There have been a lot of queries and concerns regarding the pairing of GPU and Deep Learning recently. This article is an attempt to clear those concerns once and for all. Read More.
There have been a lot of queries and concerns regarding the pairing of GPU and Deep Learning recently. This article is an attempt to clear those concerns once and for all.
In recent years, AI, Machine Learning, and Deep Learning have seen tremendous progress in research, development, and execution. These technologies show a more significant impact on the way society works, sometimes even surprising us with results unheard of.
Deep Learning focuses on training systems to achieve specific results. But, depending on the data to be processed, the duration of training was an issue, especially with conventional CPUs. With GPUs' arrival into the equation, things started moving much faster, producing miraculous results.
Let us see how this change came about and what really is the benefit behind it.
The Hardware Question
It is a common misconception that deep learning requires a lot of heavy hardware. This notion comes from the idea that there are tons of computations necessary for training models and that CPUs with their sequential cycles are slower in handling it.
Well, the first part is correct - the training phase of most deep learning models is very resource-intensive. The neural network takes inputs, processes it in the hidden layers using varied weights, and produces a prediction. This process has to happen over and again - until the model achieves the expected outcome.
These computations are possible via Central Processing Units, aka CPUs. Modern CPUs can handle hundreds of thousands of these computational parameters in minutes or sometimes a couple of hours. But, with advanced deep learning projects, the number of computations is higher. When we say higher, we're not talking hundreds of thousands, not even millions - they are in the billions, even hundreds of billions, for advanced neural networks. To compute such amounts of data, CPUs will need months or even years.
Who has that kind of time in this ultra-fast world, right?
Enter, GPUs.
GPUs and Deep Learning
A Graphical Processing Unit or GPU is a specially designed processor that executes floating-point operations. Primarily designed for rendering graphics, GPUs were later equipped with capabilities of handling mathematical computations. They have a dedicated memory to perform the required functions.
With GPUs, it is possible to run multiple operations simultaneously instead of one after the other. This working mode allows for deep learning models to train considerably faster and frees up CPU cycles to perform other tasks.
GPU vs. CPU in deep learning is a topic often discussed. Let us see what the key differentiating parameters are.
- Even though GPUs have a smaller capacity than CPUs, they have more dedicated logical cores to perform mathematical functions.
- GPUs have a lot more memory bandwidth than CPUs.
- CPUs process tasks sequentially, and thus computations take a lot more cycles.
- Deep Learning training models usually have large datasets - GPUs can process them faster.
- CPU cores may be lesser in number, but they are more powerful than GPUs. Hence, complex optimization is better done in CPUs.
- GPUs are costlier than CPUs. So, unless there is a significant difference in performance, it doesn't make financial sense to choose the former. In conclusion, if a deep learning neural network is small-scale, CPUs are good enough. GPUs are definitely the preferred choice for larger data sets and scales requiring high-performance and a higher number of computations.
High-Performance GPUs With VEXXHOST
If your organization needs deep learning for large amounts of data, then GPUs should be the choice. GPUs can significantly speed up the training phase, which will benefit the data scientists, saving them a lot of time and resources.
At VEXXHOST, we offer you NVIDIA accelerators as the option of enterprise-grade GPUs when utilizing our OpenStack-based Private Cloud service. Please find out more about our enterprise-grade GPUs, deploy a fully-equipped cloud with VEXXHOST, and achieve your goals with deep learning. Contact us with your requirements and questions.
