Why CPU was not enough: Need for GPU in the picture of AI
Tags: webdev, programming, cpu, gpu
Published: 2024-06-24 04:04:06

The CPUs are the jack-of-all-trades but masters of none regarding parallel processing tasks.

Normal People: GPUs are generally used for video games. rty?

Developers: Do GPUs are generally used in ML? rty?

You all are correct. let's dive deep into GPU's 

GPU's are used in many different use cases
1. **Graphics Rendering** :Rendering high-resolution graphics in video games, simulations, and visual effects. Because nobody wants to play a 3D game that looks like it’s from the ‘90s.

2. **Machine Learning** : Training large neural networks for deep learning applications. CPUs trying to handle this is like bringing a knife to a gunfight.

3. **Scientific Computing**: Performing large-scale simulations and computations in fields like physics, chemistry, and biology. CPU.

4. **Cryptocurrency Mining**: Solving complex cryptographic puzzles in mining cryptocurrencies like Bitcoin. CPUs would need a lifetime supply of energy drinks to keep up.

5. **Data Parallelism** : Processing large datasets where the same operation needs to be applied to many data points simultaneously. Think of it as the CPU trying to clone itself a thousand times – it's not going to happen.


I think a lot of questions when it comes to GPU coming into the picture from the perspective of AI/ML
1. What CPU was not able to handle?
2. How does CPU and GPU work hand in hand?


Before we go deep, let's see some key milestones 

## Key milestones in GPU development
1. **1980s** : Early graphics accelerators were introduced to offload simple drawing tasks from the CPU.

2. **1990s** : The rise of 3D graphics in gaming and professional visualization led to the development of more advanced graphics hardware.

3. **2006** : NVIDIA introduced CUDA (Compute Unified Device Architecture), enabling GPUs to be used for general-purpose computing.

## Why CPU was not enough ? 
The increasing demand for higher-quality graphics in video games and professional applications exposed the CPU's limitations in handling parallel processing tasks. CPUs are optimized for sequential serial processing, which is ideal for a wide range of general-purpose computing tasks. However, they struggle with the highly parallel nature of graphics rendering and the massive computational requirements of AI.


### CPU was not enough in

1. **Parallel Processing** : CPUs typically have a few cores optimized for sequential processing, whereas GPUs have thousands of smaller, efficient cores designed for parallel processing.

2. **Task Specialization** : CPUs are versatile and can handle a wide range of tasks, but this general-purpose nature limits their efficiency in specialized tasks like rendering graphics or performing large-scale matrix operations.

3. **Performance Bottlenecks** : The complex computations required for rendering high-quality graphics and processing large datasets created bottlenecks that CPUs could not efficiently overcome.



## GPU and CPU Coordination

Despite their differences, CPUs and GPUs are designed to work together, complementing each other’s strengths. In a typical computing task, the CPU handles the general-purpose processing and decision-making tasks, while the GPU handles the parallelizable tasks.

### How they coordinate:

- **Task Division** : The CPU offloads specific computationally intensive tasks to the GPU.

- **Data Transfer** : Data is transferred between the CPU and GPU through a high-speed interface such as PCIe (Peripheral Component Interconnect Express).

- **Synchronization** : Both units work in synchronization, with the CPU often preparing data and instructing the GPU on the operations needed, then processing the results.

## GPUs in AI and Large Language Models
In the realm of AI, GPUs have become indispensable. Training AI models, especially large language models (LLMs) like GPT-3 and beyond, involves processing vast amounts of data through complex computations. CPUs simply can't keep up with the demands.

### How GPUs are used in AI:

1. **Massive Parallelism** : AI training involves performing millions of matrix multiplications simultaneously. GPUs, with their thousands of cores, can handle these operations in parallel, significantly speeding up the training process.

2. **High Throughput** : For inference (running the trained model on new data), GPUs provide the high throughput necessary to process large amounts of data quickly.

3. **Energy Efficiency** : Despite their power, GPUs can be more energy-efficient than CPUs for specific tasks, making large-scale AI training more feasible.

4. **Optimized Libraries** : Libraries like TensorFlow and PyTorch are optimized for GPU acceleration, enabling researchers and engineers to leverage GPU power easily.


## Hardware-Based Accelerators: 
1. **TPUs (Tensor Processing Units)** : Developed by Google, TPUs are custom-built application-specific integrated circuits (ASICs) designed to accelerate machine learning workloads. They are highly efficient for training and running AI models.

2. **FPGA (Field-Programmable Gate Arrays)** : These are reconfigurable hardware devices that can be programmed to perform specific computations efficiently. They offer a balance between flexibility and performance, suitable for specialized AI tasks.

These are some of them, this accelerators help us in training the model, which would take years for a CPU to do.

Python is recommended to have a lot of libraries and people use it a lot, internally they use the help of GPU when specified to be used.

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If you reading here, I hope this helped you in some way. 

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