Jia Wang - US grants

Eicken 0712673
Univ. of Alaska - Fairbanks

Funds are provided to examine over 30 years of landfast ice records,
cyclone tracks and intensity along with frequency and timing of coastal high wind
conditions, nearshore pack ice drift, and coastal weather observations in two
representative arctic coastal regions. The focus of the project is to examine the relevant processes driving landfast ice responses to storm-produced coastal environmental change. To understand the physics that drive the dynamic and thermodynamic processes of landfast ice, existing coastal observations and numerical modeling will be included in a detailed process analysis.

The principal investigators will merge their various data sets and knowledge to address the following questions: (1) How do changes in the storm climate affect the coastal air temperatures and wind conditions? (2) How does landfast ice, including its stability and grounding patterns, respond to coastal winds and storm activity as well as nearshore ice motion and coastal currents? (3) What are the physical connections among those factors determining long-term variations of fast ice extent and duration? (4) What impacts on landfast ice are likely under a scenario of increased storm activity?

Landfast ice is a small fraction of the total ice cover of the Arctic Ocean, yet it is of extreme importance to the indigenous polpulation's way of life. It protects the beach from erosion during extended periods of time; it provides a surface over which to travel between villages by snowmobile and sled; it is the surface from which much subsistence hunting (seal, walrus, whale) takes place. Understanding how it is and will change has importance for day-to-day safety, as well as strategic management.

In this project, the PIs study secure computation outsourcing in cloud computing with the focus on widely applicable engineering computing and optimization problems. Their methodology is to explicitly decompose computations into public programs and private data and leverage the structures of specific computations for achieving desirable trade-offs among security, efficiency, and practicality.

The PIs propose to organize the mechanisms into a hierarchy where computation can be represented at various abstraction levels, and then explore a systematic methodology consisting of the following three methods: (1) problem transformations that encrypt the data such that the computation can be performed on the same abstraction level, (2) procedure transformations that leverage the mechanisms defined at a lower abstraction level as a subroutine for secure computation outsourcing, and (3) structural-preserving transformations that further improve the practical efficiency of mechanisms by maintaining favorable problem structures.

The PIs expect the outcomes of this research to be adopted by application developers, who will build applications to support secure computation outsourcing either privately for end-users within the same organization, or for public end-users resembling the practices of software-as-a-service (SaaS).

As the number of computer nodes increases, so does the size of the interconnect network. Historically, floating point was the most costly component of a system, but this is no longer the case. Systems today, and those anticipated in the future, are increasingly bound by their communication infrastructure and the power dissipation associated with data movement across the rapidly growing number of nodes. How to address the increasing cost of data movement on ever-growing systems is becoming critical.

This project will develop a framework named COTA, a COoperative framework for Topology Awareness. COTA will be an integrated framework that coordinates across the hardware, job scheduler, runtime, and application to jointly attack the increasing concern of data movement for communication- and power-efficiency on large-scale systems. Most importantly, the framework will support topology awareness not only at job startup, but also during job execution. The newly developed mapping algorithms, topology-aware methods and tools, and topology-aware models will provide a critical foundation for the realization of topology awareness on current and future systems. This research will have a direct impact on system productivity as well as a broad range of application domains that use parallel systems for simulations. The project will also enhance the curriculum at Illinois Institute of technology, broaden the participation by underrepresented groups, and outreach to the surrounding communities.