NVIDIA is making available to the Arm ecosystem its full stack of AI and HPC software — which accelerates more than 600 HPC applications and all AI frameworks — by year’s end. The stack includes all NVIDIA CUDA-X AI and HPC libraries, GPU-accelerated AI frameworks and software development tools such as PGI compilers with OpenACC support and profilers.
Once stack optimization is complete, NVIDIA will accelerate all major CPU architectures, including x86, POWER and Arm.
“Supercomputers are the essential instruments of scientific discovery, and achieving exascale supercomputing will dramatically expand the frontier of human knowledge,” said Jensen Huang, founder and CEO of NVIDIA. “As traditional compute scaling ends, power will limit all supercomputers. The combination of NVIDIA’s CUDA-accelerated computing and Arm’s energy-efficient CPU architecture will give the HPC community a boost to exascale.”
“Arm is working with our ecosystem to deliver unprecedented compute performance gains and exascale-class capabilities to Arm-based SoCs,” said Simon Segars, CEO of Arm. “Collaborating with NVIDIA to bring CUDA acceleration to the Arm architecture is a key milestone for the HPC community, which is already deploying Arm technology to address some of the world’s most complex research challenges.”
According to the Green500 list released today, NVIDIA powers 22 of the world’s 25 most energy-efficient supercomputers.
Key factors making this possible are: the ability of NVIDIA GPU-powered supercomputers to offload heavy processing jobs to more energy-efficient parallel processing CUDA GPUs; NVIDIA’s collaboration with Mellanox to optimize processing across entire supercomputing clusters; and NVIDIA’s invention of SXM 3D-packaging and NVIDIA NVLink interconnect technology, which allows for extremely dense scale-up nodes.
NVIDIA’s support for Arm-based HPC systems builds on more than 10 years of collaboration. NVIDIA uses Arm for several of its system-on-chip products available for portable gaming, autonomous vehicles, robotics and embedded AI computing.