Why CPU was not enough: Need for GPU in the picture of AI

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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