NVIDIA-Accelerated Supercomputers Hit New Highs on TOP500 List
November 12 2018 - 9:00AM
NVIDIA GPU-Accelerated Systems Jump Nearly 50
Percent in One Year;Accelerate Fastest Supercomputers in the World
- U.S., Europe, Japan;Power 22 of World’s 25 Most Energy-Efficient
Supercomputers
NVIDIA’s position as the most dynamic force in global
supercomputing was confirmed today in the just-issued TOP500 list
of the world’s fastest supercomputers.
The closely watched list, released at the start of the SC18
annual high performance computing conference, shows a 48 percent
jump in one year in the number of systems using NVIDIA GPU
accelerators. The total climbed to 127 from 86 a year ago, and is
three times greater than five years ago.
Moreover, NVIDIA GPUs power the world’s two fastest
supercomputers — the U.S. Department of Energy’s Summit, at Oak
Ridge National Laboratory, and Sierra, at Lawrence Livermore
National Lab. Combined, the two systems feature more than 40,000
NVIDIA® V100 Tensor Core GPUs, which enabled the world’s leading
researchers to do groundbreaking research recognized in five out of
this year’s six finalists for the Gordon Bell Prize, to be awarded
during this week’s SC18 conference.
Europe’s and Japan’s fastest supercomputers are also accelerated
by NVIDIA GPUs.
Also released today, the GREEN500 list, which measures the
energy-efficiency of the world’s fastest systems, shows that NVIDIA
powers 22 of the top 25 “greenest” systems.
The latest list marks another milestone — for the first time,
nearly half of its compute power — 702 of 1,417 petaflops — comes
from accelerated systems. Prior to 10 years ago, no accelerated
systems appeared on the list.
“This is a breakout year for NVIDIA in the world of
supercomputing,” said Jensen Huang, founder and CEO of NVIDIA.
“With the end of Moore’s law, a new HPC market has emerged, fueled
by new AI and machine learning workloads. These rely as never
before on our high-performance, highly efficient GPU platform to
provide the power required to address the most challenging problems
in science and society.”
The latest list shows that 52, or one-third, of the 153 systems
debuting on the TOP500 are GPU-accelerated, compared with 33 new
GPU-accelerated systems on the list a year ago.
Among the new systems this year is NVIDIA DGX-2 POD™, at No. 62.
The first DGX-2 cluster appearing on the list, it combines 36 DGX-2
systems and delivers more than 3 petaflops of double-precision
performance. Based on these results, a cluster of only 11 of these
systems would earn a spot on the current TOP500 list.
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About NVIDIANVIDIA‘s (NASDAQ: NVDA) invention
of the GPU in 1999 sparked the growth of the PC gaming market,
redefined modern computer graphics and revolutionized parallel
computing. More recently, GPU deep learning ignited modern AI — the
next era of computing — with the GPU acting as the brain of
computers, robots and self-driving cars that can perceive and
understand the world. More information at
http://nvidianews.nvidia.com/.
For further information, contact:Kristin
BrysonPR Director for Data Center AI, HPC and Accelerated
ComputingNVIDIA Corporation+1-203-241-9190kbryson@nvidia.com
Certain statements in this press release including, but not
limited to, statements as to: NVIDIA’s position as the most dynamic
force in global supercomputing; NVIDIA GPUs enabling the world’s
leading researchers to do groundbreaking research that have been
named finalists for the Gordon Bell Prize; NVIDIA GPUs powering the
world’s fastest energy-efficient systems; NVIDIA having a breakout
year in the world of supercomputing; new HPC markets emerging,
fueled by new AI and machine learning workloads, and HPC markets
relying on NVIDIA’s GPU platform to provide the power required to
address the most challenging problems in science and technology;
GPUs accelerating one-third of the systems on the TOP500 list and
DGX-2 clusters earning a spot on the TOP500 list; and the
performance, benefits and abilities of DGX-2 and NVIDIA GPUs are
forward-looking statements that are subject to risks and
uncertainties that could cause results to be materially different
than expectations. Important factors that could cause actual
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or demands; changes in industry standards and interfaces;
unexpected loss of performance of our products or technologies when
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