With parallel processing by using multiple cores, you can increase the amount of calculations your program can do in a given time frame without needing a faster processor. The main idea is to divide a problem into independent subunits and use multiple cores to solve those subunits in parallel.
Parallel processing is necessary to tackle large-scale problems. Companies produce massive quantities of data every day that need to be stored in multiple computers and analyzed. Scientists and engineers run parallel code on supercomputers to simulate massive systems.
Parallel processing allows you to take advantage of multicore CPUs as well as GPUs that work extremely well with highly parallel problems. In this chapter, we will cover the following topics:
- A brief introduction to the fundamentals of parallel processing
- Illustrating how to parallelize simple problems with the multiprocessing Python library...