SC13 Denver, CO

The International Conference for High Performance Computing, Networking, Storage and Analysis

Achieving High Performance for Big Data Analytics.


Student: Oded Green (Georgia Institute of Technology)
Supervisor: David Bader (Georgia Institute of Technology)

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.

Poster: pdf
Two-page extended abstract: pdf


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