Importance of GPU in Training Neural Networks

Introduction

When we refer to the explosive field of Artificial Intelligence (AI) and deep learning, one component cannot be overlooked: the GPU, or Graphics Processing Unit.

Essentially, neural networks are inspired by the human brain and are composed of an interconnected series of artificial neurons. Each neuron performs simple mathematical operations, such as multiplication or addition. The real power emerges when these neurons are interconnected in layers and work together to produce complex results.

Training a neural network involves meticulously adjusting the weights of these neurons to optimize the network’s accuracy in performing a specific task. Thanks to GPUs, what once took weeks can now be accomplished in days or even hours. Compared to CPUs, using a GPU can accelerate the training of a neural network by up to 10 times.

The Rise of GPUs

Historically, GPUs were the go-to engine for gaming enthusiasts, centered around accelerating graphic rendering. However, around 2007/2008, thanks to the emergence of NVIDIA’s CUDA architecture, GPUs underwent a makeover. They began to be used in high-performance computing and data science, dramatically expanding their applications.

Today, GPUs are essential for accelerating numerically intensive tasks. While they still operate alongside CPUs, they handle the bulk of the heavy mathematical workload involved in training neural networks.

GPU vs CPU: A Direct Comparison

To fully understand the revolution that GPUs brought, let’s compare them to CPUs.

🖥 CPU: It’s the brain of the computer. It excels at executing complex sequential tasks. However, in the world of neural network training, where parallelism and simple operations are key, CPUs have some limitations:

• They operate in a serial approach, processing operations one after another.
• Slow memory access compared to computation speed can create bottlenecks.
• Current CPUs have at most a few dozen cores, limiting their parallel capacity.

🎮 GPU: Here lies the real strength for deep learning:

• GPUs are designed for parallelism, able to process thousands of tasks simultaneously.
• They have thousands of cores, specifically ALUs, optimized for mathematical calculations.
• Although they still need to access shared memory, GPUs are designed to maximize the efficiency of these accesses.

The image below clearly illustrates how a GPU acts as an accelerator for the CPU:

The GPU is highly interdependent with the CPU and they work together to deliver computational power. Some examples of how the GPU depends on the CPU are:

1. Data Loading: Before any operation is carried out by the GPU, it requires specific data to be processed. This data typically resides in the system’s main storage or RAM. The CPU’s role is to read this data from the system and load it into the GPU’s memory, preparing it for the operations to be performed. Without this proper loading, the GPU wouldn’t have the necessary inputs to function.

2. Instruction Provision: Just like the data, the GPU also needs clear instructions on how to process them. The GPU, by itself, doesn’t decide which operations to perform. The CPU is the component that provides these guidelines. It sends specific commands to the GPU, informing it about which calculations or transformations should be applied to the data.

3. Memory Management: The GPU comes equipped with its own memory, often referred to as video memory or VRAM. This memory is optimized for fast graphical operations. However, the allocation and coordination of sending data to this memory largely depend on the CPU. The CPU ensures that the GPU’s memory is used efficiently, avoiding overloads or conflicts.

4. Resource Management: In a computational system, there are multiple components and resources that need to be coordinated and managed. Beyond the GPU, there are other devices, such as hard drives, network interfaces, among others. The CPU acts as a sort of ‘maestro’ in this scenario, ensuring that all components work in harmony. This includes the proper allocation of resources for the GPU, making sure it has everything it needs to function appropriately.

Important Consideration about GPU Efficiency

📌 Data Volume and Parallel Operations: One of the main characteristics of GPUs is their ability to perform numerous operations simultaneously. A GPU’s efficiency is best utilized when it has a large volume of data to process. This is an inherent advantage of its architecture, which is designed to perform many simple and parallel operations at the same time. The idea here is to split a large task into multiple smaller sub-tasks that are executed simultaneously, maximizing the utilization of the GPU’s cores. When faced with a task that is excessively small or that cannot be divided into smaller parts for parallel execution, the GPU might find itself in a position where many of its cores remain idle. This means that many of the GPU’s resources are being underutilized, resulting in decreased efficiency.

📌 Data Transfer and Latency: Another point to consider is the data transfer between the CPU and the GPU. Often, people underestimate the time and resources needed to move data from one component to the other. If the amount of data is relatively small, the time spent transferring these data from the CPU to the GPU and vice versa might end up being disproportionate to the time it would take to process these data. This makes the operation less efficient than if it had been performed solely on the CPU. In short, data transfer latency can overshadow the speed benefits of the GPU, making it less advantageous in certain scenarios.

📌 The Balance Between CPU and GPU: Given the complexity and variety of tasks that computers face today, it’s crucial to know when to use the CPU or the GPU. For tasks with large data volumes that can be processed in parallel, GPUs are an undeniable choice, offering performance far superior to traditional CPUs. However, for tasks that are intrinsically sequential or don’t require significant parallelization, the CPU, with its cores optimized for more complex operations, might be the best option.

Tensor Processing Unit (TPU)

Artificial intelligence (AI) has revolutionized the world of technology, and behind this revolution lies a plethora of specialized hardware that makes it all possible. One of the key components in this equation is the Tensor Processing Unit, or TPU. Understanding what TPUs are, alongside CPUs and GPUs, and when to use each, is crucial for those embarking on their AI journey.

TPUs are a particular type of processor. While the name might remind you of a GPU (Graphics Processing Unit), their purpose and design are quite different. TPUs were specifically designed to accelerate the training and inference of neural networks. The “tensor” nomenclature refers to the data structure frequently used in neural networks, thus becoming the primary focus of this hardware.

You might wonder, “Aren’t GPUs already used for neural network training?” Yes, they are. However, while GPUs were initially designed for graphics and rendering – and later adapted for parallel computations and AI – TPUs were crafted from the get-go with the sole purpose of optimizing AI computations. In many cases, this makes TPUs a more efficient choice for certain neural network training tasks, although they might come at a higher cost.

A unique characteristic of TPUs is that they don’t execute code directly like a CPU or GPU. The code to be run on a TPU needs to be translated or “compiled” into a special format using the Accelerator Linear Algebra Compiler, known as XLA. XLA optimizes the code to ensure it runs as efficiently as possible on the TPU. This means that when working with TPUs, there’s an additional step in preparing your code, but this effort is offset by the superior performance TPUs can offer.

CPU, GPU, or TPU: How to Choose?

The decision about which hardware to use to train a neural network isn’t always straightforward and depends on several factors. Here’s a simplified guide:

CPU:

• When to use: If you’re just starting with neural networks, a CPU might be sufficient for your initial needs. They are ideal for small models, rapid prototyping, or when learning the basics.
• Advantages: Easy access (since most computers come with a CPU) and versatility to run various types of tasks.
• Disadvantages: Not optimized for large-scale parallel computations, making them slower for training considerable-sized neural networks.

GPU:

• When to use: If you already have a foundation in AI and are dealing with medium to large-sized neural networks, a GPU might be the best choice.
• Advantages: Designed for parallel computations, GPUs are significantly faster than CPUs for many AI tasks.
• Disadvantages: While faster than CPUs, they might not be ideal for extremely large-scale models.

TPU:

• When to use: Ideal for advanced AI projects involving large-scale neural networks, such as language models with billions of parameters.
• Advantages: Ultra-optimized for training and inference of neural networks, offering unprecedented efficiency.
• Disadvantages: Higher cost and might require specific code adaptations, like compilation via XLA, to maximize their capabilities.

Conclusion

Artificial Intelligence (AI) represents one of the revolutionary pillars of contemporary technology, and the underlying hardware infrastructure plays a crucial role in this revolution. Within this context, the GPU emerges as a key component, significantly accelerating the training of neural networks, which are complex in nature. From their origin in gaming to their redefinition for AI, GPUs have demonstrated the ability to perform mathematical operations in parallel and on a massive scale, surpassing traditional CPUs in specific tasks. However, the synergy between CPU and GPU is fundamental, as the CPU performs essential functions of management, allocation, and instruction.

The challenge of processing efficiency falls not only on the choice between CPU and GPU but also on the consideration of TPUs. While GPUs evolved from graphic applications, TPUs were conceived from the start with the sole focus on optimizing AI calculations, representing a highly specialized option for intensive tasks.

Finally, the choice between CPU, GPU, and TPU depends on the project’s scope, data volume, and the nature of the problem. CPUs are ideal for startups and prototyping, GPUs for medium to large neural networks, and TPUs for large-scale advanced applications. The AI processing landscape is, therefore, a mosaic of options, and the correct choice of hardware can be decisive for the success and efficiency of a project.

Cite this article

You can cite this article in your academic work.

@article{rodrigues2023gpuAI,
  title={Importance of GPU in Training Neural Networks},
  author={Rodrigues, Thiago Luiz},
  journal={URL https://rodriguesthiago.me/posts/importance_gpu_in_training_neural_networks/},
  year={2023}
}

References

• https://researchcomputing.princeton.edu/support/knowledge-base/gpu-computing
• https://cloud.google.com/tpu/docs/intro-to-tpu?hl=pt-br