Accelerated computing is humming in the background, making life better even on a quiet night at home.
It prevents credit card fraud when you buy a movie to stream. It recommends a dinner you might like and arranges a fast delivery. Maybe it even helped the movie's director win an Oscar for stunning visuals.
So, What Is Accelerated Computing? Accelerated computing is the use of specialized hardware to dramatically speed up work, often with parallel processing that bundles frequently occurring tasks. It offloads demanding work that can bog down CPUs, processors that typically execute tasks in serial fashion.
Born in the PC, accelerated computing came of age in supercomputers. It lives today in your smartphone and every cloud service. And now companies of every stripe are adopting it to transform their businesses with data.
Accelerated computers blend CPUs and other kinds of processors together as equals in an architecture sometimes called heterogeneous computing.
Accelerated Computers: A Look Under the Hood GPUs are the most widely used accelerators. Data processing units (DPUs) are a rapidly emerging class that enable enhanced, accelerated networking. Each has a role to play along with the host CPU to create a unified, balanced system.
An accelerated computer offers lower overall costs and higher performance and energy efficiency than a CPU-only system. Both commercial and technical systems today embrace accelerated computing to handle jobs such as machine learning, data analytics, simulations and visualizations. It's a modern style of computing that delivers high performance and energy efficiency.
How PCs Made Accelerated Computing Popular Specialized hardware called co-processors have long appeared in computers to accelerate the work of a host CPU. They first gained prominence around 1980 with floating-point processors that added advanced math capabilities to the PC.
Over the next decade, the rise of video games and graphical user interfaces created demand for graphics accelerators. By 1993, nearly 50 companies were making graphics chips or cards.
In 1999, NVIDIA launched the GeForce 256, the first chip to offload from the CPU key tasks for rendering 3D images. It was also the first to use four graphics pipelines for parallel processing.
NVIDIA called it a graphics processing unit (GPU), putting a stake in the ground for a new category of computer accelerators.
How Researchers Harnessed Parallel Processing By 2006, NVIDIA had shipped 500 million GPUs. It led a field of just three graphics vendors and saw the next big thing on the horizon.
Some researchers were already developing their own code to apply the power of GPUs to tasks beyond the reach of CPUs. For example, a team at Stanford led by Ian Buck unveiled Brook, the first widely adopted programming model to extend the popular C language for parallel processing.
Buck started at NVIDIA as an intern and now serves as vice president of accelerated computing. In 2006, he led the launch of CUDA, a programming model to harness the parallel-processing engines inside the GPU for any task.
Teamed with a G80 processor in 2007, CUDA powered a new line of NVIDIA GPUs that brought accelerated computing to an expanding array of industrial and scientific applications.
HPC + GPUs = Accelerated Science This family of GPUs destined for the data center expanded on a regular cadence with a succession of new architectures named after innovators - Tesla, Fermi, Kepler, Maxwell, Pascal, Volta, Turing, Ampere.
Like the graphics accelerators of the 1990s, these new GPUs faced many rivals, including novel parallel processors such as the transputer from Inmos.
But only the GPU survived because the others had no software ecosystem and that was their death knell, said Kevin Krewell, an analyst at Tirias Research.
Experts in high performance computing around the world built accelerated HPC systems with GPUs to pioneer science. Their work today spans fields from the astrophysics of black holes to genome sequencing and beyond.
Indeed, Oak Ridge National Lab even published a guide to accelerated computing for HPC users.
InfiniBand Revs Accelerated Networks Many of these supercomputers use InfiniBand, a fast, low-latency link ideal for creating large, distributed networks of GPUs. Seeing the importance of accelerated networking, NVIDIA acquired Mellanox, a pioneer of InfiniBand, in April 2020.
Just six months later, NVIDIA announced its first DPU, a data processor that defines a new level of security, storage and network acceleration. A roadmap of BlueField DPUs is already gaining traction in supercomputers, cloud services, OEM systems and third-party software.
By June 2021, 342 of the TOP500 fastest supercomputers in the world were using NVIDIA technologies, including 70 percent of all new systems and eight of the top 10.
NVIDIA's Selene is an accelerated computer that ranks No. 6 on the June 2021 TOP500 list. To date, the CUDA ecosystem has spawned more than 700 accelerated applications, tackling grand challenges like drug discovery, disaster response and even plans for missions to Mars.
Meanwhile, accelerated computing also enabled the next big leap in graphics. In 2018, NVIDIA's Turing architecture powered GeForce RTX GPUs, the first to deliver ray tracing, a holy grail of visual technology, giving games and simulations lifelike realism.
AI, a Killer App for Accelerated Computing In 2012, the tech world heard a Big Bang, signaling a new and powerful form of computing had arrived, AI.
Under the covers, it's a parallel processing job. So, from the early days of deep learning, researchers and cloud-service providers started using GPU-accelerated computers to train and run their neural networks.
Leading companies across every vertical market quickly saw the significance of AI on accelerated computers.
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Accelerated computing is humming in the background, making life better even on a quiet night at home.
