Sustainable Strides: How AI and Accelerated Computing Are Driving Energy Efficiency

AI and accelerated computing - twin engines NVIDIA continuously improves - are delivering energy efficiency for many industries.

It's progress the wider community is starting to acknowledge.

Even if the predictions that data centers will soon account for 4% of global energy consumption become a reality, AI is having a major impact on reducing the remaining 96% of energy consumption, said a report from Lisbon Council Research, a nonprofit formed in 2003 that studies economic and social issues.

The article from the Brussels-based research group is among a handful of big-picture AI policy studies starting to emerge. It uses Italy's Leonardo supercomputer, accelerated with nearly 14,000 NVIDIA GPUs, as an example of a system advancing work in fields from automobile design and drug discovery to weather forecasting.

Energy-efficiency gains over time for the most efficient supercomputer on the TOP500 list. Source: TOP500.org Why Accelerated Computing Is Sustainable Computing Accelerated computing uses the parallel processing of NVIDIA GPUs to do more work in less time. As a result, it consumes less energy than general-purpose servers that employ CPUs built to handle one task at a time.

That's why accelerated computing is sustainable computing.

Accelerated systems use parallel processing on GPUs to do more work in less time, consuming less energy than CPUs. The gains are even greater when accelerated systems apply AI, an inherently parallel form of computing that's the most transformative technology of our time.

When it comes to frontier applications like machine learning or deep learning, the performance of GPUs is an order of magnitude better than that of CPUs, the report said.

By transitioning from CPU-only operations to GPU-accelerated systems, HPC and AI workloads can save over 40 terawatt-hours of energy annually, equivalent to the electricity needs of nearly 5 million U.S. homes.

NVIDIA offers a combination of GPUs, CPUs, and DPUs tailored to maximize energy efficiency with accelerated computing. User Experiences With Accelerated AI Users worldwide are documenting energy-efficiency gains with AI and accelerated computing.

In financial services, Murex - a Paris-based company with a trading and risk-management platform used daily by more than 60,000 people - tested the NVIDIA Grace Hopper Superchip. On its workloads, the CPU-GPU combo delivered a 4x reduction in energy consumption and a 7x reduction in time to completion compared with CPU-only systems (see chart below).

On risk calculations, Grace is not only the fastest processor, but also far more power-efficient, making green IT a reality in the trading world, said Pierre Spatz, head of quantitative research at Murex.

In manufacturing, Taiwan-based Wistron built a digital copy of a room where NVIDIA DGX systems undergo thermal stress tests to improve operations at the site. It used NVIDIA Omniverse, a platform for industrial digitization, with a surrogate model, a version of AI that emulates simulations.

The digital twin, linked to thousands of networked sensors, enabled Wistron to increase the facility's overall energy efficiency by up to 10%. That amounts to reducing electricity consumption by 120,000 kWh per year and carbon emissions by a whopping 60,000 kilograms.

Up to 80% Fewer Carbon Emissions The RAPIDS Accelerator for Apache Spark can reduce the carbon footprint for data analytics, a widely used form of machine learning, by as much as 80% while delivering 5x average speedups and 4x reductions in computing costs, according to a recent benchmark.

Thousands of companies - about 80% of the Fortune 500 - use Apache Spark to analyze their growing mountains of data. Companies using NVIDIA's Spark accelerator include Adobe, AT&T and the U.S. Internal Revenue Service.

In healthcare, Insilico Medicine discovered and put into phase 2 clinical trials a drug candidate for a relatively rare respiratory disease, thanks to its NVIDIA-powered AI platform.

Using traditional methods, the work would have cost more than $400 million and taken up to six years. But with generative AI, Insilico hit the milestone for one-tenth of the cost in one-third of the time.

This is a significant milestone not only for us, but for everyone in the field of AI-accelerated drug discovery, said Alex Zhavoronkov, CEO of Insilico Medicine.

This is just a sampler of results that users of accelerated computing and AI are pursuing at companies such as Amgen, BMW, Foxconn, PayPal and many more.

Speeding Science With Accelerated AI In basic research, the National Energy Research Scientific Computing Center (NERSC), the U.S. Department of Energy's lead facility for open science, measured results on a server with four NVIDIA A100 Tensor Core GPUs compared with dual-socket x86 CPU servers across four of its key high-performance computing and AI applications.

Researchers found that the apps, when accelerated with the NVIDIA A100 GPUs, saw energy efficiency rise 5x on average (see below). One application, for weather forecasting, logged gains of nearly 10x.

Scientists and researchers worldwide depend on AI and accelerated computing to achieve high performance and efficiency.

In a recent ranking of the world's most energy-efficient supercomputers, known as the Green500, NVIDIA-powered systems swept the top six spots, and 40 of the top 50.

Underestimated Energy Savings The many gains across industries and science are sometimes overlooked in forecasts that extrapolate only the energy consumption of training the largest AI models. That misses the benefits from most of an AI model's life when it's consuming relatively little energy, delivering the kinds of efficiencies users described above.

In an analysis citing dozens of sources, a recent study debunked as misleading and inflated projections based on training mo

LINK:	https://blogs.nvidia.com/blog/accelerated-ai-energy-efficiency/...
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