Why GPUs Are More Efficient Than CPUs

Buried within the heart of modern computers are two seemingly identical silicon-based microprocessor chips. Both can perform thousands of operations per second. Both use huge volumes of transistors, and both are powered by internal cores. Yet the differences between a machine’s central processing unit and its graphics processing unit are significant. The variations between a GPU and CPU become extremely significant if the computer is juggling complex processing or rendering tasks…

Central Perks 

A CPU can be considered as the brain of a computer, handling simultaneous tasks like software loading and stack operations. While it is highly skilled at performing certain functions, a CPU may become overwhelmed by multitasking requirements, such as supporting video editing while simultaneously streaming a Spotify playlist. Modern CPUs tend to contain four or eight cores, running at speeds between one and four gigahertz per second (GHz). This means they can execute between four and eight simultaneous instructions in each clock cycle.

Graphical Illustrations 

A GPU is a specialized type of processor, running at slower clock speeds but containing thousands of individual cores, each one capable of executing one instruction at any given millisecond. GPUs were originally developed to support the complex rendering required for three-dimensional computer game graphics and were often marketed as an optional upgrade on Millennial PCs. Because it’s intended to conduct a single task at lightning speed, a GPU offers far greater scope for processing. However, it’s by no means a CPU replacement, which can perform a far wider range of tasks.

The Power And The Glory 

While CPUs are powerful in their own right, and essential for certain computing tasks, their overall performance is completely shaded by GPUs containing thousands of cores. One key difference between GPUs and CPUs is that the latter can conduct tasks like big data analysis up to a hundred times faster. Another is the amount of electrical power required to run these processors – hence bitcoin miners harnessing arrays of GPUs to carry out repetitive, non-graphical mining tasks. 

Companies seeking to conduct large-scale data processing have found the choice between GPUs and CPUs to be an easy one. Machine learning algorithms are a prime example of the functions that today’s computers are expected to process, with massive volumes of calculations required to complete a single specific task. And though they started out as 3D graphics rendering tools, GPUs are equally well-suited to processing data to improve areas like speech recognition and financial modeling. In terms of sheer brute force processing, they’re peerless. 

GPUs and CPUs work best in tandem, with the former handling data-intensive tasks and the latter managing a system’s wider resources, such as memory and software management. Today’s supercomputers tend to delegate processing responsibilities to GPUs, especially since the latest and best GPU chips are delivering faster year-on-year performance increases than today’s high-end CPUs. If your business involves machine learning, data processing or algorithmic analysis, the choice between GPUs and CPUs is an easy one.