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

Amdahl's Law's relationship to the law of diminishing returns

Amdahl's Law is often conflated with the law of diminishing returns, which is a rather popular concept in economics. However, the law of diminishing returns is only a special case of applying Amdahl's Law, depending on the order of improvement. If the order of separate tasks in the program is chosen to be improved in an optimal way, a monotonically decreasing improvement in execution time will be observed, demonstrating diminishing returns. An optimal method indicates first applying those improvements that will result in the greatest speedups, and leaving those improvements yielding smaller speedups for later.

Now, if we were to reverse this sequence for choosing resources, in which we improve less optimal components of our program before more optimal components, the speedup achieved through the improvement would increase throughout the process. Furthermore, it is actually more beneficial for us to implement system improvements...