So What's the Real Difference Between a CPU and a GPU?
Graphics Processing Units — GPUs — have become the kings of the market in recent years, turning Nvidia into one of the largest companies in the world.
These processors are responsible, first and foremost, for everything that appears on our screens — any content, on any display. But beyond that, they are the engine powering the gaming industry, film, computer graphics, Bitcoin, and more recently, artificial intelligence.
GPUs are the new oil (alongside digital data), and the hunger for them will only grow with time. They are gradually becoming one of the most indispensable engines of the modern world.
But what exactly are they? What sets a graphics processor apart from a regular one?
Understanding the internal architecture of a processor is a fascinating topic — one for another post. For now, let's focus on the fundamental difference between a CPU and a GPU.
A processor can perform one computational operation at a time.
To execute operations in parallel, chip manufacturers integrate multiple small processors onto a single board, with each processing unit called a "core."
A core can be physical — meaning several physical processors soldered onto a single board (cores) — or logical, meaning a single physical processing chip that is logically divided to function as two processors (threads).
A modern CPU has a relatively small number of cores; even in relatively expensive processors, you'll typically find 16–24 cores and no more. These cores handle complex computational tasks very well, but the number of operations running in parallel is still limited by the number of available cores.
A GPU, by contrast, places its emphasis not on "smart" processing cores, but on a very large number of "dumb" ones. A modern graphics card designed primarily for video games can contain several thousand graphics cores.
In practice, this means a CPU is especially efficient for everyday computing tasks such as browsing the web, using software applications, and writing documents. These tasks benefit from the CPU's relative advantages over a GPU — such as a very high clock speed and basic functions that have been pre-coded and embedded directly in the processor.
A GPU, on the other hand, is designed for tasks where the main priority is executing as many relatively simple operations as possible, simultaneously.
In video games, for example, the graphics card's job is to calculate the position and color of the millions of polygons that make up each frame on screen, at 60 frames per second or more. A regular processor — no matter how powerful or fast — simply cannot perform those millions of calculations at a sufficient rate. The GPU, however, distributes the task across the thousands of weaker cores that make it up, and handles the workload successfully.
In Bitcoin mining, computer graphics, and artificial intelligence as well, what matters most is performing the largest possible number of operations in parallel — not executing them one after another at high speed.
The bottom line: the main bottleneck in your computer's processor is not its speed, but the number of cores it contains.
One interesting exception in the market is Apple Silicon — the processors manufactured by Apple. They deliver impressive performance in computer graphics applications despite having a particularly low core count.
How do they do it? Next time.
*Pictured: Nvidia's A100 processor, designed for artificial intelligence applications*
*Source: Nvidia*
#not_written_with_AI #GPU #CPU