Book Image

Delphi High Performance

By : Primož Gabrijelčič
Book Image

Delphi High Performance

By: Primož Gabrijelčič

Overview of this book

Delphi is a cross-platform Integrated Development Environment (IDE) that supports rapid application development for Microsoft Windows, Apple Mac OS X, Google Android, iOS, and now Linux with RAD Studio 10.2. This book will be your guide to build efficient high performance applications with Delphi. The book begins by explaining how to find performance bottlenecks and apply the correct algorithm to fix them. It will teach you how to improve your algorithms before taking you through parallel programming. You’ll then explore various tools to build highly concurrent applications. After that, you’ll delve into improving the performance of your code and master cross-platform RTL improvements. Finally, we’ll go through memory management with Delphi and you’ll see how to leverage several external libraries to write better performing programs. By the end of the book, you’ll have the knowledge to create high performance applications with Delphi.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
Packt Upsell

Chapter 2. Fixing the Algorithm

In the previous chapter, we explored the concept of performance and looked at different scenarios where we would like to make the program faster. The previous chapter was largely theoretical, but now is the time to look at it in a more practical way.

There are two main approaches to speeding up a program:

  • Replace the algorithm with a better one.
  • Fine-tune the code so that it runs faster.

I spent lots of time in the previous chapter discussing the time complexity simply to make it clear that a difference between two algorithms can result in impressive speed-up. It can be much more than a simple constant factor (such as a 10-times speed-up). If we go from an algorithm with bad time complexity (say, O(n2)) to an algorithm with a better behavior (O(n log n), for example), then the difference in speed becomes more and more noticeable when we increase the size of the data.

Saying all that, it should not be surprising that I prefer the first approach (fixing the algorithm...