Did you know that in the past decade performance for computers has increased by 1000x? What exactly caused this kickstart in performance? The secret lies in huge advances within accelerated computing.
Accelerated computing can be thought of as jet fuel for computer performance. And, it’s changing the future of multiple industries. But what exactly is this form of computing? And how did it rise to change the face of the tech industry?
If you want to learn the answer to these questions, and more, then you’re in the right place. In this article, we’ll teach you everything you need to know about accelerated computing.
That way, you can start taking advantage of this huge performance increase. Let’s get started!
Table of Contents
What is Accelerated Computing?
Before we begin it’s important to first understand exactly what accelerated computing is. Accelerated computing is known as a computing model. It carries out complex calculations that can be applied to a variety of engineering and scientific applications.
These applications are carried out on processors that are known as accelerators. These accelerators work together with a computer’s CPU (Central Processing Unit) to produce execution times that work faster than time as we know it.
So, how do accelerators reach these indescribably fast execution times? Essentially, the computer offloads any computations that require intensive calculations onto the accelerator.
The accelerator then performs these computations while the CPU simultaneously runs the remainder of the code. This process, which is known as data parallelism, allows the computer to have greatly reduced execution times.
As we will see, accelerated computing first began on the PC. However, these days you can find it in everything from our smartphones to cloud services. Many companies are now utilizing it to transform the way their business handles data.
How PCs Caused the Rise of Accelerated Computing
In the past, personal computers relied entirely on the CPU to handle all of their application needs. This included things like security, software-defined I/O, operating systems, application control flow, and application business logic.
Sadly, this was a lot to ask of the CPU in a PC. That’s why accelerated computers began to combine different types of processors into the CPU. This combination became known as heterogeneous computing.
The first practical application of this was found in PCs from the 1980s. Co-processors divided the calculations to improve the acceleration of the CPU. However, it was the video game industry in the 1990s that began to introduce different types of processors.
Notably, the GPU (or (Graphics Processing Units). These units helped keep up with the intense graphics processing power that was required from video games. However, it wasn’t until 1993 that NVIDIA created GeForce 256.
This was the first chip that offloaded key tasks from the CPU. And, with this, accelerated computing as we know it today was born. These days, the most popular types of accelerators are GPUs and DPUs (Data Processing Units).
From here, the work is divided between the CPU, GPU, and DPU. The CPU handles things like the operating system and the application control flow. The GPU, on the other hand, tackles the application business logic.
And, the DPU worries about the security and software-defined I/O. This, in turn, creates a much more balanced system that works together instead of against each other.
It’s part of what makes the new 11th generation Intel Core mobile processors so efficient. Make sure to get the facts here if you want to learn more about it.
The Role of CUDA in Accelerated Computing
By the 2000s GPUs were in full swing. However, some innovative researchers were discovering ways to bring the power of the GPU to tasks that were beyond the capabilities of the CPU.
This culminated with the development of CUDA. CUDA is a programming model and computing platform that was started by Ian Buck in 2006. With this type of platform, the CPU is responsible for running the sequential workload.
To optimize this the CPU runs them through single-threaded performance. Meanwhile, the intensive portion of the application is GPU cores simultaneously. This allows developers to harness the full power of the GPU.
The Role of InfiniBand in Accelerated Computing
With the rise of CUDA, it quickly became apparent that something would be needed to create a large distributed network of GPUs. That’s where InfiniBand came into the picture. InfiniBand is an in-network computing platform.
It’s fully off loadable which means that it can provide incredibly high performance. This performance is needed for things like simulations that require high resolution. It’s also helpful for dealing with algorithms that are highly parallelized and huge data sets.
As we will see, this accelerated network opened up the way for applying accelerated computing to concepts like artificial intelligence, cloud infrastructure, and high-performance computing (HPC). What’s
How Accelerated Computing Can Optimize AI Technology
In 2012 the world was first introduced to the computing form known as AI. While this technology had promising applications there were some issues. Notably, I/O bottlenecks prevented AI from effectively communicating with the rest of the processors.
However, as researchers dived into this issue they quickly realized that GPUs and other types of accelerators were the secrets to creating faster AI insights, as well as more accurate models.
This led to many practical applications of AI. Here are some of the major ones.
According to a recent study, cybercrime costs the global economy roughly $600 billion every year. To put that in perspective, that’s .08% of the worldwide GOP. Of the financial institutions, credit cards and banks are the most vulnerable.
That’s why companies like American Express began implementing fraud algorithms that were monitored by AI. But, to be effective this algorithm would need to monitor every transaction made in real-time.
Luckily, the power of accelerated computing allows us to detect potentially fraudulent actions within mere milliseconds.
In the past, we had to search out for products that we wanted. However, these days the products that we won’t seem to find us. This is thanks to recommender systems that personalize what we want to see on the internet.
But, how exactly is this possible? Well, it’s through the power of AI. AI-run recommender systems can look through millions of people’s data to find patterns and trends.
This, in turn, allows them to recommend products that are personally tailored to our interests which drives up sales. But, combing through so much data requires a lot of power. Luckily, accelerated computing is strong enough to support it.
If you’ve visited a website recently, then you might be familiar with a little chat window pop-up that offers to put you in touch with customer service. But, it’s not a human representative speaking to you. It’s an AI system.
So, how is it that AI can handle the complex network that is the human conversation? You guessed it: accelerated computing. You see, to sound human-like this system must be familiar with statements that build on top of each other for infinite combinations.
And, only the high performance of accelerated computing can deliver the power to make these conversation cue points in a millisecond. This, in turn, leads to improved customer service interactions
Accelerated Computing May Hold the Key to a More Energy Efficient Future
Anyone familiar with the concept of Bitcoin mining knows that computing is a serious energy drain. As such, it’s putting a dramatic strain on our nonrenewable resources.
Since this model isn’t sustainable for the environment or the economy, it’s forced researchers to turn to other methods of computing. They quickly discovered that accelerated computing can be much more energy-efficient than the traditional CPU.
Specifically, they found that when a GPU was run on an artificial intelligence inference it ran at 42x the energy efficiency of a CPU. It doesn’t take a huge imagination to see that if this was run on a large scale it could save tons of energy.
One researcher found that if all AI servers currently run on a CPU switched to a GPU, then it would save at least ten trillion watt-hours of energy each year.
While that number can be hard to wrap your head around, it’s enough energy to power 1.4 million homes every year. As such, we can expect accelerated computing to stay around as we move forward toward a more energy-efficient future.
Want More Content? Keep Reading
We hope this article helped you learn more about accelerated computing. As you can see, this computing model is likely here to stay. It might hold the secret to overcoming tech issues like semiconductor shortages.
As such, researchers should make sure to familiarize themselves with accelerated coding. It’s important to understand both the history of computers, as well as their future.
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