Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconMachine Learningbreadcumb forward arrow iconCPU vs GPU in Machine Learning? Which is Important

CPU vs GPU in Machine Learning? Which is Important

Last updated:
24th Feb, 2023
Views
Read Time
5 Mins
share image icon
In this article
Chevron in toc
View All
CPU vs GPU in Machine Learning? Which is Important

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.

Enroll for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

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. 

Check out upGrad’s Advanced Certification in DevOps 

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. 

Best Machine Learning and AI Courses Online

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.

In-demand Machine Learning Skills

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.

Popular AI and ML Blogs & Free Courses

At upGrad, our Advanced Certificate in Machine Learning and Deep Learning, offered in collaboration with IIIT-B, is an 8-month course taught by industry experts to give you a real-world idea of how deep learning and machine learning work. In this course, you’ll get a chance to learn important concepts around machine learning, deep learning, computer vision, cloud, neural networks, and more. 

Check out the course page and get yourself enrolled soon! 

 

Profile

Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
Get Free Consultation

Select Coursecaret down icon
Selectcaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Explore Free Courses

Suggested Blogs

What is BFS Algorithm? Breath First Search Algorithm Explained
3319
BFS is a graph traversal technique to explore and analyse graphs. It systematically visits all the neighbouring vertices of a current vertex before mo
Read More

by Pavan Vadapalli

27 Jul 2023

Machine Learning vs Data Analytics: A Brief Comparison
5231
Data is also called the new ‘oil’ of this century. Meaning data is as precious for the functioning of a business in the 21st century as cr
Read More

by Pavan Vadapalli

20 Feb 2023

Maths for Machine Learning Specialisation
5291
Is machine learning possible without maths? Absolutely Not. Machine learning is entirely about maths. It is an application of artificial intelligence
Read More

by Pavan Vadapalli

20 Feb 2023

Machine Learning for Java Developers
5871
Machine Learning in Java: Machine learning has taken over the industry and is rising at a rapid rate. Machine learning gives algorithms a chance to l
Read More

by Pavan Vadapalli

19 Feb 2023

How to Compute Square Roots in Python
11227
A high-level, multi-paradigm programming language with an object-oriented approach, Python is designed to be highly extensible, with multiple compact
Read More

by Pavan Vadapalli

02 Feb 2023

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon