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Timestamp:
Aug 23, 2011, 9:55:22 AM (8 years ago)
Author:
cameron
Message:

Minor edits in abstract/intro

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  • docs/HPCA2012/01-intro.tex

    r1348 r1349  
    88shut off. Chip makers strive to achieve energy efficient computing by
    99operating at more optimal core frequencies and aim to increase
    10 performance with larger number of cores. Unfortunately, given the
     10performance with a larger number of cores. Unfortunately, given the
    1111limited levels of parallelism that can be found in
    1212applications~\cite{blake-isca-2010}, it is not certain how many cores
     
    3939transposing byte-oriented character data into parallel bit streams for
    4040the individual bits of each byte, the Parabix framework exploits the
    41 SIMD extensions (SSE/AVX on x86, Neon on ARM) on commodity processors
     41SIMD extensions on commodity processors (SSE/AVX on x86, Neon on ARM)
    4242to process hundreds of character positions in an input stream
    4343simultaneously.  To achieve transposition, Parabix exploits
    4444sophisticated SIMD instructions that enable data elements to be packed
    45 and unpacked from registers in a regular manner which improve the
     45and unpacked from registers in a regular manner which improves the
    4646overall cache access behavior of the application resulting in
    4747significantly fewer misses and better utilization.  Parabix also
     
    5757applications ranging from Office Open XML in Microsoft Office to NDFD
    5858XML of the NOAA National Weather Service, from KML in Google Earth to
    59 Castor XML in the Martian Rovers, a XML data in Android phones.  XML
     59Castor XML in the Martian Rovers, as well as ubiquitous XML data in Android phones.  XML
    6060parsing efficiency is important for multiple application areas; in
    6161server workloads the key focus in on overall transactions per second,
    62 while in applications in the network switches and cell phones, latency
    63 and the energy are of paramount importance.  Traditional
    64 software-based XML parsers have many inefficiencies due to complex
    65 input-dependent branching structures leading to considerable branch
    66 misprediction penalties as well as poor use of memory bandwidth and
     62while in applications in network switches and cell phones, latency
     63and energy are of paramount importance.  Traditional
     64software-based XML parsers have many inefficiencies including
     65considerable branch misprediction penalties due to complex
     66input-dependent branching structures as well as poor use of memory bandwidth and
    6767data caches due to byte-at-a-time processing and multiple buffering.
    6868XML ASIC chips have been around for over 6 years, but typically lag
    6969behind CPUs in technology due to cost constraints. Our focus is how
    70 much can we improve performance of the XML parser on commodity
     70much we can improve performance of the XML parser on commodity
    7171processors with Parabix technology.
    7272
    7373In the end, as summarized by
    7474Figure~\ref{perf-energy} our Parabix-based XML parser improves the
    75 performance by ?$\times$ and energy efficiency by ?$\times$ compared
     75performance by %?$\times$
     76and energy efficiency %by ?$\times$
     77several-fold compared
    7678to widely-used software parsers and approaching the performance of
    77 ?$cycles/input-byte$ performance of ASIC XML
    78 parsers~\cite{}.\footnote{The actual energy consumption of the XML
     79%?$cycles/input-byte$
     80performance of ASIC XML
     81parsers.%~\cite{}.
     82\footnote{The actual energy consumption of the XML
    7983  ASIC chips is not published by the companies.}
    8084
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