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I implemented a continuous map-reduce framework called Phobos on top of NVIDIA CUDA. This presentation gives an intuition about the idea.

I implemented a continuous map-reduce framework called Phobos on top of NVIDIA CUDA. This presentation gives an intuition about the idea.

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Phobos Phobos Presentation Transcript

  • Phobos Mars on Steriods Jamie Jablin Kamran Azam Suman Karumuri Nathan Backman
  • Map Reduce • Given the stock data, compute the 200 day moving average for all the stocks. • Map – Filter stocks not in your portfolio. • Reduce – Compute the 200 day average for stocks in your portfolio.
  • Continuous Map Reduce • Given the stock data, compute the 200 day moving average for all the stocks every 1 hour. • Map – Filter stocks not in your portfolio. • Reduce – Compute the 200 day average for stocks in your portfolio.
  • At t=0 Window Sub-piece/ Stream Sub-window time
  • At t=1 Window Stream Slides
  • Redundant map computation Window Overlapping Stream Block
  • Enter Phobos • Eliminate these redundant map computations. • On CUDA. • Design – Instead of computing map on entire window, compute map on the new sub-window. – Compute the reduce on the whole window. • How? Keep old data around. • How long? In a circular buffer of size num of sub windows per window.
  • Implementation • Updated Mars – Added notion of windowing. – Eliminated redundant computations by using circular buffer. • Trade off latency for more working memory. – Keep the old data on host instead of the device.
  • Demo time!