For those who are familiar with the technologies, the difference between CPU and GPU is relatively simple. However, to better understand the differences, we must enumerate them to appreciate their applications fully. Generally, GPUs are used to take on added functions to what the CPUs already execute. In reality, though, often, it is the GPU that is the driving force behind machine learning and Artificial Intelligence. Let us now look at the core differences between CPU vs GPU in machine learning.
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CPU vs GPU
CPU stands for Central Processing Unit. It functions much like the human brain does in our bodies. It takes the form of a microchip which is placed on the motherboard. It receives data, executes commands, and processes information that other computers, devices, and software components send. In how they are created, CPUs are best for sequential processing and scalar processing, which allows multiple different operations on the same data set.
GPU is short for Graphics Processing Unit. In most computer models, the GPU is integrated into the CPU. Its role is to take care of processes that the CPU cannot i.e., intense graphics processing. While the CPU can only execute a limited number of commands, GPU can manage thousands of commands in parallel. This happens because it is processing the same operation on multiple sets of data. GPUs are built on Single Instruction Multiple Data (SIMD) architecture, and they employ vector processing to arrange inputs into data-streams so that all of them can be processed at once.
Thus, having established the core difference between CPU and GPU, we have learned that they process different data pieces, and now we can look at CPU vs GPU in machine learning. While CPUs can handle graphic functions, GPUs are ideal for them as they are optimized for the required fast-paced computation. For the rendering of 3D figures in games, GPUs were primarily used till very recently. However, due to new research into them, the application area has significantly broadened.
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The Application of Graphics in Machine Learning
Machine learning and artificial intelligence often invoke images from science fiction in us. We dream of the robots of Terminator or the supercomputers of Asimov. However, the reality is slightly more prosaic. It involves things like business intelligence and analytics shortcuts. They are in the line of steady progression that began from supercomputers like Deep Blue. Deep Blue was a computer that beat Gary Kasparov, the then chess champion. It was called a supercomputer because it had 75 teraflops of processing power, which took up the equivalent of several racks over a large floorspace.
Today, a graphics card holds around 70 teraflops of processing power. When used on a computer, it uses 2000-3000 cores. By way of comparison, this single GPU chip can handle up to 1000 times more data than a traditional CPU chip.
It is also important to note that CPUs and GPUs add to our existing capabilities. We could do all the functions they do without having to resort to them. But the benefit they bring is that they make everything easier and faster. Think about physical mail versus actual mail. Both can be done, but the latter is undoubtedly quicker and easier. Therefore, machine learning is nothing but doing the same work we are doing but in an augmented setting. Machines can do tasks and calculations over a matter of days that would take us a lifetime or more otherwise.
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Machine Learning Cases Concerning GPUs
Machine learning borrows heavily from the Darwinian Theory of Evolution. It takes into account any analysis on big data what the previously leanest and fastest solution was. It saves this iteration for future analysis. As an example, a local business wants to analyze a data set for local customers. When it begins the first set, it will not know what any of the data means. But based on the continued purchases, each simulation can be compared to keep the best and discard the rest.
Online sites such as Google and YouTube use this feature often. It takes historical data and creates a trend based on that for recommended pages and videos. For example, if you watch a “cute cat video”, the machine has learned from the experience of site patterns and user behavior what it should recommend next to you. Similarly, once you establish your trends based on continuous usage, that also gets factored into what they learn. This same principle is at work on e-commerce sites like Amazon and Facebook. If you search for football-related products, the next ads you will see are similar in nature to it.
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Choosing the correct GPU
GPUs, as we have established, work better for machine learning. But even when selecting a GPU, we must choose the best option available for our needs. The determinant factor when selecting a GPU is primarily on the type of calculations that need to be done. There are two types of precision calculations a GPU can do depending on the number of places it can make calculations up to. These are known as Single Floating Point and Dual Floating Point precision types.
Single Precision Floating Points take up 32 bits of computer memory compared to the Dual Precision Floating Points, which occupy 64 bits. Intuitively, it shows that the Dual Precision Floating Points can undertake more complex calculations and therefore have an increased range. However, because of the same reason, they require a higher grade of a card to run, and they also take more time because often, the data being computed is based on higher-level mathematics.
If you are not a developer yourself, one should reconsider before going in for these high-end technologies. No one size fits all requirements. Each computer needs to be customized based on the data set that needs to be analyzed. Furthermore, hardware requirements like power and cooling are also important considerations and can use up between 200-300 watts. Sufficient cooling racks and air-coolers need to be present to balance the heat generated because the heat can end up affecting your other devices.
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