WASHINGTON--(BUSINESS WIRE)--Internet2 and the National Science Foundation (NSF) confirmed the selection of six proposals by an external academic review panel for the first phase of the Exploring Clouds for Acceleration of Science (E-CAS) project that was first announced in November 2018. The E-CAS project comprises two phases of NSF-funded, campus-based research that will be supported in part through contributions from Amazon Web Services and Google Cloud to produce a deeper understanding of the use of cloud computing in accelerating scientific discoveries.
The successful proposals for the year-long first phase of the E-CAS project are:
Development of BioCompute Objects for Integration into Galaxy in a
Cloud Computing Environment
Raja Mazumder, George Washington
University
BioCompute objects allow researchers to describe bioinformatic analyses comprised of any number of algorithmic steps and variables to make computational experimental results clearly understandable and easier to repeat. This project will create a library of BioCompute objects that describe bioinformatic workflows on AWS, which can be accessed and contributed to by users of the widely used bioinformatics platform, Galaxy.
Investigating Heterogeneous Computing at the Large Hadron Collider
Philip
Harris, Massachusetts Institute of Technology
Only a small fraction of the 40 million collisions per second at the Large Hadron Collider are stored and analyzed due to the huge volumes of data and the compute power required to process it. This project proposes a redesign of the algorithms using modern machine learning techniques that can be incorporated into heterogeneous computing systems, allowing more data to be processed and thus larger physics output and potentially foundational discoveries in the field.
Building Clouds: Worldwide building typology modelling from images
Daniel
Aliaga and Dev Niyogi, Purdue University
This project will utilize computational power and network connectivity to provide a world-scalable solution for generating building-level information for urban canopy parameters as well as for improving the information for estimating local climate zones, both of which are critical to high resolution urban meteorological/environmental models.
Accelerating Science by Integrating Commercial Cloud Resources in the
CIPRES Science Gateway
Mark Miller, San Diego Supercomputing
Center
CIPRES is a web portal that allows scientists around the world to analyze DNA and protein sequence data by providing access to parallel phylogenetics codes run on large high-performance computing (HPC) clusters provided by the NSF-funded eXtreme Science and Engineering Discovery Environment (XSEDE) program and currently runs analyses for about 12,000 scientists per year. This project will develop the infrastructure needed to cloudburst CIPRES jobs to newer, faster V100 GPUs at AWS. As a result, individual jobs will run up to 1.8 fold faster, and users will have access to twice as many GPU nodes as they did in the previous year.
Deciphering the Brain's Neural Code Through Large-Scale Detailed
Simulation of Motor Cortex Circuits
William Lytton, State
University of New York
This project aims to help decipher the brain's neural coding mechanisms with far-reaching applications, including developing treatments for brain disorders, advancing brain-machine interfaces for people with paralysis, and developing novel artificial intelligence algorithms. Using a software tool for brain modeling, researchers will run thousands of parallelized simulations exploring different conditions and inputs to the system.
IceCube computing in the cloud
Benedikt Riedel,
University of Wisconsin
The IceCube Neutrino Observatory located at the South Pole supports science from a number of disciplines including astrophysics, particle physics, and geographical sciences operating continuously being simultaneously sensitive to the whole sky. This project aims to burst into cloud to support follow-up computations of observed events, as well as alerts to and from the research community, such as other telescopes and LIGO.
“NSF is thrilled to see the scientific diversity and potential among the selected projects. We look forward to the progress over the next year for these six projects as they investigate the viability and effectiveness of commercial clouds as an option for leading-edge research computing and computational science in a range of areas,” said Manish Parashar, Director of NSF's Office of Advanced Cyberinfrastructure (OAC).
“More recent advancements in cloud offerings allow researchers to explore unique ways of processing huge amounts of data with highly complex inter-relationships using high-throughput computational methods and machine learning systems,” added Howard Pfeffer, president and CEO of Internet2, and principal investigator on the E-CAS project. “We’re excited to support these important and timely scientific research projects as we collectively explore how advancement in commercial clouds can better support the work of researchers and the higher education community.”
Following the completion of the first phase of these six research projects, two final projects will be selected in July 2020 for another year of support, with a focus on delivering scientific results. Each phase of the project will be followed by a community-led workshop to assess lessons learned and to define leading practices.
For more information about the E-CAS project, please visit www.internet2.edu/ecas.
The National Science Foundation (NSF) is an independent federal agency that supports fundamental research and education across all fields of science and engineering. In fiscal year (FY) 2018, its budget was $7.8 billion. NSF funds reach all 50 states through grants to nearly 2,000 colleges, universities and other institutions. Each year, NSF receives more than 50,000 competitive proposals for funding and makes about 12,000 new funding awards.
This material is based upon work supported by the National Science Foundation under Grant No. 1904444. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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