In this video from CCTV News, ASC16 wraps up their Student Supercomputing Challenge. Huazhong University of Science won the overall competition, which concluded April 22 in Wuhan at the Central China University of Science. “With over 175 participating teams, the ASC16 is the world’s largest student cluster competition. In a race against time, student teams build HPC clusters and tune application codes to run with under 3000 watts of power.”
Today Panasas announced that it has joined the iRODS Consortium as a contributing member. The iRODS Consortium leads development and support of the Integrated Rule-Oriented Data System (iRODS), free open source software for data discovery, workflow automation, secure collaboration, and data virtualization.
Today Cambridge University spin-out Optalysys announced that the company has been awarded a $350k grant for a 13-month project from the US Defense Advanced Research Projects Agency (DARPA). The project will see the company advance their research in developing and applying their optical co-processing technology to solving complex mathematical equations. These equations are relevant to large-scale scientific and engineering simulations such as weather prediction and aerodynamics.
“The National Supercomputing Center supports research projects at the University of Nevada, Las Vegas by providing a full-service supercomputing facility, plus available training and services, to academic and research institutions, government and private industry. NSCEE’s focus is on R&D related to energy, the environment, medical informatics and health care delivery. In this presentation, Lombardo will highlight results from an Alzheimer’s research project and the NSCEE’s new system at the Supernap and how it is being used to advance research for HPC users in both academia/R&D and commercial industry. Lombardo will also highlight two emerging projects; the New School of Medicine and new Technology park.”
“We wanted to get away from the complexity of POSIX for data, yet retain the parts of POSIX that people are used to (metadata manipulation). By divorcing ourselves from the complications of ensuring a completely POSIX data flow, we can massively simplify the data movement and storage mechanisms. MarFS lets us retain the parts of POSIX that users appreciate for data management (chown, chmod, rename, mv, etc) without inheriting the complexity of managing POSIX semantics for data manipulation. By treating the data as essentially immutable, we can leverage the very simple PUT/GET/DELETE semantics of “cloudy” data storage systems to scale out storage with ease.”
Berkeley Lab recently hosted the fourth annual X-Stack PI event, where X-Stack researchers, facilities teams, application scientists, and developers from national labs, universities, and industry met to share the latest developments in X-Stack application codes. “X-Stack was launched in 2012 by the U.S. Department of Energy’s Advanced Scientific Computing Research program to support the development of exascale software tools, including programming languages and libraries, compilers and runtime systems, that will help programmers handle massive parallelism, data movement, heterogeneity and failures as the scientific community transitions to the next generation of extreme-scale supercomputers.”
In news from China this week, the Huazhong University of Science has won the ASC16 Student Cluster Challenge. The final round of the competition concluded Friday in Wuhan at the Central China University of Science.
Today Mellanox announced that University of Cambridge has selected Mellanox End-to-End Ethernet interconnect solution including Spectrum SN2700 Ethernet switches, ConnectX-4 Lx NICs and LinkX cables for its OpenStack-based scientific research cloud. This new win has expanded Mellanox’s existing footprint of InfiniBand solution and empowers the UoC to realize its vision of HPC and Cloud convergence through high-speed cloud networks at 25/50/100Gb/s throughput.
Today the University of Iceland unveiled a new supercomputer that will boost research in a range of scientific areas. Manufactured by Lenovo, the cluster was funded by the Research Infrastructure Fund Iceland with matching funds from the University of Iceland, Reykjavik University.
In this video from the 2016 GPU Technology Conference, Greg Schmidt from Hewlett Packard Enterprise describes the new Apollo 6500 server. “With up to eight high performance NVIDIA GPU cards designed for maximum transfer bandwidth, the HPE Apollo 6500 System is purpose-built for deep learning applications. Its high ratio of GPUs to CPUs, dense 4U form factor and efficient design enable organizations to run deep learning recommendation algorithms faster and more efficiently, significantly reducing model training time and accelerating the delivery of real-time results, all while controlling costs.”