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DTSTART;TZID=America/Chicago:20181113T153000
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UID:submissions.supercomputing.org_SC18_sess279_drs117@linklings.com
SUMMARY:The Algorithm and Framework Designs and Optimizations for Scalable
  Automata Processing on HPC Platforms
DESCRIPTION:Doctoral Showcase\nArchitectures, Memory, Runtime Systems, Sto
 rage, Workshop Reg Pass, Tutorial Reg Pass, Tech Program Reg Pass, Exhibit
 s Reg Pass, Exhibits - Exhibit Hall Only Reg Pass, Doctoral Showcase\n\nTh
 e Algorithm and Framework Designs and Optimizations for Scalable Automata 
 Processing on HPC Platforms\n\nYu, Yao\n\nAutomata processing could perfor
 m as the core of many applications in the areas such as network security, 
 text mining, and bioinformatics. Achieving high-speed and scalable automat
 a processing is exceptionally challenging. For one thing, the classic DFA 
 representation is memory-bandwidth efficient but suffer from the state exp
 losion problem in the presence of large datasets with complex patterns. An
 d for another, the automata processing is inherently difficult to be paral
 lelized due to the strong dependencies and unstructured memory-access patt
 ern.<br /><br />  In this thesis, we provide a comprehensive scheme f
 or improving automata processing efficiency. At the algorithm level, we pr
 opose JFA that uses state variables to avoid state explosion. We also prop
 ose O3FA to handle out-of-order packets in NIDS. At the implementation lev
 el, we propose a comprehensive GPU-based automata processing framework and
  a code-generation framework for Automata Processors. Moreover, we provide
  a toolchain to conduct the apple-to-apple automata processing accelerator
 s comparison.
URL:https://sc18.supercomputing.org/presentation/?id=drs117&sess=sess279
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