Why GPUs for AI

While CPUs excel at handling a wide range of general-purpose tasks, from running operating systems to executing complex applications, their architecture is not inherently optimized for the parallel processing demands of AI workloads. This is where GPUs come into play. GPUs are specifically designed to handle massive parallel computations, making them ideal for AI tasks such as image recognition, natural language processing, and deep learning.

CPUs typically have a limited number of powerful cores, ranging from two to sixty-four, that execute instructions sequentially, one after the other. In contrast, GPUs boast thousands of smaller, simpler cores that can perform many simpler tasks simultaneously. This parallel processing capability allows GPUs to handle the massive matrix operations that are fundamental to AI algorithms much faster than CPUs. For example, in a task like cracking a password hash, a GPU would divide each instruction into separate threads that it can then compute in parallel in its kernels.

However, it's important to remember that CPUs still play a crucial role in AI systems. They are responsible for tasks such as data preprocessing, operating system management, and handling sequential operations that are not well-suited for parallel processing. In essence, CPUs and GPUs work together in a synergistic manner to achieve optimal performance in AI workloads. Another key difference between CPUs and GPUs lies in their memory access patterns. CPUs are optimized for low latency memory access, meaning they prioritize minimizing the time it takes to retrieve data from memory. They achieve this through a complex hierarchy of cache memory. GPUs, on the other hand, are more tolerant of memory latency. They have fewer and smaller cache layers because they prioritize maximizing throughput, or the amount of data they can process in a given time. This difference in design reflects their respective strengths: CPUs excel at handling diverse computing tasks with high precision, while GPUs are optimized for massively parallel workloads where high throughput is paramount.

Parallelism

AI models, particularly deep learning networks, rely on matrix multiplications and tensor operations. GPUs are designed to perform these computations in parallel across thousands of cores, significantly speeding up training and inference times.

Key Differences Between CPUs and GPUs:

Scalability

Modern GPUs, such as the NVIDIA A100 and H100, are built with scalability in mind, supporting:

• Multi-GPU setups using NVLink.
• Multi-node systems with interconnects like InfiniBand.

Power Efficiency

GPUs are not only faster but also more power-efficient for specific workloads compared to CPUs. This efficiency is critical in large-scale data centers where energy consumption is a key concern.

Software Ecosystem

The GPU ecosystem includes powerful frameworks and libraries, such as:

• CUDA: NVIDIA’s parallel computing platform.
• cuDNN: Optimized libraries for deep neural networks.
• NCCL: For multi-GPU communication.

Leveraging these tools, GPUs have become indispensable for AI research and production environments.