March 29, 2021 — The Pittsburgh Supercomputing Center (PSC) has deployed Neocortex, an HPC system PSC said is designed to revolutionize scientific AI research. Funded by a $5 million grant from the National Science Foundation last June, Neocortex utilizes new hardware with the goal of accelerating AI research. PSC, a joint research organization of Carnegie Mellon University and the University of Pittsburgh, built the new system in partnership with Cerebras Systems and Hewlett Packard Enterprise (HPE). Neocortex is available at no cost to selected researchers advancing open research.
PSC said science and engineering projects are now running on Neocortex as a part of the system’s early user program that began last month. Projects include drug discovery, genomics, molecular dynamics, climate research, computational fluid dynamics, signal processing and medical imaging analysis.
The center said Neocortex goes beyond technologies powering AI since 2012 by “exploring a revolutionary combination of Cerebras’ CS-1 systems and Wafer Scale Engine (WSE) processors, which are designed specifically to accelerate AI, and a large-memory HPE Superdome Flex server for data handling capability. Cerebras’ WSE processors are the largest computer chips ever built and power the Cerebras CS-1 system. Depending on workload, the CS-1 delivers hundreds or thousands of times more performance than legacy alternatives, and it does so at a fraction of the power draw and space.”
Neocortex system democratizes access for researchers to enhanced compute power for AI model training, the most time-consuming step in deep learning research, to be much faster, even interactive, according to PSC.
“The Neocortex program introduces a spectacular cutting-edge supercomputer resource to accelerate our research to combat breast cancer,” said Dr. Shandong Wu, Associate Professor of Radiology and Director of the Intelligent Computing for Clinical Imaging (ICCI) Lab at the University of Pittsburgh, who leads a team employing deep learning on Neocortex for high-resolution medical imaging analysis for breast cancer risk prediction. “The program also has a very responsive support team to help us resolve issues and has enabled great progress of our work.”
“As early users, we are working to use Neocortex to train novel graph neural algorithms for very large graphs, such as those found in social networks,” said Dr. John Wohlbier of the Emerging Technology Center at the CMU Software Engineering Institute, another early user. “We are excited to explore to what extent will AI-specific ASICs, such as the one in Neocortex, enable model training and parameter studies on a much faster timescale than resources found in a typical datacenter or the cloud.”
