Book Image

Parallel Programming with Python

By : Jan Palach, Jan Palach V Cruz da Silva
Book Image

Parallel Programming with Python

By: Jan Palach, Jan Palach V Cruz da Silva

Overview of this book

Table of Contents (16 chapters)
Parallel Programming with Python
About the Author
About the Reviewers

Chapter 1. Contextualizing Parallel, Concurrent, and Distributed Programming

Parallel programming can be defined as a model that aims to create programs that are compatible with environments prepared to execute code instructions simultaneously. It has not been too long since techniques of parallelism began to be used to develop software. Some years ago, processors had a single Arithmetic Logic Unit (ALU) among other components, which could only execute one instruction at a time during a time space. For years, only a clock that measured in hertz to determine the number of instructions a processor could process within a given interval of time was taken into consideration. The more the number of clocks, the more the instructions potentially executed in terms of KHz (thousands of operations per second), MHz (millions of operations per second), and the current GHz (billions of operations per second).

Summing up, the more instructions per cycle given to the processor, the faster the execution. During the '80s, a revolutionary processor came to life, Intel 80386, which allowed the execution of tasks in a pre-emptive manner, that is, it was possible to periodically interrupt the execution of a program to provide processor time to another program; this meant pseudo-parallelism based on time-slicing.

In the late '80s, there came Intel 80486 that implemented a pipelining system, which in practice, divided the stage of execution into distinct substages. In practical terms, in a cycle of the processor, we could have different instructions being carried out simultaneously in each substage.

All the advances mentioned in the preceding section resulted in several improvements in performance, but it was not enough, as we were faced with a delicate issue that would end up as the so-called Moore's law (

The quest for high taxes of clock ended up colliding with physical limitations; processors would consume more energy, thereby generating more heat. Moreover, there was another as important issue: the market for portable computers was speeding up in the '90s. So, it was extremely important to have processors that could make the batteries of these pieces of equipment last long enough away from the plug. Several technologies and families of processors from different manufacturers were born. As regards servers and mainframes, Intel® deserves to be highlighted with its family of products Core®, which allowed to trick the operating system by simulating the existence of more than one processor even though there was a single physical chip.

In the Core® family, the processor got severe internal changes and featured components called core, which had their own ALU and caches L2 and L3, among other elements to carry out instructions. Those cores, also known as logical processors, allowed us to parallel the execution of different parts of the same program, or even different programs, simultaneously. The age core enabled lower energy use with power processing superior to its predecessors. As cores work in parallel, simulating independent processors, we can have a multi-core chip and an inferior clock, thereby getting superior performance compared to a single-core chip with higher clock, depending on the task.

So much evolution has, of course, changed the way we approach software designing. Today, we must think of parallelism to design systems that make rational use of resources without wasting them, thereby providing a better experience to the user and saving energy not only in personal computers, but also at processing centers. More than ever, parallel programming is in the developers' daily lives and, apparently, it will never go back.

This chapter covers the following topics:

  • Why use parallel programming?

  • Introducing the common forms of parallelization

  • Communicating in parallel programming

  • Identifying parallel programming problems

  • Discovering Python's programming tools

  • Taking care of Python Global Interpreter Lock (GIL)