New research and a new support wiki
By NYU HPC Support Team
High performance computing hardware
Using NYU’s High Performance Computing service (NYU HPC) got easier this semester when ITS released the new NYU HPC support wiki on January 25. In an easily navigated format, this new wiki offers expanded information about the resources of the NYU HPC service, and how to use them—information that will help researchers whether they are currently using the service, just getting started, or considering using high performance resources in their research.
NYU HPC: A crucial resource for researchers
NYU HPC provides a crucial set of resources for researchers at NYU and other research institutions, enabling them to solve large-scale, data-intensive, advanced computation problems on research topics across the disciplinary spectrum.
NYU HPC offers high-powered computer systems equipped with a wide variety of software packages and related services for the research community. This ITS service is available to full-time NYU faculty members and sponsored graduate students and staff, as well as to sponsored researchers at other universities and organizations with whom NYU faculty members are collaborating.
Each researcher using NYU HPC has access to the service’s high-speed, high-performance clusters, 2TB of archival storage space; an NYU HPC technical team experienced in the support of NYU researchers and their high performance computing needs; and the extensive online assistance provided by the new wiki—including newly framed and expanded information on software, and instructions on managing data, and compiling and running jobs.
Making the service easier to use
“Our goal in creating the new wiki,” said Lynn Rohrs, who manages the NYU HPC service, “has been to better enable researchers to take full advantage of HPC services, by making these resources more accessible and easier to navigate and use. The beauty of the wiki format is that it’s easily edited and updated. We are adding new information on a regular basis. We also plan to showcase some of the remarkable projects and the researchers who have used HPC to advance their research.”
The NYU HPC support wiki: a brief tour
Whether you’re currently using NYU HPC services or would just like to learn more about what NYU HPC can offer, visit the wiki and have a look around. Check Resources at a Glance, in the About NYU HPC section, for an easy-to-read, tabular overview of the NYU HPC clusters’ specifications. Visit The Clusters section for further details, as well as a guide to which cluster is best for your purpose. For detailed how-to-use information, check out Using the Clusters. And a Glossary of high performance computing terms is among the useful topic areas within the wiki’s Reference section.
A very new addition to the wiki is the HPC Research Gallery, featuring descriptions of some of the exciting projects for which researchers have been using the NYU HPC clusters.
If you or a member of your research group has high performance computing or networking needs—including visualization, simulations, or other data-intensive operations—send an email to email@example.com to request a consultation or an NYU HPC account.
Research using the NYU HPC service
A great deal of fascinating research is being accomplished with the support of the NYU HPC. For a sample of current projects employing NYU HPC resources, be sure to read Research Using the HPC Service in this issue of Connect. For additional related articles from earlier issues of Connect, see Cluster Works: A Sampling of Research Using an NYU High Performance Computing Resource (Spring/Summer 2009) and Cardiac: A High Performance Computing Cluster for the Heart (Fall/Winter 2008), by Joseph Hargitai, and Big Data: Researchers Discuss the Opportunities & Challenges (Spring/Summer 2009), by Heather Stewart. And be sure to visit the Research Gallery within the new NYU HPC wiki for continuing updates on research using NYU HPC services.
About the author
The NYU HPC Support Team is dedicated to providing technical support to NYU researchers and their high performance computing needs.
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