Ding "GPUs Accelerate Research"
CS Ass't. Prof. Yufei Ding and UCSB researchers are already reaping the rewards of a new state-GPU computer cluster
Originally designed to advance video games and on-screen three-dimensional rendering, GPUs are now powering scientific data processing and academic research, dramatically accelerating modeling and computations. Because it is specifically designed for basic mathematical operations and some codes, a GPU-based system can perform computations thirty to fifty times faster than conventional central processing unit (CPU) hardware can. GPUs are more efficient — performing multiple calculations simultaneously, while CPUs work through calculations a few at a time — and as a result have turbocharged computational research in a growing number of fields.
“This is a game-changer for researchers at UCSB,” said Kris Delaney, a co-principal investigator on the grant and project scientist with the Materials Research Laboratory (MRL).
The Center for Scientific Computing (CSC) at the California NanoSystems Institute (CNSI) at UC Santa Barbara and the MRL are managing and maintaining the new system, which was funded by a $400,000 grant from the National Science Foundation to accelerate scientific computing. The upgrade includes twelve compute nodes, with each one supporting four high-end NVIDIA Tesla V100 GPUs that are equipped with hardware double-precision processing, large (32 GB) memory, and NVLink for fast inter-GPU communications. Ten of the nodes were added to the existing High Performance Computing (HPC) campus cluster “Pod,” which has three GPU nodes as part of its complement of computing nodes and is available to be used by all UCSB researchers. The two remaining nodes became part of the delocalized Pacific Research Platform Nautilus cluster, which connects a few dozen universities along the West Coast, including UCSB.
The cluster has also allowed Yufei Ding to process data in her research on machine learning and quantum computing. Ding’s group seeks
to facilitate faster and more energy-efficient neural-network training and inference by minimizing the size of the network and modeling the associated architecture and framework.
“This new GPU cluster enables a tremendous amount of research to be completed that would not be done or would take too much time to fulfill,” said Ding.
The center estimates that up to three-fourths of the people using the expanded cluster will be UCSB students. The shared resource will also be available to high school and community college students and teachers who participate in campus-sponsored programs.
COE/CLS Convergence magazine (W21) - "GPUs Accelerate Research" (full article pg. 7)
