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Hadoop Acceleration with Scalable Merging Algorithms
-- Weikuan Yu, Auburn University

Abstract:
Cloud computing has emerged as a new computing paradigm for the government and industry to process and analyze increasingly larger volumes of data. Hadoop implements the MapReduce programming model for cloud computing. However, it faces a number of issues to achieve the best performance from the underlying system. These include a serialization barrier that delays the reduce phase, repetitive merges and disk access, and lack of capability to leverage latest high speed interconnects. This presentation will shed light on a number of recent efforts to leverage high speed interconnects and accelerate Hadoop for fast data analytics.

Bio:
Weikuan Yu is currently an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. He directs the Parallel Architecture and System Laboratory (PASL) which hosts a NVIDIA teaching center at Auburn University and a 30Teraflop, 80-node heterogenous GPU+CPU computer cluster. Yu has research interests on cloud computing, high-performance computing, computer architecture, file and storage systems, and interdisciplinary research on climate modeling and computational biology. Yu's research is sponsored by NASA, NSF, ORNL, Mellanox, NVIDIA, and Auburn University.