on Ground-truth
Jaewon Yang and Jure Leskovec. 2012 · 2012
Earlier work this paper cites.
A Lightweight Infrastructure for Graph Analytics. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles (Farminton, Pennsylvania) (SOSP ’13) . Association for Computing Machinery, New York, NY, USA, 456–471
Donald Nguyen, Andrew Lenharth, and Keshav Pingali. 2013 · 2013
Earlier work this paper cites.
Ligra: A Lightweight Graph Processing Framework for Shared Memory
Julian Shun and Guy E. Blelloch. 2013 · 2013
Earlier work this paper cites.
From “Think like a Vertex” to “Think like a Graph”
Yuanyuan Tian, Andrey Balmin, Severin Andreas Corsten, Shirish Tatikonda, and John McPherson. 2013 · 2013
Earlier work this paper cites.
GasCL: A vertex-centric graph model for GPUs. In 2014 IEEE High Performance Extreme Computing Conference (HPEC) . 1–6
S. Che. 2014 · 2014
Earlier work this paper cites.
Kokkos: Enabling manycore performance portability through polymorphic memory access patterns
H. Carter Edwards, Christian R. Trott, and Daniel Sunderland. 2014 · 2014
Earlier work this paper cites.
CuSha: Vertex-Centric Graph Processing on GPUs. In Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing (Vancouver, BC, Canada) (HPDC ’14) . Association for Computing Machinery, New York, NY, USA, 239–252
Farzad Khorasani, Keval Vora, Rajiv Gupta, and Laxmi N. Bhuyan. 2014 · 2014
Earlier work this paper cites.
GoFFish: A Sub-graph Centric Framework for Large-Scale Graph Analytics. In Euro-Par 2014 Parallel Processing , Fernando Silva, Inês Dutra, and Vítor Santos Costa (Eds.). Springer International Publishing, Cham, 451–462
Yogesh Simmhan, Alok Kumbhare, Charith Wickramaarachchi, Soonil Nagarkar, Santosh Ravi, Cauligi Raghavendra, and Viktor Prasanna. 2014 · 2014
Earlier work this paper cites.
Medusa: Simplified Graph Processing on GPUs
Jianlong Zhong and Bingsheng He. 2014 · 2014
Earlier work this paper cites.
Page Placement Strategies for GPUs within Heterogeneous Memory Systems
Neha Agarwal, David Nellans, Mark Stephenson, Mike O’Connor, and Stephen W. Keckler. 2015 · 2015
Earlier work this paper cites.
Scott Beamer, Krste Asanovic, and David A. Patterson. 2015 · 2015
Earlier work this paper cites.
Improving high-performance GPU graph traversal with compression
Krzysztof Kaczmarski, Piotr Przymus, and Paweł Rzążewski. 2015 · 2015
Earlier work this paper cites.
GPUswap: Enabling Oversubscription of GPU Memory through Transparent Swapping
Jens Kehne, Jonathan Metter, and Frank Bellosa. 2015 · 2015
Cited alongside, same era.
Scalable SIMD-Efficient Graph Processing on GPUs. In 2015 International Conference on Parallel Architecture and Compilation (PACT) . 39–50
F. Khorasani, R. Gupta, and L. N. Bhuyan. 2015 · 2015
Cited alongside, same era.
High-Performance and Scalable GPU Graph Traversal
Duane Merrill, Michael Garland, and Andrew Grimshaw. 2015 · 2015
Cited alongside, same era.
GraphBIG: Understanding Graph Computing in the Context of Industrial Solutions. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Austin, Texas) (SC ’15) . Association for Computing Machinery, New York, NY, USA, Article 69, 12 pages
Lifeng Nai, Yinglong Xia, Ilie G. Tanase, Hyesoon Kim, and Ching-Yung Lin. 2015 · 2015
Cited alongside, same era.
The Network Data Repository with Interactive Graph Analytics and Visualization. In AAAI
Accelerating Dynamic Graph Analytics on GPUs
Mo Sha, Yuchen Li, Bingsheng He, and Kian-Lee Tan. 2017 · 2017
Later among the works it cites.
MOLIERE: Automatic Biomedical Hypothesis Generation System. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD ’17) . Association for Computing Machinery, New York, NY, USA, 1633–1642
Justin Sybrandt, Michael Shtutman, and Ilya Safro. 2017 · 2017
Later among the works it cites.
GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators
Zhen Xu, Xuhao Chen, Jie Shen, Yang Zhang, Cheng Chen, and Canqun Yang. 2017 · 2017
Later among the works it cites.
Making caches work for graph analytics. In 2017 IEEE International Conference on Big Data (Big Data) . 293–302
Y. Zhang, V. Kiriansky, C. Mendis, S. Amarasinghe, and M. Zaharia. 2017 · 2017
Later among the works it cites.
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Ryan A. Rossi and Nesreen K. Ahmed. 2015 · 2015
Cited alongside, same era.
GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Austin, Texas) (SC ’15) . Association for Computing Machinery, New York, NY, USA, Article 28, 12 pages
Dipanjan Sengupta, Shuaiwen Leon Song, Kapil Agarwal, and Karsten Schwan. 2015 · 2015
Cited alongside, same era.
GraphMat: High Performance Graph Analytics Made Productive
Narayanan Sundaram, Nadathur Satish, Md Mostofa Ali Patwary, Subramanya R. Dulloor, Michael J. Anderson, Satya Gautam Vadlamudi, Dipankar Das, and Pradeep Dubey. 2015 · 2015
Cited alongside, same era.
NVIDIA Tesla P100 Architecture Whitepaper
2016 · 2016
Cited alongside, same era.
Graphicionado: A high-performance and energy-efficient accelerator for graph analytics. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) . 1–13
T. J. Ham, L. Wu, N. Sundaram, N. Satish, and M. Martonosi. 2016 · 2016
Cited alongside, same era.
A Compiler for Throughput Optimization of Graph Algorithms on GPUs
Sreepathi Pai and Keshav Pingali. 2016 · 2016
Cited alongside, same era.
Big data analytics on Apache Spark
Salman Salloum, Ruslan Dautov, Xiaojun Chen, Patrick Xiaogang Peng, and Joshua Zhexue Huang. 2016 · 2016
Cited alongside, same era.
Gunrock: A High-Performance Graph Processing Library on the GPU. In Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (Barcelona, Spain) (PPoPP ’16) . Association for Computing Machinery, New York, NY, USA, Article 11, 12 pages
Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, and John D. Owens. 2016 · 2016
Cited alongside, same era.
How Well do CPU, GPU and Hybrid Graph Processing Frameworks Perform?. In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) . 458–466
T. K. Aasawat, T. Reza, and M. Ripeanu. 2018 · 2018
Later among the works it cites.
Collaborative (CPU+ GPU) algorithms for triangle counting and truss decomposition. In 2018 IEEE High Performance extreme Computing Conference (HPEC’18) . Boston, USA
Vikram S Mailthody, Ketan Date, Zaid Qureshi, Carl Pearson, Rakesh Nagi, Jinjun Xiong, and Wen-mei Hwu. 2018 · 2018
Later among the works it cites.
Tigr: Transforming Irregular Graphs for GPU-Friendly Graph Processing
Amir Hossein Nodehi Sabet, Junqiao Qiu, and Zhijia Zhao. 2018 · 2018
Later among the works it cites.
XBFS: EXploring Runtime Optimizations for Breadth-First Search on GPUs. In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (Phoenix, AZ, USA) (HPDC ’19) . Association for Computing Machinery, New York, NY, USA, 121–131
Anil Gaihre, Zhenlin Wu, Fan Yao, and Hang Liu. 2019 · 2019
Later among the works it cites.
Interplay between Hardware Prefetcher and Page Eviction Policy in CPU-GPU Unified Virtual Memory. In Proceedings of the 46th International Symposium on Computer Architecture (Phoenix, Arizona) (ISCA ’19) . Association for Computing Machinery, New York, NY, USA, 224–235
Debashis Ganguly, Ziyu Zhang, Jun Yang, and Rami Melhem. 2019 · 2019
Later among the works it cites.
A Framework for Memory Oversubscription Management in Graphics Processing Units. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (Providence, RI, USA) (ASPLOS ’19) . Association for Computing Machinery, New York, NY, USA, 49–63
Chen Li, Rachata Ausavarungnirun, Christopher J. Rossbach, Youtao Zhang, Onur Mutlu, Yang Guo, and Jun Yang. 2019 · 2019
Later among the works it cites.
Update on Triangle Counting on GPU. In 2019 IEEE High Performance extreme Computing Conference (HPEC’19) . Boston, USA
Carl Pearson, Mohammad Almasri, Omer Anjum, Vikram S Mailthody, Zaid Qureshi, Rakesh Nagi, Jinjun Xiong, and Wen-mei Hwu. 2019 · 2019
Later among the works it cites.
GPU-Based Graph Traversal on Compressed Graphs. In Proceedings of the 2019 International Conference on Management of Data (Amsterdam, Netherlands) (SIGMOD ’19) . Association for Computing Machinery, New York, NY, USA, 775–792
Mo Sha, Yuchen Li, and Kian-Lee Tan. 2019 · 2019
Later among the works it cites.
DiGraph: An Efficient Path-Based Iterative Directed Graph Processing System on Multiple GPUs. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (Providence, RI, USA) (ASPLOS ’19) . Association for Computing Machinery, New York, NY, USA, 601–614
Yu Zhang, Xiaofei Liao, Hai Jin, Bingsheng He, Haikun Liu, and Lin Gu. 2019 · 2019
Later among the works it cites.
CUDA C++ Best Practices Guide
2020 · 2020
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Intel® VTune™ Profiler
2020 · 2020
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NVIDIA A100 GPU Architecture Whitepaper
2020 · 2020
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NVIDIA DGX A100 Datasheet
2020 · 2020
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PCIe 3.0 Specification
2020 · 2020
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Adaptive Page Migration for Irregular Data-intensive Applications under GPU Memory Oversubscription. In Proceedings of the Thirty-forth International Conference on Parallel and Distributed Processing (IPDPS)
Debashis Ganguly, Z Zhang, J Yang, and Rami Melhem. 2020 · 2020
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Traversing Large Graphs on GPUs with Unified Memory
Prasun Gera, Hyojong Kim, Piyush Sao, Hyesoon Kim, and David Bader. 2020 · 2020
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Batch-Aware Unified Memory Management in GPUs for Irregular Workloads. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (Lausanne, Switzerland) (ASPLOS ’20) . Association for Computing Machinery, New York, NY, USA, 1357–1370
Hyojong Kim, Jaewoong Sim, Prasun Gera, Ramyad Hadidi, and Hyesoon Kim. 2020 · 2020
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Subway: Minimizing Data Transfer during out-of-GPU-Memory Graph Processing. In Proceedings of the Fifteenth European Conference on Computer Systems (Heraklion, Greece) (EuroSys ’20) . Association for Computing Machinery, New York, NY, USA, Article 12, 16 pages
Amir Hossein Nodehi Sabet, Zhijia Zhao, and Rajiv Gupta. 2020 · 2020
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