The Graphical Processing Unit (GPU) is a hardware component in computers, originally designed for rendering graphics but has recently found extensive use in AI due to its ability to efficiently handle parallel operations, a common requirement in AI computations.


Imagine you’re coloring in a big picture with lots of small areas, and you have a team to help. If each person colors in one small area at the same time, it’s much quicker than one person doing all the work alone. A GPU is like a team of very fast “colorers” in a computer, which makes them great for AI tasks that involve lots of these small, simultaneous coloring tasks, like processing data or making predictions.

In-depth explanation

The Graphical Processing Unit or GPU is a specialized form of microprocessor with thousands of small cores designed for simultaneous processing, a feature that makes them highly effective for rendering graphics. The image processing in games or 3D applications, for example, requires many small, similar operations to be performed at once - a process known as parallel computing, which GPUs are uniquely equipped to handle.

However, it turned out that the same properties that make GPUs adept at image rendering make them excellent for the matrix operations and vector calculations commonly found in AI and Machine Learning algorithms. These algorithms often involve handling large volumes of data where the same operation has to be performed on each data point.

Neural networks, for instance, require a multitude of operations to calculate the weights and biases of each node during each iteration of training. Linear algebra operations, convolutions for image processing, Fourier transforms for signal processing - many of these tasks benefit from a parallelized approach.

GPUs have a larger number of Arithmetic Logic Units (ALUs, the building blocks of calculations) than regular CPUs, enabling them to perform more operations per clock cycle. Essentially, while a CPU may have 4, 8, or 16 cores capable of executing series of instructions, a GPU has hundreds or even thousands of smaller cores, allowing for heightened parallelism.

A downside of using a GPU for AI calculations is that they require a different programming model than CPUs. While standard programming languages can be used, to fully exploit the GPU’s processing power, a specialized approach to programming typically used in graphic rendering is necessary. CUDA and OpenCL are examples of such programming frameworks.

Another point of consideration is that GPUs are power-hungry due to their high-performance characteristics, leading to higher electricity consumption and possible thermal challenges in data centers.

However, the significant increase in computational speed provided by GPUs often justifies the power costs for many AI applications - leading to their widespread adoption in the domain of AI.

Parallel Computing, CUDA, OpenCL, Deep Learning, Neural Networks, Tensor, Matrix Multiplication, High Performance Computing, Convolutional Neural Network (CNN), (CNN).