Folks agree: accelerated computing is energy-efficient computing.
The Nationwide Vitality Analysis Scientific Computing Middle (NERSC), the U.S. Division of Vitality’s lead facility for open science, measured outcomes throughout 4 of its key excessive efficiency computing and AI functions.
They clocked how briskly the functions ran and the way a lot vitality they consumed on CPU-only and GPU-accelerated nodes on Perlmutter, one of many world’s largest supercomputers utilizing NVIDIA GPUs.
The outcomes have been clear. Accelerated with NVIDIA A100 Tensor Core GPUs, vitality effectivity rose 5x on common. An software for climate forecasting logged features of 9.8x.
GPUs Save Megawatts
On a server with 4 A100 GPUs, NERSC acquired as much as 12x speedups over a dual-socket x86 server.
Which means, on the identical efficiency stage, the GPU-accelerated system would eat 588 megawatt-hours much less vitality per 30 days than a CPU-only system. Operating the identical workload on a four-way NVIDIA A100 cloud occasion for a month, researchers might save greater than $4 million in comparison with a CPU-only occasion.
Measuring Actual-World Purposes
The outcomes are important as a result of they’re based mostly on measurements of real-world functions, not artificial benchmarks.
The features imply that the 8,000+ scientists utilizing Perlmutter can sort out larger challenges, opening the door to extra breakthroughs.
Among the many many use instances for the greater than 7,100 A100 GPUs on Perlmutter, scientists are probing subatomic interactions to search out new inexperienced vitality sources.
Advancing Science at Each Scale
The functions NERSC examined span molecular dynamics, materials science and climate forecasting.
For instance, MILC simulates the basic forces that maintain particles collectively in an atom. It’s used to advance quantum computing, examine darkish matter and seek for the origins of the universe.
BerkeleyGW helps simulate and predict optical properties of supplies and nanostructures, a key step towards growing extra environment friendly batteries and digital units.
EXAALT, which acquired an 8.5x effectivity acquire on A100 GPUs, solves a basic problem in molecular dynamics. It lets researchers simulate the equal of quick movies of atomic actions reasonably than the sequences of snapshots different instruments present.
The fourth software within the assessments, DeepCAM, is used to detect hurricanes and atmospheric rivers in local weather information. It acquired a 9.8x acquire in vitality effectivity when accelerated with A100 GPUs.
Financial savings With Accelerated Computing
The NERSC outcomes echo earlier calculations of the potential financial savings with accelerated computing. For instance, in a separate evaluation NVIDIA carried out, GPUs delivered 42x higher vitality effectivity on AI inference than CPUs.
Which means switching all of the CPU-only servers working AI worldwide to GPU-accelerated methods might save a whopping 10 trillion watt-hours of vitality a 12 months. That’s like saving the vitality 1.4 million properties eat in a 12 months.
Accelerating the Enterprise
You don’t must be a scientist to get features in vitality effectivity with accelerated computing.
Pharmaceutical firms are utilizing GPU-accelerated simulation and AI to hurry the method of drug discovery. Carmakers like BMW Group are utilizing it to mannequin whole factories.
They’re among the many rising ranks of enterprises on the forefront of what NVIDIA founder and CEO Jensen Huang calls an industrial HPC revolution, fueled by accelerated computing and AI.
