Why use GPU for Deep Learning?

Posted by heyadams on September 16th, 2021

Deep learning is a subset of machine learning, which is the study of computer algorithms that can learn from data. It has applications in self-driving cars, image recognition services like Google image search and Facebook automatic tagging features, weather prediction systems, speech recognition systems, among many others. Best Graphics Cards can help you in deep learning.

So what are GPUs?

GPUs are basically very similar to CPUs (Central Processing Units) but designed specifically for parallel computing. A single GPU has large number of relatively simple computing cores which are executed in parallel on multiple data at once.  It is designed specifically to boost computational efficiency in doing math operations in deep learning tasks by orders of magnitude faster than traditional CPUs .

GPUs have special hardware known as \\" CUDA cores \\", they\\'re co-processors which do the heavy lifting required for deep learning and then feeds back results to the main CPU. GPUs are typically used to speed up computations involving large datasets so as to reduce training times .

However, those who need more complex calculations will need multiple GPUs working together. And given that such computations can take hundreds of hours, this parallelism is very important. Once computed, these functions can be stored in what\\'s called a \\" model \\", which serves as a kind of recipe for running an application. The data we\\'re talking about here is known as \\" input data \\", and that comes from sources like images and video footage or even experimental results from scientific instruments that produce huge volumes of densely packed numbers known as \\" arrays \\". So now you have your data, which you now need to pass through tensor operations using the model. That\\'s where \\"tensor operations\\" come in. This involves running those numbers with different mathematical functions and at different points of time as necessary so as to alter those numbers and give you what you want .

GPUs are particularly good at performing these operations on many data at once, and that\\'s because they\\'re built with thousands of cores programmed for massively parallel operation  and this is why deep learning relies heavily on GPUs and their CUDA cores instead of CPUs

So what does this mean?

It means that if we use a GPU then we become less dependent on having bigger computers with more processing power, but instead we can simply unlock the potential already built-in inside our graphics cards.

So, to sum it up GPUs have a limited number of cores that are specifically designed for parallel computation and deep learning requires you to perform the same operation on many data at once or in parallel. This is where CUDA cores from GPUs shine because they\\'re quick and capable of carrying out such operations much faster than CPUs.

In other words, by using NVIDIA\\'s technology we can provide the world with a computing platform that enables us all to tap into the computational power of these massively parallel processors to solve real-world problems . And if you think about it, what could possibly be more exciting than this? Thank goodness GPUs weren\\'t invented before now - otherwise we\\'d be having a hard time explaining why we need them all the time.

Conclusion:

GPUs are actually quite different from normal CPUs. They have a different architecture and capabilities which makes them very good at performing the operations required for deep learning, while a CPU is more suited to other tasks such as making sure your computer doesn\\'t overheat when you\\'re playing games or editing graphically intensive images.

You can think of GPUs like having several processors in one which allows you to run many computations at once (in parallel) and thus speeds up things like deep learning dramatically.