Thesis (Selection of subject)Thesis (Selection of subject)(version: 285)
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Employing Parallel Computing in Data Analytics
Thesis title in Czech: Použití paralelních výpočtů v datově-analytických úlohách
Thesis title in English: Employing Parallel Computing in Data Analytics
Key words: paralelní výpočty, datová analýza, data mining, vysoce výkonné počítání
English key words: parallel computing, data analytics, data mining, high performance computing
Academic year of topic announcement: 2017/2018
Type of assignment: dissertation
Thesis language: angličtina
Department: Department of Software Engineering (32-KSI)
Supervisor: RNDr. Martin Kruliš, Ph.D.
Author:
Guidelines
In the past decade, mainstream hardware has experienced a significant shift towards parallelism.
Multicore CPUs as well as manycore GPUs are present in commodity PCs and laptops
and specialized massively parallel accelerators (such as Xeon Phi devices) have
emerged in the high performance computing domain. Unfortunately, most applications
and algorithms of the day are not ready for such a radical change and cannot be
easily ported to parallel platforms.

The main objective of this thesis is to investigate algorithms, data structures,
and methods employed in the domain of data analytics in order to adopt them for
parallel platforms of the day. Particular attention will be payed to computationally
intensive problems, which are solved solely by fully automated processing, and
which will benefit the most from the potential speedup of parallel hardware.
The investigated areas will include data mining algorithms, optimization problems,
and pattern recognition. The proposed modifications will be implemented and
empirically evaluated.

References
Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. Introduction to data mining. Vol. 1. Boston: Pearson Addison Wesley, 2006.

Russom, Philip. "Big data analytics." TDWI Best Practices Report, Fourth Quarter (2011).

Wu, Ren, Bin Zhang, and Meichun Hsu. "GPU-accelerated large scale analytics." IACM UCHPC (2009).

 
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