Massively Parallel Astronomic Event Detection using GPU’s

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Presentation transcript:

Massively Parallel Astronomic Event Detection using GPU’s Matthias Alexander Lee Wentworth Institute of Technology Dr. Pavlos Protopapas Help and Moral support: Richard Edgar, Mike Clark and Tom Buckley Doug: The wikipedia of programming Richard: Help with CUDA Ros Reid: making it possible for me to be here Dom: Entertaining me

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

Where’s the Data from? Whipple Proposed NASA satellite Monitors 10,000 stars 40hz 86,400s/day Total of 3.456*10^10 So where does the data come from?

Search Method: Sliding Window Cross Correlation Generate Templates Generate templates of occultations where the objects have different parameters, such as size, speed and distance from the earth But since they are symmetric we only need half the template. We generate templates that match a wide variety of parameters

Search Method: Sliding Window Cross Correlation Generate Templates Generate templates of occultations where the objects have different parameters, such as size, speed and distance from the earth But since they are symmetric we only need half the template. This cuts the number of points to be compared in half.

Search Method: Sliding Window Cross Correlation Generate Templates Generate templates of occultations where the objects have different parameters, such as size, speed and distance from the earth But since they are symmetric we only need half the template. This cuts the number of points to be compared in half. Event Detected!

The Code: part I 2 Main Function. PreProc() Reads in Templates Normalizes Templates Generates Binary: contains all data Dicionary: contains metadata about templates Generate templates of occultations where the objects have different parameters, such as size, speed and distance from the earth But since they are symmetric we only need half the template. This cuts the number of points to be compared in half.

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 Generate templates of occultations where the objects have different parameters, such as size, speed and distance from the earth But since they are symmetric we only need half the template. This cuts the number of points to be compared in half.