Fei Chen - Oracle | LinkedIn (original) (raw)

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Fei Chen is a Director of AI and Advanced Compute solutions at AWS. Before that she was a…

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Hewlett Packard Labs December 22, 2016

Iterative graph processing has emerged as a necessary and powerful tool at the heart of
solutions to several large scale analytics problems such as malware detection, genome
analysis and online advertising. Due to the large amount of communication involved,
iterative graph processing on large graphs does not scale well on distributed systems.
To demonstrate the benefits of a memory driven computing architecture, we designed
and implemented an iterative graph processing engine…
Iterative graph processing has emerged as a necessary and powerful tool at the heart of
solutions to several large scale analytics problems such as malware detection, genome
analysis and online advertising. Due to the large amount of communication involved,
iterative graph processing on large graphs does not scale well on distributed systems.
To demonstrate the benefits of a memory driven computing architecture, we designed
and implemented an iterative graph processing engine that uses The Machine’s Fabric attached-memory as a communication medium. The engine demonstrates near-linear
scalability with a 162x speed-up over a state of the art graph processing system on a
Superdome X and a projected 85x speed up over the same system on the Machine
Fabric Testbed (MFT).
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SIGMOD 2014

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Local Chair of ACM SIGMOD 2016

Jan 2007 - Present

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