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Learn about the breakthrough level of performance GPUs deliver for large-scale analytics problems. See use cases, architectures and how the latest GPU-accelerated databases can both improve performance and reduce energy and data center costs.
Eaton will discuss the latest GPU-accelerated databases, graph analytics use cases and how all of this can fit into an existing workflow based on SPARK or HDFS.
Joe is the technical lead for Accelerated Graph and Data Analytics at NVIDIA. Before, he was Chief Scientist at Object Reservoir, producing Finite-Element based 3phase reservoir simulations with automated adaptive meshing technology. Next he worked as VP of Engineering at Algebraix Data, leading a development team to produce a high-performance SPARQL query engine. He joined NVIDIA in 2013 to work in the CUDA Libraries team on sparse linear algebra methods.
Joe holds a Ph.D. in Computational and Applied Mathematics from UT Austin, a Masters in Mechanical Engineering from Stanford, and a Bachelors in Mechanical Engineering from Rice University.