Parallelization and performance tuning
In part 1 of this post, we introduced the mpi4py module (MPI for Python) which provides an object-oriented interface for Python resembling the message passing interface (MPI) and enables Python programs to exploit multiple processors on multiple compute nodes. The mpi4py module provides methods for communicating various types of Python objects in different ways. In … Continue reading “Parallel programming in Python: mpi4py (part 2)”
In previous posts we have introduced the multiprocessing module which makes it possible to parallelize Python programs on shared memory systems. The limitation of the multiprocessing module is that it does not support parallelization over multiple compute nodes (i.e. on distributed memory systems). To overcome this limitation and enable cross-node parallelization, we can use MPI … Continue reading “Parallel programming in Python: mpi4py (part 1)”
You may have heard of the Top500 list. It ranks the world’s 500 most powerful supercomputers based on their performance as measured by the Linpack benchmark. Published twice per year (in June and November) since 1993, the Top500 list records the development of supercomputers over the past two to three decades. In addition to performance, … Continue reading “Top500 list: a brief introduction”
In the previous post we introduced the Pool class of the multiprocessing module. In this post we continue on and introduce the Process class, which makes it possible to have direct control over individual processes. A process can be created by providing a target function and its input arguments to the Process constructor. The process can then be started with … Continue reading “Parallel programming in Python: multiprocessing (part 2)”
Parallel programming solves big numerical problems by dividing them into smaller sub-tasks, and hence reduces the overall computational time on multi-processor and/or multi-core machines. Parallel programming is well supported in traditional programming languages like C and FORTRAN, which are suitable for “heavy-duty” computational tasks. Traditionally, Python is considered to not support parallel programming very well, … Continue reading “Parallel programming in Python: multiprocessing (part 1)”
