How Does GPU Technology Help In Machine Learning?

Be it a data scientist or any machine learning expert; no one will deny the importance of large datasets for any successful machine learning (or ML) models. As the size of datasets keeps increasing, the performance scaling of ML models tends to hit a peak – before experiencing a performance bottleneck or lag. 

A growing technology within the ML fold is Deep Learning, which has both practical and research applications across multiple domains. Based on the use of artificial neural networks (or ANNs), deep learning is widely used to make accurate predictions from large volumes of data. In short, Deep learning requires a lot of computational power – or the best of hardware configuration. 

One such hardware component is either a traditional and reliable CPU (or central processing unit) or the advanced GPU (or graphic processing unit) technology. Which of these two hardware technologies performs better – when it comes to machine or deep learning models with heavy datasets? First, let us discuss these technologies in detail.

CPU and GPU, an Introduction

Since the 1970s, CPU units – introduced by Intel – have been the brain of any computer or computing device. CPU units have performed these operations without any speed/performance bottlenecks, be it any logical, computational, or input-output operations. Essentially, CPU technology was designed with a single core – to perform a single operation at any given time. 

Gradually, with technological advancement and higher computational demand, we began to see dual (or even multi-) core CPUs designed to perform more than one operation at the same time. Most CPUs today are built using a few cores – and their basic design is still prime for solving a few complex computations—example, machine learning problems that require interpretation or parsing of complex code logic.

Like CPU, GPU is also used in any computer to process instructions. The significant difference being GPUs can work on multiple instructions at a given time, thanks to parallelization. Most GPUs are designed with many processing cores – with clock speeds that are much lower than those of CPUs. However, with its multiple processing, GPUs can parallelize calculations using many threads – thus increasing the rate of calculations that would take longer using the CPU.

GPUs have smaller sizes – but larger numbers of cores consisting of arithmetic logic units (or ALUs), control units, and in-memory cache. In other words, a GPU is a specialized processing unit with dedicated memory for performing floating-point operations – that were conventionally used in graphics processing. GPUs have also been in existence since the 1970s but were mostly restricted for gaming applications. It only gained mainstream popularity after NVIDIA released its GeForce line of GPU server products. 

Thanks to its higher memory bandwidth, GPU is a faster technology as compared to CPU. When you are using large datasets to train your ML model, you need high memory bandwidth from your processor. On the other end, CPUs consume more clock cycles to compute complex tasks because of sequential processing. GPUs are built with a dedicated Video RAM (or VRAM) memory for handling complex tasks – thus leaving the CPU memory for less-intensive tasks.

For instance, on average, CPUs can provide a bandwidth of around 60 GB/s, while the NVIDIA Tesla GPU offers a bandwidth of close to 300 GB/s.

• Size of the datasets

As mentioned before, data model training is resource-intensive and requires a large dataset. This, in turn, requires high computational power and memory allocation – which shifts the balance towards GPU processing. In short, the larger the dataset and computing power, the more advantageous is GPU as compared to CPU.

• Optimization

Task optimization is much easier to perform in CPU cores than in GPU cores. Although they are much lesser in number, CPU cores are more potent than their GPU counterparts. 

Based on its MIMD architecture, CPU cores can work on different instructions. On the other hand, GPU cores are organized within 32 core blocks – and can execute the same instruction in parallel using its SIMD architecture. Additionally, parallelization in extremely dense neural networks is complex in GPU computing.

• Cost factor

On average, GPU-based compute instances cost around two to three times that of CPU-based compute instances. The higher cost is only justified if you are looking for 2-3 times more gains in the performance in GPU data models. For other applications, CPUs are always the better alternative thanks to its lower costs.

You do not need to run your ML instances on your hardware or server with cloud hosting services.

Next, let us look at some of the best GPU for machine learning applications.

Best GPUs for Deep learning

Be it any project, selecting the right GPU for machine learning is essential to support your data project in the long run. NVIDIA GPUs are among the best in the market for machine learning or integrating with other frameworks like TensorFlow or PyTorch. 

Here are some of the best NVIDIA GPUs that can improve the overall performance of your data project:

• NVIDIA Tesla

The NVIDIA Tesla GPU series is best recommended for large-scale AI and ML projects and data centres. Designed for GPU acceleration and tensor operations, the NVIDIA Tesla V100 is one GPU in this series that can be used for deep learning and high-performance computing. 

• NVIDIA DGX

This is the top-of-the-level GPU series used for enterprise-level machine learning projects. Optimized for AI and multiple node scalability, the DGX series offers complete integration with deep learning libraries and NVIDIA solutions. If you are looking for an Ubuntu-hosted GPU, then the DGX-1 is the best choice as it is integrated with Red Hat solutions.

Conclusion

When it is a matter of running high-level machine learning jobs, GPU technology is the best bet for optimum performance.

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