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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20131120T001500Z
DTEND:20131120T020000Z
LOCATION:Mile High Pre-Function
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Irregular algorithms such as graph algorithms, sorting, and sparse matrix multiplication, present numerous programming challenges that include scalability, load balancing, and efficient memory utilization. In this age of Big Data we face additional challenges since the data is often streaming at a high velocity and we wish to make near real-time decisions for real-world events. For instance, we may wish to track Twitter for the pandemic spread of a virus. Analyzing such data sets requires combing algorithmic optimizations and utilization of massively multithreaded architectures, accelerator such as GPUs, and distributed systems. My research focuses upon designing new analytics and algorithms for continuous monitoring of dynamic social networks. Specifically, we deal with load balancing, scheduling, avoiding redundant computations, and utilizing network properties for designing dynamic graph algorithms.
SUMMARY:Achieving High Performance for  Big Data Analytics
PRIORITY:3
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