Research Projects



The overreaching goal of CometCloud is to enable highly heterogeneous, dynamically federated computing and data platforms that can support end-to-end application workflows with diverse and dynamic changing application requirements. This is achieved through (a) autonomic on-demand federation of geographically distributed compute and data resources as needed by the application workflow, and (b) exposing the resulting software-defined federated cyberinfrastructure using elastic cloud abstractions and science-as-a-service platforms. As a result, CometCloud is able to create a nimble and dynamically programmable environment that autonomously evolves over time, adapting to changes in both the federated infrastructure and the application requirements.




DataSpaces is a data management framework for scientific workflows running on high-performance computing platforms. It provides a scalable, in-memory shared-space abstraction that allows the component applications that comprise a workflow to interact and exchange data using a very simple put/get API. This shared space is built using a dedicated set of staging nodes, which are used exclusively for handling I/O operations. In addition, communication between applications and this shared space can be asynchronous and are performed with RDMA transport when available, enabling applications to take advantage of high-speed interconnect protocols without rewriting their applications for different vendors. DataSpaces has been ported to many of the current large-scale platforms, including InfiniBand clusters, Cray machines (uGNI/Gemini), and IBM Blue Gene systems (PAMI/DCMF). In addition, DataSpaces is integrated with and deployed as part of the Adaptive I/O System (ADIOS) framework distributed by Oak Ridge National Laboratories ( ADIOS is an open source I/O middleware package that has been shown to scale to hundreds of thousands of cores and is being used by a very wide range of applications. DataSpaces/ADIOS has been used to support coupled applications in combustion, fusion, material science, chemistry, and FEM+AMR. 




The GreenHPC initiative at Rutgers is a research and educational initiative aiming at addressing several efforts in the intersection of energy efficiency, scalable computing and high performance computing. Key focus areas include (1) Energy efficiency of scientific data analysis pipelines at scale, (2) In-situ data analytics and co-processing at extreme scales and (3) Application-aware cross-layer power management for High Performance Computing systems .GreenHPC also acts as a forum for researchers and the educational community to exchange ideas and experiences on energy efficiency by disseminating research results, educational activities at different levels (PhD, MS, undergraduate - REU, K12 - GSET) and organizing events and editorial activities of related topics