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Why GPUs for Machine Learning? Ultimate Guide

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14th Sep, 2023
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Why GPUs for Machine Learning? Ultimate Guide

In the realm of modern technology, the convergence of data and algorithms has paved the way for groundbreaking advancements in artificial intelligence and machine learning. As these fields continue to evolve at a rapid pace, the need for efficient and high-performance hardware becomes paramount. This is where Graphics Processing Units (GPUs) step into the spotlight. 

Originally designed to render graphics and images, GPUs have found a new purpose as indispensable tools for accelerating machine learning tasks. Besides knowing what is the use of graphics card in laptop, decoding its significance in machine learning play an exceptional role in unleashing its power.

Let us take you through the insightful world of machine learning and the significance of GPUs in it. 

What Is Machine Learning and How Does Computer Processing Play a Role?

Machine learning can be described as a subset of Artificial Intelligence AI that studies how algorithms can learn or make predictions based on data without being explicitly trained for every specific task. In machine learning, computers use statistical techniques to improve their performance of a specific task over time as they get exposed to more data. 

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As computing technology continues to advance, it has enabled more complex and sophisticated machine-learning applications across various industries. 

The role of computer processing in machine learning is quite a crucial factor that holds significant importance in this process. Here’s how computer processing assists ML. 

Data Processing

Data is required to train machine learning models to learn patterns and make accurate predictions. These data are processed and analysed by computers to be used in the desired manner during training.

Feature Extraction

Computer processing also extracts relevant features from raw data, enabling the model to understand and learn better. 

Model Training

Model training involves algorithms to adjust the parameters of the model so that it can predict the outcomes more accurately. This process demands intense computation as the computer compares the model’s prediction to the actual outcomes and adjusts its parameters accordingly.

Prediction

Lastly, after the model has been successfully trained, it can be used to make predictions or new data. Computers process the input data through the model to generate predictions. 

Apart from these, computer processing is also required to perform other equally significant tasks. Such include performance evaluation, scaling and efficiency, real-time processing and deep learning, to name a few. 

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What is a Graphics card (GPU)?

Now that you have a clear understanding of the role of computer processing in machine learning, let’s learn what is a GPU and what does a graphics card do?

A GPU, or a Graphics Processing Unit, can be described as hardware specifically focused on accelerating the processing of images and videos on a computer. It makes the computer more powerful, thus enabling it to handle complex or high-level tasks with ease. 

While a GPU is mainly essential for gaming and graphic-intensive tasks, it has found applications across various fields, such as AI, machine learning, cryptocurrency mining, etc. For example, GPUs have become essential in training and executing machine learning models, as it involves handling large databases. 

Modern-day GPUs are available in various specifications and performances that you can opt for depending on the task that you wish to perform.

What Does a Graphics Card Do?

The primary role of a graphics card is to handle the processing of visual data, which includes graphics, images and animations. In the realm of machine learning, the GPU is responsible for enhancing the training and inference processes of machine learning models. 

One of the main reasons GPU has become so increasingly important in machine learning is its parallel processing ability, allowing the opportunity to perform multiple calculations simultaneously. In addition, GPUs also help quickly process and analyse big data, which is so often required in training machine learning models, thus enabling faster data preprocessing and feature extraction.

Most of today’s machine learning frameworks, such as TensorFlow and CUDA, are optimised for GPU acceleration. They allow developers to harness the power of GPUs without implementing low-level optimisations. 

GPUs also perform pixel processing, which is quite a complicated operation that requires quite a lot of processing power for creating intricate textures and multiple layers, ultimately resulting in realistic graphics. 

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How Graphics Processing Units are Changing the Game in Machine Learning

The Graphic Processing Unit has undoubtedly been a game changer in machine learning by providing the computational muscle required to tackle complex tasks. On that note, here are a few ways GPUs have revolutionised machine learning.

Faster Training

Training machine learning models, especially deep neural networks, require multiple mathematical operations. GPUs can execute all these complex tasks in a much faster way. What would take multiple days or even weeks for a traditional CPU can often be completed within hours or sometimes even minutes by a GPU.

Model Complexity

With the help of the computational power of GPUs, researchers can now delve into more complex algorithms and models. This, in turn, allows for significant breakthroughs in areas such as image recognition, medical diagnosis, and more. 

Real-Time Inference

Other than simply training machine learning models, another significant application of GPU can be witnessed in enhancing real-time inference capabilities, where models can make predictions on new data. This is especially crucial for applications like NLP, recommendation systems, and autonomous vehicles. 

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What are the Components of a Graphics Card?

Now that you know what is graphic card is, let’s take a look at some of its different components.

GPU Chip

The GPU, or the Graphic Processing Unit chip, is the heart of the graphics card. It contains hundreds and thousands of cores, each capable of performing calculations in parallel. These cores are thoroughly optimised for handling graphic-related computations such as rendering images and videos. 

Memory

Also referred to as Video RAM or VRAM, it is a type of high-speed memory specially designed to store graphical data and accelerate all graphic-related tasks. Every graphics card must contain sufficient VRAM to ensure smooth graphics performance, especially at high resolutions.

Internal Interface

The internal interface of a graphics card is responsible for connecting it to your motherboard. Contrary to the earlier interface, such as AGP, modern-day graphics cards are equipped with a much faster and more efficient internal interface called PCI Express 2.0 X 16.

Cooling Systems

Since graphics cards carry the potential to run intensive tasks, they tend to generate heat during such operations. To prevent the chances of overheating, every graphics card comes alongside a cooling system, which consists of a heat sink and fan. These components dissipate heat and maintain the GPU’s temperature within safe limits.

Power Connectors

Graphics cards in the mid-to-high price range have power connectors because they require more power than the motherboard can deliver. With the help of these power connectors, the necessary power is supplied to the graphics card.

Apart from the theme, there are also a few other graphics card components. Such include DVI/ HDMI/ VGA ports, voltage regulators, backplates, and LEG lighting.

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GPU Over CPU: Why Choose GPU for Machine Learning?

Using a GPU instead of a CPU (Central Processing Unit) for machine learning holds several significant advantages, mainly due to these processors’ architectural and design differences. 

For example, GPUs are designed to handle massively parallel processing tasks, making them ideal for machine learning operations that typically involve processing large amounts of data simultaneously. However, there are several use cases in machine learning wherein opting for a CPU can be cost-effective. This includes performing tasks that do not require parallel computing, such as time series data.

In the realm of neural networks, which form the basis of deep learning, GPUs have been known to be a better choice than CPUs. This is because neural networks usually work with massive amounts of data, which becomes much easier to handle with GPU. CPUs can be less efficient in these cases since they tend to be more efficient when working with smaller-scale neural networks.

Lastly, when it comes to deep learning, GPU is considered the ideal choice for users. Deep learning is a subset of machine learning that relies heavily on neural networks with multiple layers. Since CPUs can only process tasks in one order at a time, they tend to be quite time-intensive and often difficult to handle. Conversely, GPUs are particularly suited for training and inference tasks in neural networks as they can perform computation for each neuron in parallel.

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What Should You Look for in a GPU?

When choosing a GPU for machine learning or other tasks that require high-performance computing, there are several factors that you must consider. 

High Memory Bandwidth

High memory bandwidth allows data to move between the GPU’s memory and its processing core much quicker. Therefore, opting for a GPU with high memory bandwidth and VRAM is always advisable, especially when dealing with large datasets.

Tensor Cores

Some modern GPUs also come alongside tensor cores that facilitate the acceleration of certain types of machine learning workloads. Such include matrix multiplications used in deep learning models. Therefore, while selecting a GPU, check whether it offers tensor cores.

Memory 

Memory is another critical factor that you must consider when selecting a GPU. Since the VRAM is essential in storing and processing large datasets, you must always opt for a GPU that offers ample memory to easily carry out deep learning and other memory-intensive tasks. 

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Conclusion

While GPUs are highly beneficial for machine learning tasks, it is worth noting that they are not always a replacement for CPUs. CPUs are still essential for performing tasks involving system management, general-purpose computing, and other tasks not optimised for parallel processing. A combination of CPUs and GPUs can provide a balanced and efficient computing environment for most machine-learning applications. 

If you wish to know more about how to leverage GPUs to strengthen machine learning, do not forget to check out the Advanced Certificate Program in Generative AI, brought to you by upGrad. This 4-month course covers some of the most important topics in this field, such as mitigating risks in AI and generative AI services offered by Azure, enabling you to match pace with field advancements. 

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Pavan Vadapalli

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Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1Why do GPUs work better for machine learning?

One of the main reasons why GPU is a popular choice for machine learning tasks is that it can perform multiple simultaneous operations. This not only helps to speed up all the processes but also enables the distribution of training processes.

2Why is GPU used for AI instead of CPU?

Contrary to CPUs, GPUs have hundreds and thousands of cores, allowing more simultaneous computations. In addition, GPUs also have a much higher memory bandwidth than CPUs, enabling them to access and transfer data quickly.

3What is the function of a graphics card?

The primary function of a graphics card is to produce visual data, such as images, videos, and graphical user interfaces, much quicker. They contain dedicated software and hardware components for performing specific tasks such as image scaling, filtering, colour correction, etc.

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