SC13 Home > SC13 Schedule > SC13 Presentation - Achieving High Performance for Big Data Analytics

SCHEDULE: NOV 16-22, 2013

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Achieving High Performance for Big Data Analytics

SESSION: ACM Student Research Competition Poster Reception

EVENT TYPE: ACM Student Research Competition Posters, ACM Student Research Competition

TIME: 5:15PM - 7:00PM

AUTHOR(S):Oded Green

ROOM:Mile High Pre-Function

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.

Chair/Author Details:

Oded Green - Georgia Institute of Technology

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