Parallel Programming with Python by Jan Palach

By Jan Palach

Beginning with the fundamentals of parallel programming, you'll continue to benefit approximately find out how to construct parallel algorithms and their implementation. you'll then achieve the services to judge challenge domain names, determine if a specific challenge should be parallelized, and the way to exploit the Threading and Multiprocessor modules in Python.

The Python Parallel (PP) module, that's one other mechanism for parallel programming, is roofed extensive that will help you optimize the use of PP. additionally, you will delve into utilizing Celery to accomplish disbursed initiatives successfully and simply. additionally, you are going to find out about asynchronous I/O utilizing the asyncio module. ultimately, by means of the top of this booklet you'll collect an in-depth knowing approximately what the Python language has to provide by way of integrated and exterior modules for an efficient implementation of Parallel Programming.

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Extra resources for Parallel Programming with Python

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This collection process is carried out by several worker bees who work simultaneously in order to solve the problem of lack of pollen. The worker bees play the role of threads, acting inside a process and sharing resources to perform their tasks. Threads belong to the same process and share the same memory space. Hence, the developer's task is to control and access these areas of memory. futures Modules Advantages and disadvantages of using threads Some advantages and disadvantages have to be taken into account when deciding on the use of threads, and it depends on the language and operating system used to implement a solution.

Info 16 12 20 [ workerø3 workerø4 Data decomposition in a matrix example [ 20 ] 8 Chapter 2 The matrix problem presented in the preceding diagram had a certain symmetry where each necessary operation to get to the final result was executed by a single worker, and each worker executed the same number of operations to resolve the problem. Nevertheless, in real world, there is an asymmetry of the relation between the number of workers and the quantity of data that is decomposed, and this directly affects the performance of the solution.

Summary In this chapter, we learned some parallel programming concepts, and learned about some models, their advantages, and disadvantages. Some of the problems and potential issues when thinking of parallelism have been presented in a brief explanations. We also had a short introduction to some Python modules, built-in and external, which makes a developer's life easier when building up parallel systems. In the next chapter, we will be studying some techniques to design parallel algorithms. info Designing Parallel Algorithms While developing parallel systems, several aspects must be observed before you start with the lines of code.

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