Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
About Packt

Chapter 9

What is Amdahl's law? What problem does Amdahl's law look to solve?

Amdahl's law provides an estimate of the theoretical speedup in latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved.

Explain the formula of Amdahl's Law, along with its components.

The formula for Amdahl's Law is as follows:


In the preceding formula, the following applies:

  • S is the theoretical speedup in consideration.
  • B is the portion of the whole task that is inherently sequential.
  • j is the number of processors being utilized.

According to Amdahl's Law, would speedup increase indefinitely as resources in the system improved?

No; as the number of processors becomes larger, the efficiency gained through the improvement decreases.

What is the relationship between Amdahl's Law and the law of diminishing returns?

You have seen that in specific situations (namely, when only the number of processors increases), Amdahl's Law resembles the law of diminishing returns. Specifically, as the number of processors becomes larger, the efficiency gained through the improvement decreases, and the speedup curve flattens out.