gpu_IR

A public repo for GPU implementations of IR astronomical data reduction techniques.

How to use gpu_IR

gpu_IR is currently base on two scripts, benchmark.py and reduce.py. Both scripts take in a fits file of non-destructive or up-the-ramp data and performs a linear fit on each pixel. benchmark.py reduces (linear fit) the data on either a gpu or cpu, splitting up the job into smaller batches of pixels. The script runs through a range of batch sizes and reports the fastest data reduction time and the corresponding batch size. Then one can run reduce.py to reduce data with the optimal batch size as an input.

Running benchmark.py

Example python benchmark.py -i "data.fits" --cpu

Help:

usage: benchmark.py [-h] -i FILE [-c] [-g]

Reduce IR data with a linear fit

optional arguments:

 -h, --help            show this help message and exit
 -i FILE, --input FILE
                       input file (default: None)                      
 -c, --cpu             select cpu implementation (default: False)
 -g, --gpu             select gpu implementation (default: False)

Running reduce.py

python reduce.py -i "data.fits" -o "slopes.fits" --gpu -n 256

Help:

usage: reduce.py [-h] -i FILE -o FILE [-c] [-g] [-n NPIX]

Reduce IR data with a linear fit

optional arguments:
  -h, --help            show this help message and exit
  -i FILE, --input FILE
                        input file (default: None)
  -o FILE, --output FILE
                        output filename and path (default: None)
  -c, --cpu             select cpu implementation (default: False)
  -g, --gpu             select gpu implementation (default: False)
  -n NPIX, --number_of_pixels NPIX
                        number of pixels in batch (default: 65536)