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DTSTART:19700308T020000
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DTSTAMP:20260522T150110Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181114T083000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess342_drs117@linklings.com
SUMMARY:The Algorithm and Framework Designs and Optimizations for Scalable
Automata Processing on HPC Platforms
DESCRIPTION:Xiaodong Yu and Danfeng Yao (Virginia Tech)\n\nAutomata proces
sing could perform as the core of many applications in the areas such as n
etwork security, text mining, and bioinformatics. Achieving high-speed and
scalable automata processing is exceptionally challenging. For one thing,
the classic DFA representation is memory-bandwidth efficient but suffer f
rom the state explosion problem in the presence of large datasets with com
plex patterns. And for another, the automata processing is inherently diff
icult to be parallelized due to the strong dependencies and unstructured m
emory-access pattern.
In this thesis, we provide a comprehens
ive scheme for improving automata processing efficiency. At the algorithm
level, we propose JFA that uses state variables to avoid state explosion.
We also propose O3FA to handle out-of-order packets in NIDS. At the implem
entation level, we propose a comprehensive GPU-based automata processing f
ramework and a code-generation framework for Automata Processors. Moreover
, we provide a toolchain to conduct the apple-to-apple automata processing
accelerators comparison.\n\nRegistration Category: Workshop Reg Pass, Tut
orial Reg Pass, Tech Program Reg Pass, Exhibits Reg Pass, Exhibits - Exhib
it Hall Only Reg Pass\n\n
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