ACAD 8- Unveiling the GPU: From Gaming to AI Revolution
I am sure a lot of you or your friends indulge in high-intensity graphic games. I have always been fascinated with the real-time rendering capabilities of computers, especially during the gameplay of classic action games such as Max Payne, Grand Theft Auto, World of Warcraft etc. In a world captivated by the stunning visuals of modern gaming, the unsung hero behind the scenes is the Graphics Processing Unit (GPU). Here comes another dimension of comparing computation capabilities beyond design, storage, CPU, cache, and RAM.
GPUs have transcended their gaming roots to become a cornerstone of technological advancement, particularly in the realm of Artificial Intelligence (AI). At its core, a GPU is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. Unlike their more general-purpose counterparts, the Central Processing Units (CPUs), GPUs excel in handling multiple tasks simultaneously.
Imagine a symphony orchestra, where the CPU is the conductor, overseeing and directing the flow of tasks, while the GPU is the chorus, a group capable of performing multiple tasks simultaneously. The CPU, with its few cores optimized for sequential processing, handles a variety of tasks one after another or a few at a time, directing overall operations, including running the operating system and executing program instructions. The GPU, on the other hand, boasts thousands of smaller, more efficient cores designed for multitasking. GPUs excel in parallel processing, optimized matrix operations, high memory bandwidth, and efficient floating-point calculations. A GPU’s performance can be evaluated in terms of its memory bandwidth, # of cores, memory capacity, and power efficiency. FLOP/s is the standard unit for measuring the computational speed at which a computer can perform mathematical operations involving floating numbers in a second.
When faced with a large, complex problem, the GPU divides it into thousands of smaller tasks and works on them concurrently. This is akin to how a team of workers might tackle the construction of a building, with each worker responsible for a different task, all happening simultaneously. This trait not only makes them ideal for rendering the intricate visuals in video games but also positions them as pivotal players in more computationally intensive tasks beyond gaming. To understand how GPUs achieve this, consider the process of rendering a scene in a video game. The scene is composed of millions of pixels, and the color of each pixel must be calculated based on the lighting, the materials of objects, and the perspective of the viewer. Instead of calculating each pixel's color, shading, and texture one by one, a GPU can do this calculation for many pixels at the same time, dramatically speeding up the process. Similarly, in AI and machine learning, models learn by adjusting their parameters in light of new data. This adjustment process, known as training, involves performing calculations over large datasets. GPUs accelerate this process by performing many calculations in parallel, making it feasible to train complex models in hours or days instead of weeks or months.
GPUs today enable cutting-edge real-world applications such as faster drug discovery, simulating digital twins, mining digital cryptocurrency, and LLM training. As GPU technology continues to evolve, we can expect to see even more specialized processors designed to tackle specific tasks, both in gaming and in AI. The evolution of GPU capacity has been nothing short of revolutionary. As games have become more graphically demanding, GPUs have grown in power and efficiency, a progression that has inadvertently fueled advancements in AI. The parallel processing power of GPUs, capable of handling thousands of tasks at once, mirrors the complex nature of neural networks in AI, making them perfectly suited for deep learning tasks. Our beloved GPT LLM models (behind ChatGPT) are also powered by Nvidia chips.
Nvidia, a name synonymous with GPUs, was founded in Fremont, California in 1993 by Chris Malachowsky,?Curtis Priem, and Jensen Huang. After multiple peaks and troughs on its journey, Nvidia has now emerged as a titan in the AI field, with its recent announcements highlighting significant leaps in GPU technology. To give you some perspective, today, Nvidia's market value briefly surpassed Amazon's at $1.78 trillion, making Nvidia the fourth most valuable US-listed company, driven by the increasing demand for its chips used in AI computing as firms like Microsoft and Google ramp up investments in bulking up their servers with thousands of A100 chips to lead the LLM wave.
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NVIDIA’s GeForce 256, the first GPU in 1999, was a dedicated processor for real-time graphics, an application that demands large amounts of floating-point arithmetic for vertex and fragment shading computations and high memory bandwidth. As real-time graphics advanced, GPUs became programmable. NVIDIA CUDA is the parallel computing platform that enables developers to use GPUs efficiently by leveraging the parallel computing engine in NVIDIA’s GPUs and guiding them to partition their complex problems into smaller, manageable sub-problems. Nvidia’s core focus is chip design while for manufacturing, they heavily rely on a Taiwan Semiconductor Manufacturing Company for all of its chips. Other key players include AMD, with its Radeon line of GPUs, and Intel, which has entered the GPU arena with its Xe graphics and AI-driven chips.
The competition among these giants is fostering a rapid pace of innovation but it can lead to turmoil between these two powerful customer segments - gamers and AI innovators. Quite recently, Nvidia reported a drop in gaming revenue driven by a rise in price for its latest chip series.
As we navigate through the evolution of GPUs from gaming consoles to the complex realms of AI and beyond, it's clear that these powerful processors are shaping the future of technology. From enabling breathtaking gaming experiences to driving advancements in AI, healthcare, and autonomous vehicles, GPUs have become an indispensable part of our digital world. Nvidia's journey, from near bankruptcy to becoming a titan in AI, underscores the transformative impact and endless possibilities GPUs hold for solving some of the world's most complex challenges.
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