Paper Details

@InProceedings{TidmusMiles97,
  title = "{P}refetch {D}ata {M}anagement for {P}arallel {P}article {T}racing",
  author= "Tidmus, Jonathan and Miles, Roger and Chalmers, Alan G.",
  editor= "Bakkers, Andr\`{e} W. P.",
  pages = "130--137",
  booktitle= "{P}roceedings of {W}o{TUG}-20: {P}arallel {P}rogramming and {J}ava",
  isbn= "90 5199 336 6",
  year= "1997",
  month= "mar",
  abstract= "The particle tracing method uses a stochastic approach for
     global illumination computation of three-dimensional
     environments. As with many graphics techniques the
     computation associated with the image generation is complex.
     Parallel processing offers the potential of solving the
     computational complex particle tracing more rapidly.
     Distributed memory parallel systems are scalable and readily
     available. However, large environmental models are often
     bigger than individual node storage capabilities requiring
     data management to distribute and control the movement of
     environmental data as computation proceeds. Prefetch data
     management attempts to reduce idle time associated with
     remote data fetches by anticipating the latency and
     requesting required data items prior to their actual use
     during computation. This paper demonstrates how attention to
     work division and supply coupled with prefetch data
     management can be utilised to minimise overheads associated
     with a parallel implementation and reduce overall image
     synthesis time."
}

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