Upon first hearing the word graphical processing unit (GPU), most people tend to associate it with gaming.
While that was true years ago, GPUs are increasingly becoming the dominant force in the world of machine learning.
GPUs are empowering data scientists to train their models in minutes which could potentially take days or weeks. While CPUs perform operations in a sequential manner, GPUs leverage parallel computing where they have hundreds and thousands of cores and each core is dedicated towards a single task. You will often hear the analogy comparing CPU to a truck and GPU to a Ferrari transporting goods from point A to point B. A truck can carry more load but its speed is slow whereas a Ferrari can only carry a few things at a time but is quite fast in terms of its speed.
At its core, data science involves millions of matrix multiplication to train a model. The speed of this process can be greatly increased if the computations can be performed in parallel. This is especially true when it comes to deep learning since deep neural networks have millions of parameters to train. Since the GPU can run multiple processes at the same time, it is quite effective in training neural networks that encompass computationally intensive matrix multiplications.
With a GPU, you get higher bandwidth to hold large datasets. This is made possible since GPUs have a dedicated video RAM. With parallel GPUs, you can process those huge datasets much faster. The larger the size of your data, the greater the benefit you will reap from your GPU. However, a major drawback of GPUs is they are not as efficient as CPUs when it comes to optimization of long-running individual tasks.
The answer to this depends on a few factors. Let’s look at them one by one:
Do you have extremely large datasets that take a lot of time to train? In that case, GPU makes perfect sense. However, if you are only dealing with small datasets that take a few minutes to train, then investing in GPU might not be a wise decision
Are you building simple linear models or deep neural networks with millions of model parameters to learn? GPU lies at the heart of Deep Learning and Big Data and is a no-brainer for complex and extensive matrix multiplications involved in training deep neural networks.
GPUs are much more expensive than CPUs and that is expected since they have more cores and, hence, have higher throughput. You have to determine if the performance gains outweigh the costs. Generally, GPUs can cost 2-3x more than CPU and, therefore, you might want to see 2-3x performance gains as well. However, with the cloud, you can now pay for GPUs only when you use them so it does not require a large capital investment in hardware.
GPUs are paving the way for the latest advancements in Artificial Intelligence. The barrier to entry for learning and utilizing GPUs is getting lower. With cloud solutions readily available, you can pay only for the time you wish to use a GPU to accelerate your model training, making cost no longer a pressing concern. Due to their ability to parallelize tasks, GPUs are much better suited for accelerating the deep learning training process and enhancing overall productivity.
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