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2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW) (2017)
Atlanta, Georgia, USA
June 5, 2017 to June 8, 2017
ISSN: 2332-5666
ISBN: 978-1-5386-3292-5
pp: 343-347
ABSTRACT
High-performance computing (HPC) systems are increasingly being used for data-intensive, or "Big Data", workloads. However, since traditional HPC workloads are compute-intensive, the HPC-Big Data convergence has created many challenges with optimizing data movement and processing on modern supercomputers. Our collaborative work addresses these challenges using a three-pronged approach: (i) measuring and modeling extreme-scale I/O workloads, (ii) designing a low-latency, scalable, on-demand burst-buffer solution, and (iii) optimizing graph algorithms for processing Big Data workloads. We describe the three areas of our collaboration and report on their respective developments.
INDEX TERMS
Computational modeling, Metadata, Big Data, Computer architecture, Acceleration, Production
CITATION

K. Brown et al., "Accelerating Big Data Infrastructure and Applications (Ongoing Collaboration)," 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), Atlanta, Georgia, USA, 2017, pp. 343-347.
doi:10.1109/ICDCSW.2017.74
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