Programmers and Data Scientists want to take advantage of fast and parallel computational devices. One of the best ways of doing this is to write vectorized code; however, this is often easier said than done. In today’s continuation of the blog series “Learning ArrayFire from scratch“, we will discuss the various methods by which you can maximize the performance of your code by using ArrayFire’s built-in vectorization features.
How to write vectorized code
Programmers and Data Scientists want to take advantage of fast and parallel computational devices. Writing vectorized code is becoming a necessity to get the best performance out of the current generation parallel hardware and scientific computing software. However, writing vectorized code may not be intuitive immediately. There are many ways you can vectorize a given code segment. Each method has its own benefits and drawbacks. Hence, writing vectorized code involves analyzing the pros and cons of the available methods and choosing the right one to solve your problem. In this post, I present various ways to vectorize your code using ArrayFire. ArrayFire is chosen because of my familiarity with the software. The same methods can be easily used in numpy, octave, …