Parallel computing is the design, study, and process of using algorithms to make multiple computers solve computational problems simultaneously. In parallel computing, problems are split up into several parts for more than two computers to work on at the same time. These parts each have their own set of instructions that are executed on multiple central processing units (CPUs).

Parallel computing speeds up problem-solving in computational learning. Previously, most computations were done individually by software written for use on a single computer with one CPU. This meant that only one instruction could be executed at any time.‎

It's important to learn about parallel computing because it is the primary system used by programmers today to increase computation power for faster application problem solving and processing power. From multi-core personal computers to supercomputers, parallel computing is what is driving the core processing power.

A processor is the computing power that drives your phone, laptop, and tablet. With more power in the processor, applications will run faster and better on your mobile device. Even smartphones are using multicore processing power, with some up to four and eight cores. Parallel computing helps to make this possible. This alone makes parallel computing worth knowing about for its current device applications and also its growing importance into the future.‎

Learning parallel computing can bring you into careers as a parallel programming architect, a software engineer, or a high performance computing engineer. Many companies are looking for computing talents who can deliver and innovate in the graphics and parallel computing fields.

Companies are continually looking to develop new parallel programming models for their GPU architecture. This is where learning about parallel computing can help you to succeed. By gaining new knowledge for creating new infrastructures and new architectures in parallel computing, you can play a part in advancing the state-of-the-art graphics performance in parallel programming models.‎