Accelerated Computing: How it Works and Why You Should Adopt it

Accelerated ComputingArtificial intelligence (AI) has recently surged, with several groundbreaking AI models now released. Every important AI innovation is enabled by ingenious hardware. This category of hardware offers what is known as Accelerated Computing (AC). It is a technology that helps machines to learn and process data with utter speed. Accelerated computing is the technology that powers self-driving cars and several other great innovations.

What Is Accelerated Computing?

Accelerated computing at its core is about specialization. Conventional computers have CPUs (Central Processing Unit). They, like you may already know are the brain of a computer system. CPUs are very capable for computation, but they might be limited when dealing with massive amounts of data. This makes it necessary to introduce hardware accelerators for faster computation. These special processors can do specific tasks at a much faster rate than conventional CPUs.

These hardware accelerators include GPUs, TPUs and FPGAs

1. GPUs (Graphics Processing Units): Originally designed for gaming. However, they have become the bedrock of AI. They are good at handling several computations simultaneously. This makes them very useful for machine learning and scientific simulations.
2. TPUs (Tensor Processing Units): Google created TPUs specifically for neural networks. They help in optimizing AI operations like training large language models (LLMs).
3. FPGAs (Field-Programmable Gate Arrays) are highly adaptable models that can be reprogrammed for unique applications. It can be set up for specific tasks, such as financial modeling or real-time video processing.

Instead of relying solely on the CPU, accelerated computing works more efficiently by giving a sizable portion of the workload to these specialized processors. As a result, tasks that would have taken weeks on a traditional computer can now be completed in hours or even minutes.

Why Accelerated Computing is Important

1. It is The Technology Behind Recent AI Breakthroughs

There wouldn’t be AI as it is today without accelerated computing. ChatGPT, self-driving cars or DeepMind’s protein-folding AI are examples of feats that were made possible with accelerated computing. Training a massive AI model like GPT-4 using conventional CPUs could take years. However, using GPUs and TPUs takes only some weeks or a few months.

2. It Saves a Lot of Power and Money

AI is an energy-consuming business. Data centers already consume massive amounts of electricity. Accelerated computing cuts energy use by optimizing tasks for efficiency, which means lower costs and a smaller carbon footprint.

3. It is Fueling Breakthroughs Everywhere

• Healthcare: Accelerated computing has been helping in simulating drug interactions or analyzing MRI scans in real-time.
• Climate Science: Running ultra-detailed weather models to predict storms and climate shifts.
• Autonomous Vehicles: Processing sensor data instantly so cars can make split-second decisions.

Without accelerated computing, these advancements wouldn’t have been able to materialize

How to Access Accelerated Computing Without Owning a Supercomputer

It is exciting to let you know that you don’t have to splash big money on hardware to use accelerated computing. There are several ways through which businesses and individuals are gaining access and staying ahead using it.

Some of these ways include:

1. Rent It in the Cloud

Companies like Amazon (AWS), Google (Cloud), and Microsoft (Azure) let you rent GPU and TPU power by the hour. You can purchase a subscription for accelerated computing if you want to train an AI model or run complex simulations on a cloud server.

For example: A small biotech startup can now run protein-folding simulations in days instead of weeks. And they can achieve this without owning a server.

2: Use the Right Software

Once you have access to any platform offering accelerated computing, the next step is to get the software that can handle accelerated computing. Some of the software adapted for accelerated computing include:

• CUDA (Nvidia’s platform for GPU computing).
• TensorFlow & PyTorch (for AI model training).

These two tools listed above let you harness the full power of GPUs/TPUs with relative ease.

Nvidia at the Forefront of Accelerated Computing

If there’s one company that’s dominated this space, it’s Nvidia. They started as a gaming GPU company, but now their chips are the backbone of AI.

For example:

• ChatGPT is trained on Nvidia GPUs.
• Self-driving cars run on Nvidia’s A100 and H100 chips.
• Data centers are packed with Nvidia hardware for AI workloads.

At the heart of Nvidia’s technology is CUDA, a programming platform that lets developers easily tap into GPU power. Because of this, Nvidia has become the monopoly for accelerated computing for AI and other operations. The company’s explosive growth shows no signs of slowing down.

Accelerated Computing Is the Future

We’re living in an era where computing speed is proportional to competitive advantage. Countries are treating AI hardware like a geopolitical resource, companies are racing to build faster chips, and breakthroughs in science, medicine, and tech are all tied to this revolution.

Whether you’re a developer, a business leader, or just someone curious about tech, one thing’s clear: AC isn’t just changing AI. It is changing the world and the race is just getting started.