Why do we need GPU for Deep Learning?

Question

As the question already suggests, I am new to deep learning. I know that the learning process of the model will be slow without GPU. If I am willing to wait, Will it be OK if i use CPU only ?

Answer 1

Many operations which are performed in computing deep learning (and neural networks in general) can be run in parallel, meaning they can be calculated independently then aggregated later. This is, in part, because most of the operations are on vectors.

A typical consumer CPU has between 4 to 8 cores, and hyperthreading allows them to be treated as 8 or 16 respectively. Server CPUs can have between 4 to 24 cores, 8 to 48 threads respectively. Additionally, most modern CPUs have SIMD (single instruction multiple data) extensions which allow them to perform vector operations in parallel on a single thread. Depending on the data type you're working with, an 8 core CPU can perform 8 * 2 * 4 = 64 to 8 * 2 * 8 = 128 vector calculations at once.

Nvidia's new 1080ti has 3584 CUDA cores, which essentially means it can perform 3584 vector calculations at once (hyperthreading and SIMD don't come into play here). That's 56 to 28 times more operations at once than an 8 core CPU. So, whether you're training a single network, or multiples to tune meta-parameters, it will probably be significantly faster on a GPU than a CPU.