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Supporting scalablility and efficiency for scientific applications


Scientific high-performance computing has supported numerous advances in several areas ofhuman knowledge in the last two decades.During this period, most organizations including government bodies, research institutions and companies have intensified the production and analysis of large data volumes on an unprecedented way. The volume and variety of data have increasedmuch faster than the available capacity of computers in the same period. Such disparity between the demand for information processing and the computational capacity has pushed the advancesin scientific computing and high-performance computing. This proposal aims at improving thescientific high performance computing in four main research topics: (i) information retireval andquery processing using massive parallel computing; (ii) high performance novelty detecion andmalware analytics in data streams for cibersecirity; (iii) cloud computing for the scalable and efficient genome assembly and sequence alignment; (iv) scalability analysis of bag-of-tasks applications in heterogeneous computing platforms. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
GAIOSO, ROUSSIAN; GIL-COSTA, VERONICA; GUARDIA, HELIO; SENGER, HERMES. Performance evaluation of single vs. batch of queries on GPUs. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, v. 32, n. 20, SI, . (15/24461-2, 18/00452-2)

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