The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Center for Hybrid Multicore Productivity Research (CHMPR)

Center Overview

Emerging applications involving large-scale data and high-complexity computational models are challenging existing computing resources. CHMPR and its affiliated industry and government partners aim to be the preeminent cooperative research consortium for addressing the productivity, performance, and scalability issues that lie at the heart of these challenges. CHMPR research draws on the diverse computational science expertise available at the partnering universities to address industry's current and future multicore application needs through the continual improvement of multicore architectures and open source tools.

CHMPR's vision is to become a leading national center in providing service-oriented computational solutions. These solutions employ multicore technologies for the optimization of problems in the fields of environmental and geophysical sciences, chemical, aerospace, defense, biomedical informatics, financial, and event-driven simulations and visualizations.

Universities

Rutgers University, Newark
North Carolina State University

View Center Website

Center Personnel

Rada Chirkova
Center Staff
+1 919 515 2444
chirkova@csc.ncsu.edu

Vijayalakshmi Atluri
Center Staff
+1 973 972 0283
atluri@rutgers.edu

Dr. Gregg Rothermel
Site Director, NC State University

Research Focus

The specific multicore applications and cloud computing services will be focused on:

Big Data computations: personal medical intelligence, climate change, bio-cellular imaging, materials genomics.
Sustainable environment: human sensor networks, visual animation social media mining and modeling.
Next-gen computing platforms, including: GPU/CPU hybrid workflow systems, 512 node IBM iDataplex Dual Intel Nehalem processors, peta-exascale storage systems, and quantum computing.
Cognitive and neuroscience: hybrid compute hardware and software designs for previously incomputable domains of social consequence.
Assured social media information: crowd sourcing, false information, steganography detection.

As part of this, CHMPR will conduct:

Performance analysis and scaling optimization of data-intensive modeling and simulation applications on hybrid multicore systems.
Software tool deployments and algorithmic design to take advantage of parallel distributed computing.
Interactive digital media and 3D graphic environments.

Awards

1624605

I/UCRC: Site application to join I/UCRC known as CHMPR

Nearly every research field and industry sector is struggling with managing and extracting useful information from massive data sets, that are commonly referred to as Big Data. Modeling, data management, and analytics are key elements in understanding and using these data sets, and their importance in academia and industry is predicted to grow exponentially. One central challenge can be expressed as follows: Given specific analysis tasks and specific constraints on the available Big Data, how do we enable productive, efficient, and secure end-to-end value extraction from the data? To address this challenge, we propose to join industry members with participating academic centers in a new site of the existing Center for Hybrid Multicore Productivity Research (CHMPR) I/UCRC, at NC State University (NCSU) in Raleigh, NC, with the goal of conducting trans-disciplinary translational science and research of enabling better decision making in presence of Big Data. The fundamental research done at CHMPR/NCSU will be translated into technology developments, delivering practical solutions to hard problems. This translation will apply to federal agencies, government organizations, and industry sectors struggling with hard Big-Data problems, thus contributing to the progress of science and advancing the future Big-Data needs of the industry and society. Success in solving the problem of extracting value from massive data hinges on balancing fundamental research, technological know-how, and commercial market intelligence. We propose both to address recognized aspects of this problem and to develop the science and training that will address future aspects of this problem. The CHMPR/NCSU effort toward end-to-end enablement of data will focus on developing technologies and tools for bridging the time gap between the acquisition of data and real-time and long-term decision making. CHMPR/NCSU research projects will be addressing the issue of what are appropriate technologies for: storing and cleaning the data; modeling the data; making the data secure; asking the right analysis questions; pre- and post-processing the data with regard to the analysis; and, finally, making the analysis results useful. The resulting techniques will be applicable across multiple industry sectors, including national security, health care, manufacturing, energy, and business intelligence. The research being done at NCSU will contribute to the computationally intensive analytics-research program of CHMPR, while complementing its portfolio by the NCSU big-data transformation and analysis expertise. The technologies and tools resulting from the proposed work will change the results that can be obtained from the data, as opposed to just obtaining the results faster. The ultimate success of this paradigm shift by the Big-Data-analysis industry will rest on the ability of universities such as NCSU to prepare experts in taking advantage of the data-enablement science and technologies to solve a variety of real-life applications. Read More >

1624503

I/UCRC: Center for Hybrid Multicore Productivity Research (CHMPR) - Rutgers Site

As a research site of the NSF I/UCRC for Hybrid Multicore Productivity Research (CHMPR), the mission of the Rutgers Institute for Data Science, Learning, and Applications (I-DSLA) is to advance research and education in the area of Big data management and analytics. The multidisciplinary research program in collaboration with the industry partners, focuses on addressing research and development challenges in application domains that span healthcare; e-government and e-commerce; and community resilience and public safety. A key objective of this project is to integrate research and education by providing student opportunities to work on real world problems with industry partners. The emphasis on real problems is particularly suited to attracting individuals from diverse backgrounds, broadening participation in computer science and informatics research. The research agenda of the Rutgers I-DSLA site is complementary and synergistic to that of CHMPR. The research projects at I-DSLA span several themes (data analytics, machine learning, data management, information security and privacy, social media, Web services, and semantic Web) and domains. In the healthcare domain, the multidisciplinary research program involves researchers from the Rutgers New Jersey Medical School Cancer Center, Rutgers I-DSLA, and Rutgers Center for Information Management, Integration, and Connectivity (CIMIC). The program leverages the complementary expertise of the team members to develop data analytics-based approaches that enables discovery of optimal treatment regimens and identification of patient and disease characteristics that permit responses to specific therapeutic combinations and modalities. In the e-government and e-commerce domains, the research work addresses fundamental problems related to secure information sharing, business process composition and management, and privacy-preserving data analysis. In the community resilience and public safety domain, the research work focuses on disaster management, critical infrastructure protection, and smart cities. Read More >

The opinions, findings, and conclusions or recommendations expressed are those of the Center author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.