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DTSTAMP:20181221T160742Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181113T171500
DTEND;TZID=America/Chicago:20181113T190000
UID:submissions.supercomputing.org_SC18_sess343_drs117@linklings.com
SUMMARY:The Algorithm and Framework Designs and Optimizations for Scalable
  Automata Processing on HPC Platforms
DESCRIPTION:Doctoral Showcase\nWorkshop Reg Pass, Tutorial Reg Pass, Tech 
 Program Reg Pass, Exhibits Reg Pass, Exhibits - Exhibit Hall Only Reg Pass
 \n\nThe Algorithm and Framework Designs and Optimizations for Scalable Aut
 omata Processing on HPC Platforms\n\nYu, Yao\n\nAutomata processing could 
 perform as the core of many applications in the areas such as network secu
 rity, text mining, and bioinformatics. Achieving high-speed and scalable a
 utomata processing is exceptionally challenging. For one thing, the classi
 c DFA representation is memory-bandwidth efficient but suffer from the sta
 te explosion problem in the presence of large datasets with complex patter
 ns. And for another, the automata processing is inherently difficult to be
  parallelized due to the strong dependencies and unstructured memory-acces
 s pattern.<br /><br />  In this thesis, we provide a comprehensive sc
 heme for improving automata processing efficiency. At the algorithm level,
  we propose JFA that uses state variables to avoid state explosion. We als
 o propose O3FA to handle out-of-order packets in NIDS. At the implementati
 on level, we propose a comprehensive GPU-based automata processing framewo
 rk and a code-generation framework for Automata Processors. Moreover, we p
 rovide a toolchain to conduct the apple-to-apple automata processing accel
 erators comparison.
URL:https://sc18.supercomputing.org/presentation/?id=drs117&sess=sess343
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