People agree: accelerated computing is energy-efficient computing.
The National Energy Research Scientific Computing Center (NERSC), the U.S. Department of Energy’s lead facility for open science, measured results across four of its key high performance computing and AI applications.
They clocked how fast the applications ran and how much energy they consumed on CPU-only and GPU-accelerated nodes on Perlmutter, one of the world’s largest supercomputers using NVIDIA GPUs.
The results were clear. Accelerated with NVIDIA A100 Tensor Core GPUs, energy efficiency rose 5x on average. An application for weather forecasting logged gains of 9.8x.
GPUs Save Megawatts
On a server with four A100 GPUs, NERSC got up to 12x speedups over a dual-socket x86 server.
That means, at the same performance level, the GPU-accelerated system would consume 588 megawatt-hours less energy per month than a CPU-only system. Running the same workload on a four-way NVIDIA A100 cloud instance for a month, researchers could save more than USD $4 million compared to a CPU-only instance.
Measuring Real-World Applications
The results are significant because they are based on measurements of real-world applications, not synthetic benchmarks.
The gains mean that the 8,000+ scientists using Perlmutter can tackle bigger challenges, opening the door to more breakthroughs.
Among the many use cases for the more than 7,100 A100 GPUs on Perlmutter, scientists are probing subatomic interactions to find new green energy sources.
Advancing Science at Every Scale
The applications NERSC tested span molecular dynamics, material science and weather forecasting.
For example, MILC simulates the fundamental forces that hold particles together in an atom. It’s used to advance quantum computing, study dark matter and search for the origins of the universe.
BerkeleyGW helps simulate and predict optical properties of materials and nanostructures, a key step toward developing more efficient batteries and electronic devices.
EXAALT, which got an 8.5x efficiency gain on A100 GPUs, solves a fundamental challenge in molecular dynamics. It lets researchers simulate the equivalent of short videos of atomic movements rather than the sequences of snapshots other tools provide.
The fourth application in the tests, DeepCAM, is used to detect hurricanes and atmospheric rivers in climate data. It got a 9.8x gain in energy efficiency when accelerated with A100 GPUs.
Savings With Accelerated Computing
The NERSC results echo earlier calculations of the potential savings with accelerated computing. For example, in a separate analysis NVIDIA conducted, GPUs delivered 42x better energy efficiency on AI inference than CPUs.
That means switching all the CPU-only servers running AI worldwide to GPU-accelerated systems could save a whopping 10 trillion watt-hours of energy a year. That’s like saving the energy 1.4 million homes consume in a year.
Accelerating the Enterprise
You do not have to be a scientist to get gains in energy efficiency with accelerated computing.
Pharmaceutical companies are using GPU-accelerated simulation and AI to speed the process of drug discovery. Carmakers like BMW Group are using it to model entire factories.
They are among the growing ranks of enterprises at the forefront of what NVIDIA founder and CEO Jensen Huang calls an industrial HPC revolution, fueled by accelerated computing and AI.
Archive
- October 2024(44)
- September 2024(94)
- August 2024(100)
- July 2024(99)
- June 2024(126)
- May 2024(155)
- April 2024(123)
- March 2024(112)
- February 2024(109)
- January 2024(95)
- December 2023(56)
- November 2023(86)
- October 2023(97)
- September 2023(89)
- August 2023(101)
- July 2023(104)
- June 2023(113)
- May 2023(103)
- April 2023(93)
- March 2023(129)
- February 2023(77)
- January 2023(91)
- December 2022(90)
- November 2022(125)
- October 2022(117)
- September 2022(137)
- August 2022(119)
- July 2022(99)
- June 2022(128)
- May 2022(112)
- April 2022(108)
- March 2022(121)
- February 2022(93)
- January 2022(110)
- December 2021(92)
- November 2021(107)
- October 2021(101)
- September 2021(81)
- August 2021(74)
- July 2021(78)
- June 2021(92)
- May 2021(67)
- April 2021(79)
- March 2021(79)
- February 2021(58)
- January 2021(55)
- December 2020(56)
- November 2020(59)
- October 2020(78)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(25)
- September 2019(24)
- August 2019(14)
- July 2019(23)
- June 2019(54)
- May 2019(82)
- April 2019(76)
- March 2019(71)
- February 2019(67)
- January 2019(75)
- December 2018(44)
- November 2018(47)
- October 2018(74)
- September 2018(54)
- August 2018(61)
- July 2018(72)
- June 2018(62)
- May 2018(62)
- April 2018(73)
- March 2018(76)
- February 2018(8)
- January 2018(7)
- December 2017(6)
- November 2017(8)
- October 2017(3)
- September 2017(4)
- August 2017(4)
- July 2017(2)
- June 2017(5)
- May 2017(6)
- April 2017(11)
- March 2017(8)
- February 2017(16)
- January 2017(10)
- December 2016(12)
- November 2016(20)
- October 2016(7)
- September 2016(102)
- August 2016(168)
- July 2016(141)
- June 2016(149)
- May 2016(117)
- April 2016(59)
- March 2016(85)
- February 2016(153)
- December 2015(150)