GraphReduce

Author:

Sengupta Dipanjan1,Song Shuaiwen Leon2,Agarwal Kapil1,Schwan Karsten1

Affiliation:

1. Georgia Institute of Technology

2. Pacific Northwest National Lab (PNNL)

Funder

Intel Science and Technology Center in Cloud Computing

Publisher

ACM

Reference42 articles.

1. Cage15: www.cise.ufl.edu/research/sparse/matrices/vanheukelum. Cage15: www.cise.ufl.edu/research/sparse/matrices/vanheukelum.

2. CUDA 7.0: https://developer.nvidia.com/cuda-downloads/. CUDA 7.0: https://developer.nvidia.com/cuda-downloads/.

3. DIMACS10 ak2010: www.cise.ufl.edu/research/sparse/matrices/dimacs10. DIMACS10 ak2010: www.cise.ufl.edu/research/sparse/matrices/dimacs10.

4. DIMACS10 belgium_osm: www.cise.ufl.edu/research/sparse/matrices/dimacs10. DIMACS10 belgium_osm: www.cise.ufl.edu/research/sparse/matrices/dimacs10.

5. DIMACS10 coAuthorsDBLP: www.cc.gatech.edu/dimacs10/data/coauthor/. DIMACS10 coAuthorsDBLP: www.cc.gatech.edu/dimacs10/data/coauthor/.

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1. Ph.D. Project: Optimizing the Data Traffic for Large Graph Processing on FPGA via a Stateful Approach;2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM);2024-05-05

2. INFINEL: An efficient GPU-based processing method for unpredictable large output graph queries;Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming;2024-02-20

3. CGgraph: An Ultra-Fast Graph Processing System on Modern Commodity CPU-GPU Co-processor;Proceedings of the VLDB Endowment;2024-02

4. HongTu: Scalable Full-Graph GNN Training on Multiple GPUs;Proceedings of the ACM on Management of Data;2023-12-08

5. Accelerating GNN Training by Adapting Large Graphs to Distributed Heterogeneous Architectures;IEEE Transactions on Computers;2023-12

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