Distributed Computing with Python
CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications.
This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.
|Course Length||5 hours 6 minutes|
|Date Of Publication||11 Apr 2016|
|The big picture|
|Common problems – clocks and time|
|Common problems – software environments|
|Common problems – permissions and environments|
|Common problems – the availability of hardware resources|
|Challenges – the development environment|
|A useful strategy – logging everything|
|A useful strategy – simulating components|