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

One key aspect of GPU architecture that makes them suitable for training neural networks is the presence of many small, efficient processing units, known as “cores,” which can work in parallel to perform the numerous calculations required by machine learning algorithms. This parallel processing capability allows GPUs to perform computations much faster than CPUs, which are designed to handle a single task at a time.

In addition to their parallel processing capabilities, GPUs also have fast memory access and high memory bandwidth, which allows them to efficiently load and process large amounts of data. This is important for machine learning applications, which often require large amounts of data to be processed in order to train and evaluate models.

Answer 2

One key aspect of GPU architecture that makes them suitable for training neural networks is the presence of many small, efficient processing units, known as “cores,” which can work in parallel to perform the numerous calculations required by machine learning algorithms. This parallel processing capability allows GPUs to perform computations much faster than CPUs, which are designed to handle a single task at a time.

In addition to their parallel processing capabilities, GPUs also have fast memory access and high memory bandwidth, which allows them to efficiently load and process large amounts of data. This is important for machine learning applications, which often require large amounts of data to be processed in order to train and evaluate models.