Why CPU Knowledge Is No Longer Enough

In today's AI age, the majority of developers train in the CPU way. This knowledge has been part of our academics as well, so it's obvious to think and problem-solve in a CPU-oriented way.

However, the problem with CPUs is that they rely on a sequential architecture. In today's world, where we are dependent on numerous parallel tasks, CPUs are unable to work well in these scenarios.

Some problems faced by developers include:

Executing Parallel Tasks

CPUs traditionally operate linearly, executing one instruction at a time. This limitation stems from the fact that CPUs typically feature a few powerful cores optimized for single-threaded performance.

When faced with multiple tasks, a CPU allocates its resources to address each task one after the other, leading to a sequential execution of instructions. This approach becomes inefficient in scenarios where numerous tasks need simultaneous attention.

While we make efforts to enhance CPU performance through techniques like multi-threading, the fundamental design philosophy of CPUs prioritizes sequential execution.

Running AI Models Efficiently

AI models, employing advanced architectures like transformers, leverage parallel processing to enhance performance. Unlike older recurrent neural networks (RNNs) that operate sequentially, modern transformers such as GPT can concurrently process multiple words, increasing efficiency and capability in training. Because when we train in parallel, it will result in bigger models, and bigger models will yield better outputs.

The concept of parallelism extends beyond natural language processing to other domains like image recognition. For instance, AlexNet, an architecture in image recognition, demonstrates the power of parallel processing by processing different parts of an image simultaneously, allowing for accurate pattern identification.

However, CPUs, designed with a focus on single-threaded performance, struggle to fully exploit parallel processing potential. They face difficulties efficiently distributing and executing the numerous parallel computations required for intricate AI models.

As a result, the development of GPUs has become prevalent to address the specific needs of parallel processing in AI applications, unlocking higher efficiency and faster computation.

How GPU Driven Development Solves These Issues

Massive Parallelism With GPU Cores

Engineers design GPUs with smaller, highly specialized cores compared to the larger, more powerful cores found in CPUs. This architecture allows GPUs to execute a multitude of parallel tasks simultaneously.

The high number of cores in a GPU are well-suited for workloads depending on parallelism, such as graphics rendering and complex mathematical computations.

We will soon demonstrate how using GPU parallelism can reduce the time taken for complex tasks.

Parallelism Used In AI Models

AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.

We will show a demo soon how to train a neural network using the power of the GPU.

Continue reading the full article https://journal.hexmos.com/gpu-survival-toolkit/