The Turning Point of 2007
The year 2007 marked a turning point in the history of computing. NVIDIA launched CUDA (Compute Unified Device Architecture), a platform that redefined the purpose of Graphics Processing Units (GPUs). Originally designed to render graphics in video games and multimedia applications, GPUs were repurposed as general-purpose accelerators, especially useful for Artificial Intelligence (AI) tasks. This change made it possible to leverage their parallel architecture for intensive calculations, radically accelerating AI research.
The Limit of CPUs and the Need for Parallelism
Traditional CPUs, although versatile, are optimized for sequential tasks. Their architecture, based on a few powerful cores, is not suitable for training deep neural networks, which require processing large volumes of data and performing millions of simultaneous operations. Before CUDA, training complex models was slow, expensive, and limited to institutions with access to supercomputers. The arrival of CUDA offered a scalable and accessible solution to this computational bottleneck.
The Parallel Architecture of GPUs
The success of GPUs as AI accelerators lies in their massively parallel architecture. While a CPU may have between 4 and 32 cores, a modern GPU has thousands of CUDA cores, capable of executing operations in parallel. This structure is ideal for tasks like matrix multiplication, which are fundamental to deep learning. CUDA allowed developers to directly access this capability, transforming the GPU into a general computing tool.
CUDA and the GPGPU Paradigm
CUDA was the first widely adopted platform for General-Purpose Computing on GPU (GPGPU). Officially launched in 2007, it offered a complete development environment that included compilers, debugging tools, and optimized libraries. Developers could write code in CUDA C, or use wrappers in languages like Python, Julia, or Fortran. The execution model is based on threads, blocks, and grids, organized into warps of 32 threads that execute instructions synchronously, maximizing computational efficiency.
Matrix Multiplication: The Engine of Deep Learning
Matrix multiplication is the mathematical core of deep learning. GPUs, by their graphic design, were already optimized for this type of operation. CUDA facilitated the implementation of algorithms like backpropagation, drastically reducing training times. This allowed for faster research iterations, testing more complex architectures, and scaling models without the need for expensive infrastructure.
Democratization of Supercomputing
Before CUDA, researchers had to resort to complex techniques such as the indirect use of graphics APIs (OpenGL or DirectX) to perform scientific calculations. CUDA simplified this process, offering a robust and efficient interface. This democratized access to supercomputing, allowing independent researchers and startups to experiment with advanced models without relying on specialized data centers.
The Rise of Deep Learning
The impact of CUDA became evident in 2012 with the success of AlexNet, a convolutional neural network that revolutionized computer vision. Trained on GPUs with CUDA, AlexNet demonstrated that deep learning could far surpass traditional methods. This milestone coincided with the availability of large volumes of data and marked the beginning of a new era in AI, where specialized hardware became a decisive factor.
Comparative Performance: GPUs vs CPUs
The performance difference between GPUs and CPUs in AI tasks is abysmal. For example, in operations like the cosine similarity between vectors, a modest GPU like the GTX 1650 can be up to 70 times faster than a powerful CPU like the Intel Core i7. This advantage is due to the ability of GPUs to execute thousands of matrix operations in parallel, something that CPUs cannot match by design.
Emergence of Alternatives and Competition
Although CUDA offered clear advantages, its proprietary nature motivated the development of alternatives. In 2009, OpenCL was launched, an open standard for parallel computing on multiple platforms. Later, Google introduced TPUs (Tensor Processing Units), chips specifically designed to run tensors in machine learning models. However, the CUDA ecosystem remained the most robust and widely adopted in the industry.
The Evolution of Hardware: From Graphics to AI
NVIDIA recognized the paradigm shift and transformed into an AI-focused company. In 2017, it introduced the Volta architecture and Tensor Cores, specialized units for mixed-precision operations in deep learning. These cores accelerate both training and inference, consolidating NVIDIA's leadership in the AI data center hardware market.
CUDA as a Pillar of Modern AI
The launch of CUDA in 2007 was not just a technical innovation but the beginning of a revolution. By enabling the use of GPUs for general computing, CUDA became the pillar on which the modern AI explosion was built. Tools like TensorFlow and PyTorch were designed to leverage this architecture, and today they are standards in AI research and development.
A Future Driven by Parallel Computing
Parallel computing on GPUs remains the standard in AI. CUDA continues to evolve, integrating into distributed platforms and the cloud. Its legacy is inescapable: it transformed the GPU into the brain of artificial intelligence, accelerating discoveries, democratizing access, and redefining what is possible in the field of machine learning. The future of AI is, in large part, built on this architecture.