What about GPU Architecture makes them superior for training neural networks over CPUs?

Question

In ML/AI, GPUs are used to train neural networks of various sizes. They are vastly superior to training on CPUs. Why is this?

In: 679

Answer 1

GPUs, or graphics processing units, are specialized computer chips that are designed to handle the complex calculations needed for rendering graphics and video. They are able to perform these calculations much faster than a regular CPU, or central processing unit, which is the main chip in a computer that handles most of its tasks.

One of the things that makes GPUs so good at handling complex calculations is their architecture, or the way that they are built and organized inside the chip. GPUs are designed with many small, simple processors that can work together to perform calculations in parallel, or at the same time. This makes them much faster than CPUs, which usually have just a few larger processors that can only work on one task at a time.

Neural networks are a type of computer program that are designed to learn and make decisions like a human brain. Training a neural network involves running many complex calculations to adjust the parameters of the network so that it can learn to recognize patterns and make predictions. Because GPUs are so good at handling complex calculations, they are much faster at training neural networks than CPUs. This is why GPUs are often used for training neural networks in machine learning and artificial intelligence applications.

Answer 2

GPUs, or graphics processing units, are specialized computer chips that are designed to handle the complex calculations needed for rendering graphics and video. They are able to perform these calculations much faster than a regular CPU, or central processing unit, which is the main chip in a computer that handles most of its tasks.

One of the things that makes GPUs so good at handling complex calculations is their architecture, or the way that they are built and organized inside the chip. GPUs are designed with many small, simple processors that can work together to perform calculations in parallel, or at the same time. This makes them much faster than CPUs, which usually have just a few larger processors that can only work on one task at a time.

Neural networks are a type of computer program that are designed to learn and make decisions like a human brain. Training a neural network involves running many complex calculations to adjust the parameters of the network so that it can learn to recognize patterns and make predictions. Because GPUs are so good at handling complex calculations, they are much faster at training neural networks than CPUs. This is why GPUs are often used for training neural networks in machine learning and artificial intelligence applications.