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Massively Parallel Astronomic Event Detection u sing GPU’s

This paper discusses innovative approaches to detecting astronomical events, focusing on Trans-Neptunian Objects and stellar light curves. It outlines the development of a GPU-based sliding window cross-correlation method that processes massive datasets from proposed NASA satellite missions. The monitoring system analyzes the brightness variations of 10,000 stars at a rate of 40 Hz, equating to about 3.46 billion measurements daily. The section on code implementation highlights optimization techniques to efficiently handle data while minimizing latency, aiming to enhance event detection capabilities significantly.

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Massively Parallel Astronomic Event Detection u sing GPU’s

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  1. Massively Parallel Astronomic Event Detectionusing GPU’s Matthias Alexander Lee Wentworth Institute of Technology Dr. PavlosProtopapas Help and Moral support: Richard Edgar, Mike Clark and Tom Buckley

  2. What are we detecting? • Trans-Neptunian-Objects • Light Curves • Brightness of a star graphed over time • Massive Data Sets

  3. Where’s the Data from? Whipple • Proposed NASA satellite • Monitors 10,000 stars • 40hz 86,400s/day • Total of 3.456*10^10

  4. Search Method:Sliding Window Cross Correlation • Generate Templates

  5. Search Method:Sliding Window Cross Correlation • Generate Templates

  6. Search Method:Sliding Window Cross Correlation • Generate Templates Event Detected!

  7. The Code: part I • 2 Main Function. • PreProc() • Reads in Templates • Normalizes Templates • Generates • Binary: contains all data • Dicionary: contains metadata about templates

  8. The Code: part II • 2 Main Function. • PreProc() • SearchLC() • Reads binary of Templates and Lightcurve • Stores at Texture • Launches 30 blocks of 64 threads per LC • Ensures shared memory is kept low enough for 2 blocks to be active on a multiprocessor, therefore hiding latency

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