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February 9, 2024

NVIDIA: How Energy-Efficient Computing for AI Is Propelling Innovation and Savings Across Industries

Feb. 9, 2024 — With advances in computing, sophisticated AI models and machine learning are having a profound impact on business and society. Industries can use AI to quickly analyze vast bodies of data, allowing them to derive meaningful insights, make predictions and automate processes for greater efficiency.

Credit: khunkornStudio/Shutterstock

In the public sector, government agencies are achieving superior disaster preparedness. Biomedical researchers are bringing novel drugs to market faster. Telecommunications providers are building more energy-efficient networks. Manufacturers are trimming emissions from product design, development and manufacturing processes. Hollywood studios are creating impressive visual effects at a fraction of the cost and time. Robots are being deployed on important missions to help preserve the Earth. And investment advisors are running more trade scenarios to optimize portfolios.

Eighty-two percent of companies surveyed are already using or exploring AI, and 84% report that they’re increasing investments in data and AI initiatives. Any organization that delays AI implementation risks missing out on new efficiency gains and becoming obsolete.

However, AI workloads are computationally demanding, and legacy computing systems are ill-equipped for the development and deployment of AI. CPU-based compute requires linear growth in power input to meet the increased processing needs of AI and data-heavy workloads. If data centers are using carbon-based energy, it’s impossible for enterprises to innovate using AI while controlling greenhouse gas emissions and meeting sustainability commitments. Plus, many countries are introducing tougher regulations to enforce data center carbon reporting.

Accelerated computing — the use of GPUs and special hardware, software and parallel computing techniques — has exponentially improved the performance and energy efficiency of data centers.

Below, read more on how industries are using energy-efficient computing to scale AI, improve products and services, and reduce emissions and operational costs.

The Public Sector Drives Research, Delivers Improved Citizen Services

Data is playing an increasingly important role in government services, including for public health and disease surveillance, scientific research, social security administration, and extreme-weather monitoring and management. These operations require platforms and systems that can handle large volumes of data, provide real-time data access, and ensure data quality and accuracy.

But many government agencies rely on legacy systems that are difficult to maintain, don’t efficiently integrate with modern technologies and consume excessive energy. To handle increasingly demanding workloads while sticking to sustainability goals, government agencies and public organizations must adopt more efficient computing solutions.

The U.S. Department of Energy is making inroads in this endeavor. The department runs the National Energy Research Scientific Computing Center for open science. NERSC develops simulations, data analytics and machine learning solutions to accelerate scientific discovery through computation. Seeking new computing efficiencies, the center measured results across four of its key high performance computing and AI applications. It clocked how fast the applications ran, as well as how much energy they consumed using CPU-only versus GPU-accelerated nodes on Perlmutter, one of the world’s largest supercomputers.

At performance parity, a GPU-accelerated cluster consumes 588 less megawatt hours per month, representing a 5x improvement in energy efficiency. By running the same workload on GPUs rather than CPU-only instances, researchers could save millions of dollars per month. These gains mean that the 8,000+ researchers using NERSC computing infrastructure can perform more experiments on important use cases, like studying subatomic interactions to uncover new green energy sources, developing 3D maps of the universe and bolstering a broad range of innovations in materials science and quantum physics.

Governments help protect citizens from adverse weather events, such as hurricanes, floods, blizzards and heat waves. With GPU deployments, climate models, like the IFS model from the European Centre for Medium-Range Weather Forecasts, can run up to 24x faster while reducing annual energy usage by up to 127 gigawatt hours compared to CPU-only systems. As extreme-weather events occur with greater frequency and, often, with little warning, meteorology centers can use accelerated computing to generate more accurate, timely forecasts that improve readiness and response.

By adopting more efficient computing systems, governments can save costs while equipping researchers with the tools they need for scientific discoveries to improve climate modeling and forecasting, as well as deliver superior services in public health, disaster relief and more.

Drug Discovery Researchers Conduct Virtual Screenings, Generate New Proteins at Light Speed

Drug development has always been a time-consuming process that involves innumerable calculations and thousands of experiments to screen new compounds. To develop novel medications, the binding properties of small molecules must be tested against protein targets, a cumbersome task required for up to billions of compounds — which translates to billions of CPU hours and hundreds of millions of dollars each year.

Highly accurate AI models can now predict protein structures, generate small molecules, predict protein-ligand binding and perform virtual screening.

Researchers at Oak Ridge National Laboratory (ORNL) and Scripps Research have shown that screening a dataset of billions of compounds against a protein, which has traditionally taken years, can now be completed in just hours with accelerated computing. By running AutoDock, a molecular-modeling simulation software, on a supercomputer with more than 27,000 NVIDIA GPUs, ORNL screened more than 25,000 molecules per second and evaluated the docking of 1 billion compounds in less than 12 hours. This is a speedup of more than 50x compared with running AutoDock on CPUs.

Iambic, an AI platform for drug discovery, has developed an approach combining quantum chemistry and AI that calculates quantum-accurate molecular-binding energies and forces at a fraction of the computational expense of traditional methods. These energies and forces can power molecular-dynamics simulations at unprecedented speed and accuracy. With its OrbNet model, Iambic uses a graph transformer to power quantum-mechanical operators that represent chemical structures. The company is using the technology to identify drug molecules that could deactivate proteins linked to certain cancer types.

As the number of new drug approvals declines and research and development and computing costs rise, optimizing drug discovery with accelerated computing can help control energy expenditures while creating a far-reaching impact on medical research, treatments and patient outcomes.

To continue reading, click here.

Source: Shar Narasimhan, Nvidia