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Green High Performance Computing (Green HPC@RU)



Green High Performance Computing (Green HPC@RU)



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. 

GreenHPC also acts as a forum for researchers and the educational community to exchange ideas and experiences on energy efficiency by disseminating research resultseducational activities at different levels (PhD, MS, undergraduate - REU, K12 - GSET) and organizing events and editorial activities of related topics.


Research Areas


  • Energy efficiency of scientific data analysis pipelines at scale 

    The goals of this effort are understanding power/performance behaviors and tradeoffs associated with data placement, data movement and data processing associated with data analytics pipelines on systems with emerging architectures and deep memory hierarchies, and to develop strategies that can fundamentally enable data-intensive workflows on current and future large-scale systems. Furthermore, it addresses energy/power-efficiency tradeoffs in a holistic manner in combination with performance, resilience, quality of solution, and other objectives. 

    Collaborators: Oak Ridge National Laboratory and Fusion-io.

  • In-situ data analytics and co-processing at extreme scales 

    This research effort focuses on developing new formulations and analysis strategies to support the increasing volumes and rates at which scientific simulations running at extreme scale generate data, which needs to be transported and analyzed before scientific discovery can be realized. Its overarching goal is exploring data-related energy/performance trade-offs at extreme scales. Specifically, it aims at analyzing the behavior of large-scale simulation workflows with an in-situ and other data analytics pipelines, running on a current high-end computing platform and beyond to develop performance and power models, which can be validated using an instrumented platform. Models can be used then to explore energy/performance tradeoffs on current systems, to help answer system design questions, and to analyze the power requirements and usage modes for emerging architectures such as the Intel Many Integrated Core (MIC) architecture. 

    Collaborators: Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, Pacific Northwest National Laboratory, Oak Ridge National Laboratory and University of Utah.

  • Application-aware cross-layer power management for High Performance Computing systems 

    The goals of this effort are investigating aggressive power management strategies and their impact on the overall energy consumption and developing autonomic advanced runtimes to improve the energy efficiency of High Performance Computing systems, datacenters and platforms based on many-core architectures such as the Intel Single-chip Cloud Computer (SCC). It investigates proactive component-based power management and cross-layer interactions using PGAS language extensions and runtime mechanisms that can be used to achieve a wide range of energy and performance behaviors and manipulate power/performance tradeoffs 

    Collaborators: Oak Ridge National Laboratory and Intel.


Keywords: Energy Efficiency; Application-Awareness; High Performance Computing; Power Management; Cross-layer; Data Management; Deep Memory Hierarchies; Accelerators




The research at GreenHPC is sponsored by the National Science Foundation, the Department of Energy and members of the Rutgers Discovery Informatics Institute and the Cloud and Autonomic Computing Center.