The role of accelerated computing in reducing energy consumption
Businesses increasingly rely on powerful computing systems housed in data centers for their workloads. As the data center market expands, at an estimated growth rate of 10.5% from 2024 to 20301, energy consumption has become a major concern. Data centers consume about 1-2% of the world’s electricity2, expected to double by 2030. That’s a lot of energy.
Without evolving data center platforms, energy sources, and operations to support this demand, data center growth will not be sustainable financially or environmentally. The energy demands are too high, and the old ways of boosting performance are no longer sufficient. This is where accelerated computing3, powered by graphics processing units (GPUs), has emerged as a transformative solution. This solution, when optimized to reduce emissions and energy consumption, is our best path to sustainable computing4.
Striking the Balance With Accelerated Computing
As businesses strive to cut emissions while delivering more computational throughput in the era of AI and big data, accelerated computing has become essential to achieve these goals. By switching from traditional CPU-only servers to GPU-accelerated systems, data centers can make huge gains in energy efficiency5 and improve performance.
Moore’s Law, which predicted that the transistors on a microchip would double approximately every two years, exponentially enhancing computing power, has slowed significantly. This has resulted in only marginal performance improvements in CPUs over the last few generations, with commensurate increases in energy consumption. This is why we see “data center inflation,” where computing costs escalate rapidly to address growing demand. In contrast, GPUs recover the performance and efficiency wasted by CPUs, returning to an exponential growth curve.
It’s useful to differentiate between power and energy to grasp potential cost savings. Power measures the energy consumption rate, while energy represents the work capacity used to complete a task.
Think of energy as fuel, and power as the speed and distance at which the fuel is burned. Imagine transporting a large quantity of goods. It might seem like a big energy expenditure, but using a train is more efficient when considering volume and distance. A train can carry lots of cargo in a single trip, easily covering hundreds of miles. Using trucks to transport the same amount of goods would require multiple vehicles and trips, which increases fuel consumption significantly. Thus, trains offer a more energy-efficient and practical bulk transport solution than trucks, saving fuel and time.
Similarly, accelerated computing is helping businesses strike the right balance by reducing overall energy consumption and costs for compute-heavy workloads. GPUs may consume more power at peak times than CPUs but complete tasks much quicker. When comparing total energy consumption, GPUs use less energy and deliver faster results, making them a superior option for tasks like developing large language models and simulations.
This distinction between power and energy is crucial for designing efficient systems and analyzing energy consumption patterns, which applies to fields such as engineering, physics, environmental science, and economics.
Real-World Application
Accelerated computing practices can improve energy efficiency, reduce carbon emissions, and enhance performance and productivity. When combined with the integration of renewable energy sources like wind and solar, sustainable computing streamlines the transition toward net-zero emissions goals.
In optimized data centers, accelerated computing powers AI training and inference. This has resulted in AI being used for different types of scientific simulations, from exploring molecular dynamics to understanding fusion physics.
One use case of AI for sustainability and public safety is its ability to manage wildfires, which are becoming more frequent and severe due to climate change. Last year, Canada saw carbon emissions from wildfires reach record-breaking levels, while Greece experienced the largest blaze documented in the European Union. Extreme wildfires are predicted to increase 14% by 2030 and 30% by 20506.
Accelerated computing enhances the ability to combat these natural disasters by accurately forecasting threats and enabling the strategic positioning of fire suppression equipment. Additionally, using digital twins to simulate wildfires allows precise predictions of the spread and the implementation of effective countermeasures.
This technology also aids high-carbon sectors such as aviation by boosting fuel efficiency through engine design improvements and enhancing safety with better preventative maintenance schedules. Such measures help reduce carbon footprints and avoid disastrous part failures.
Embracing Sustainability
The limitations of traditional CPUs and the end of Moore’s Law are steering us toward a future where accelerated computing is necessary. By embracing accelerated computing, business leaders can recapture the immense waste inherent in CPU-driven systems. This shift promises a monumental leap in performance per watt, dramatically reducing costs, energy consumption, and carbon emissions.
With accelerated computing, data centers can transform into energy-efficient AI factories, achieving extraordinary savings and efficiency. The path forward is clear—accelerate everything for the sake of sustainable computing.
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