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Trends and Challenges in Large-Scale HPC Network Analysis

Trends and Challenges in Large-Scale HPC Network Analysis

Copyright: © 2018 |Pages: 27
ISBN13: 9781522537991|ISBN10: 1522537996|EISBN13: 9781522538004
DOI: 10.4018/978-1-5225-3799-1.ch006
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MLA

Alberto Garcia-Robledo, et al. "Trends and Challenges in Large-Scale HPC Network Analysis." Creativity in Load-Balance Schemes for Multi/Many-Core Heterogeneous Graph Computing: Emerging Research and Opportunities, IGI Global, 2018, pp.144-170. https://doi.org/10.4018/978-1-5225-3799-1.ch006

APA

A. Garcia-Robledo, A. Diaz-Perez, & G. Morales-Luna (2018). Trends and Challenges in Large-Scale HPC Network Analysis. IGI Global. https://doi.org/10.4018/978-1-5225-3799-1.ch006

Chicago

Alberto Garcia-Robledo, Arturo Diaz-Perez, and Guillermo Morales-Luna. "Trends and Challenges in Large-Scale HPC Network Analysis." In Creativity in Load-Balance Schemes for Multi/Many-Core Heterogeneous Graph Computing: Emerging Research and Opportunities. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3799-1.ch006

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Abstract

Many algorithms in graph analytics can be sped up by using the power of low-cost but massively parallel architectures, such as GPUs. On the other hand, the storage and analysis capabilities needed for large-scale graph analytics have motivated the development of a new wave of HPC technologies, including MapReduce-like BSP distributed analytics, No-SQL data storage and querying, and homogeneous and hybrid multi-core/GPU graph supercomputing. In this chapter, the authors review these trends and current challenges for HPC large-scale graph analysis.

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