a b s t r a c t

High performance computing has been used in various fields of astrophysical research. But most of it

is implemented on massively parallel systems (supercomputers) or graphical processing unit clusters.

With the advent of multicore processors in the last decade, many serial software codes have been reimplemented

in parallel mode to utilize the full potential of these processors. In this paper, we propose

parallel processing recipes for multicore machines for astronomical data processing. The target audience

is astronomers who use Python as their preferred scripting language and who may be using PyRAF/IRAF

for data processing. Three problems of varied complexity were benchmarked on three different types

of multicore processors to demonstrate the benefits, in terms of execution time, of parallelizing data

processing tasks. The native multiprocessing module available in Python makes it a relatively trivial task

to implement the parallel code.Wehave also compared the three multiprocessing approaches—Pool/Map,

Process/Queue and Parallel Python. Our test codes are freely available and can be downloaded from our

website