Allok: a machine learning approach for efficient graph execution on CPU–GPU clusters-Reference-Cited by-同舟云学术

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Allok: a machine learning approach for efficient graph execution on CPU–GPU clusters

Published:2024-05-23 Issue:13 Volume:80 Page:18838-18865
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Moori Marcelo Koji,Rocha Hiago Mayk G. de A.,Lorenzon Arthur F.,Beck Antonio Carlos S.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06079-9.pdf

Reference41 articles.

1. Dhulipala L, Blelloch GE, Shun J (2021) Theoretically efficient parallel graph algorithms can be fast and scalable. ACM Trans Parallel Comput (TOPC) 8(1):1–70

2. Yang C, Buluç A, Owens JD (2022) GraphBLAST: a high-performance linear algebra-based graph framework on the GPU. ACM Trans Math Softw (TOMS) 48(1):1–51

3. Davis TA (2019) Algorithm 1000: SuiteSparse: GraphBLAS: graph algorithms in the language of sparse linear algebra. ACM Trans Math Softw (TOMS) 45(4):1–25

4. Khorasani F, Vora K, Gupta R, Bhuyan LN (2014) CuSha: vertex-centric graph processing on GPUs. In: Proceedings of the 23rd International Symposium on High-performance Parallel and Distributed Computing, pp 239–252

5. Shun J, Blelloch GE (2013) Ligra: a lightweight graph processing framework for shared memory. In: Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp 135–146

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