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2014 23rd International Conference on Parallel Architecture and Compilation (PACT) (2014)
Edmonton, Canada
Aug. 23, 2014 to Aug. 27, 2014
ISBN: 978-1-5090-6607-0
pp: 301
Bob Blainey , Hardware Acceleration Laboratory, IBM Software Group, Markham, Ontario, Canada
ABSTRACT
Big data is a transformational force for businesses and organizations of every stripe. The ability to rapidly and accurately derive insights from massive amounts of data is becoming a critical competitive differentiator so it is driving continuous innovation among business analysts, data scientists, and computer engineers. Two of the most important success factors for analytic techniques are the ability to quickly develop and incrementally evolve them to suit changing business needs and the ability to scale these techniques using parallel computing to process huge collections of data. Unfortunately, these goals are often at odds with each other because innovation at the algorithm and data model level requires a combination of domain knowledge and expertise in data analysis while achieving high scale demands expertise in parallel computing, cloud computing and even hardware acceleration. In this talk, I will examine various approaches to bridging these two goals, with a focus on domain-specific models that simultaneously improve the agility of analytics development and the achievement of efficient parallel scaling.
INDEX TERMS
Software, Hardware, Parallel processing, Analytical models, Technological innovation, Acceleration, Business,parallel scaling, Application Development, analytics, domain-specific models, parallel computing
CITATION
Bob Blainey, "Keynote: Domain-specific models for innovation in analytics", 2014 23rd International Conference on Parallel Architecture and Compilation (PACT), vol. 00, no. , pp. 301, 2014, doi:10.1145/2628071.26359312
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