Perlmutter or NERSC 9, the GPU-accelerated supercomputer built by Hewlett Packard Enterprise (HPE) in partnership with Nvidia and AMD, was unveiled virtually at the Berkeley Lab's National Energy Research Scientific Computing Center (NSERC). 

Perlmutter is now the fastest AI supercomputer in the world. The HPE Cray EX supercomputer harnesses 6,159 Nvidia A100 GPUs and 1,500 AMD Milan CPUs to deliver nearly 3.8 exaflops of theoretical “AI performance” or about 60 petaflops of peak double-precision (standard FP64) HPC performance. The Perlmutter system will help map the visible universe spanning 11 billion light years by processing data from Dark Energy Spectroscopic Instrument (DESI), which is capable of capturing as many as 5,000 galaxies in a single exposure, stated reports. 

The system is the namesake of Saul Perlmutter, an astrophysicist at Berkeley Lab who shared the 2011 Nobel Prize in Physics for his contributions to research showing that the expansion of the universe is accelerating. 

In early benchmarking, NERSC researchers have reported up to 20X performance speedups using the GPUs, which they say will accelerate their workflows from a matter of weeks or months down to hours. Materials science is expected to see similar benefits, laying the way for advances in batteries and biofuels. Applications such as Quantum Espresso leverage Perlmutter’s traditional simulation and machine learning capabilities, enabling scientists to study more atoms over a longer time period.

“In the past it was impossible to do fully atomistic simulations of big systems like battery interfaces, but now scientists plan to use Perlmutter to do just that,” said Brandon Cook, an applications performance specialist at NERSC. That’s where Tensor Cores in the A100 play a unique role. They accelerate both the double-precision floating point math for simulations and the mixed-precision calculations required for deep learning. Similar work won NERSC recognition in November as a Gordon Bell finalist for its BerkeleyGW program using NVIDIA V100 GPUs. The extra muscle of the A100 promises to take such efforts to a new level, said Jack Deslippe, who led the project and oversees application performance at NERSC.

In order to know where to point this expensive instrument each evening, researchers need to assess the data from the night before. Perlmutter can analyse dozens of exposures quickly enough to provide this feedback in time for the next nightly cycle.