Tuesday Keynote I: The Next Generation of High Performance Computing
    William D. Gropp, University of Illinois Urbana-Champaign, Illinois, USA

Tuesday Keynote II: Practical Distribution of Random Streams for Stochastic High Performance Computing: Application to Life Sciences
    David Hill, Blaise Pascal University, France

Wednesday Keynote I: On Cyber-Physical Systems Challenges and Research Opportunities
    Taieb Znati, University of Pittsburgh and National Science Foundation, USA

Wednesday Keynote II: Building the European e-Infrastructure Ecosystem for Data Intensive Science
    Robert Jones, EGEE-I and EGEE-II Projects Director, CERN, Geneva - Switzerland

Thursday Keynote I: Data-driven Modeling and Design of Networked Mobile Societies: A Paradigm Shift for Future Social Networking
    Ahmed Helmy, University of Florida, Florida, USA

Thursday Keynote II: Distributed Knowledge Discovery Services in Grids and Clouds
    Domenico Talia, Università della Calabria and ICAR-CNR, Italy

Tuesday Keynote I: The Next Generation of High Performance Computing
    William D. Gropp, University of Illinois Urbana-Champaign, Illinois, USA

William D. Gropp photo

Clusters revolutionized computing by making supercomputer capabilities widely available.  But one of the main drivers of that revolution, the rapid doubling of processor clock rates, ran out of steam several years ago.  To maintain (or even increase) the historic rate of improvement in computing power, processor designs are rapidly increasing parallelism at all levels, including more functional units, more cores, and ways to share resources among threads.  Heterogeneous designs that use more specialized processors such as GPGPUs are becoming common.  The scale of high-end systems is also getting larger, with 1000-core systems becoming commonplace and systems with over 300,000 cores becoming operational in 2011.  However, the software and algorithms for these systems are still basically the same as when the cluster revolution began.  Drawing on experiences with the sustained PetaFLOPS system, called Blue Waters, to be installed at Illinois in 2011, and with exploratory work into Exascale system designs, this talk will discuss some of the challenges facing the high performance and cluster community as scalability becomes increasingly important and reviews some of the developments in algorithms, programming models, and software frameworks that must complement the evolution of high performance computing hardware.

Tuesday Keynote IIPractical Distribution of Random Streams for Stochastic High Performance Computing:  Application to Life Sciences
    David Hill , Blaise Pascal University, France

David Hill photo

The purpose of this talk is to present the best practical approaches used to distribute random streams in the context of Stochastic High Performance Computing.  The first part will give a short presentation dealing with the differences between various paradigms: Grid Computing, Internet Computing and Cloud Computing, Peer-To-Peer, Cluster Computing, Meta Computing, Parasitic computing and Hybrid Computing.  Then, in a second part, the focus will be given to the distribution of stochastic applications and to the distribution of simulation experiments (whether stochastic or not).  The best techniques in use to distribute pseudo random numbers will be presented as well as future research directions.  A highlight will be given to optimization techniques and real applications on ecological modelling and medical projects will be presented.  The case of successful large-scale simulations and data challenges on the EGEE Grid will also be exposed.

Wednesday Keynote IOn Cyber-Physical Systems Challenges and Research Opportunities
    Taieb Znati , University of Pittsburgh and National Science Foundation, USA

Taieb Znati photo

Unprecedented advances in wireless and mobile technologies, coupled with the proliferation of social networks and applications, is paving the way for a new era of cyber-physical systems that will revolutionize the way humans interact with their physical environment. Cyber-physical systems exhibit deeply integrated computational and physical capabilities, interacting with humans through diverse modalities. The ability to interact with and expand capabilities of the physical world through computational means is the key technological multiplier. Realizing the vision of cyber-physical systems, however, brings about unprecedented challenges, technical and non-technical, underscoring the need for radical thinking and new insights into how we design and deploy distributed cyber-physical systems, on which our lives and critical sectors of our society can depend. These systems are likely to exhibit complex dynamics at different spatial and temporal scales, and various levels of control. They need to be predictive, reactive to conditions and external events, and receptive to coordination and negotiation. They also need to be fault tolerant and recoverable, satisfying potentially very high availability and timeliness requirements. As critical as these physical and cyber infrastructures are to our lives and diverse sectors of our society, we have little rigorous knowledge for understanding their structure and dynamics. The talk will discuss challenges and future research directions in how to effectively design robust and secure large-scale complex systems, so that we can engineer them to have predictable behaviors.

Wednesday Keynote IIBuilding the European e-Infrastructure Ecosystem for Data Intensive Science
    Robert Jones , EGEE-I and EGEE-II Projects Director, CERN, Geneva - Switzerland

Robert Jones photo

The importance of research data for modern science is growing daily, and new initiatives are been required to cope with the resulting "data deluge". The emergence of big and complex-data science is here to stay.  It will open completely new ways to dig knowledge out of the huge amounts of information that are becoming available across a range of research from astronomy to archaeology, and physics to epidemiology. Incorporating e-Science digital repositories and their holdings into an open information ecosystem will help support new scientific methods and paradigms, improving both the efficiency of the scientific process and its impact.

Existing Pan-European computing infrastructures, including high-speed networking (GEANT), high-capacity grid systems (EGEE/EGI) and specialised high-performance computing centres (DEISA/PRACE) serve a significant proportion of Europe's research community.

By considering these developments, this talk will suggest some opportunities and challenges for how e-infrastructures can evolve in the future to address the challenges facing Europe's research communities.

Thursday Keynote IData-driven Modeling and Design of Networked Mobile Societies: A Paradigm Shift for Future Social Networking
    Ahmed Helmy , University of Florida, Florida, USA

Ahmed Helmy photo

The future of social networking is in the mobile world. Future network services are expected to center around human activity and behavior. Wireless networks (including ad hoc, sensor networks and DTNs) are expected to grow significantly and accommodate higher levels of mobility and interaction. In such a highly dynamic environment, networks need to adapt efficiently (performance-wise) and gracefully (correctness and functionality-wise) to growth and dynamics in many dimensions, including behavioral and mobility patterns, on-line activity and load. Understanding and realistically modeling this multi-dimensional space is essential to the design and evaluation of efficient protocols and services of the future Internet.

This level of understanding to drive the modeling and protocol design shall be developed using data-driven paradigm. The design philosophy for the proposed paradigm is unique in that it begins by intensive analysis of measurements from the target contexts, which then drive the modeling, protocol and service design through a systematic framework, called TRACE. Components of TRACE include: 1. Tracing and monitoring of behavior, 2. Representing and Analyzing the data, 3. Characterizing behavioral profiles using data mining and clustering techniques, and finally 4. Employing the understanding and insight attained into developing realistic models of mobile user behavior, and designing efficient protocols and services for future mobile societies.

Tracing at a large scale represents the next frontier for sensor networks (sensing the human society). Our latest progress in that field (MobiLib) shall be presented, along with data mining and machine learning tools to meaningfully analyze the data. Several challenges will be presented and novel use of clustering algorithms will be provided. Major contributions to modeling of human mobility (the time variant community model, TVC) will also be discussed.

Finally, insights developed through analysis, mining and modeling will be utilized to introduce and design a novel communication paradigm, called profile-cast, to support new classes of service for interest-aware routing and dissemination of information, queries and resource discovery, trust and participatory sensing (crowd sourcing) in future mobile networks. Unlike conventional - unicast, multicast or directory based - paradigms, the proposed paradigm infers user interest using implicit behavioral profiling via self-monitoring and mining techniques. In order to capture interest, a spatio-temporal representation is introduced to capture users behavioral-space. Users can then identify similarity of interest based on their position in such space.

The proposed profile-cast paradigm will act as enabler to new classes of service, ranging from mobile social networking, and navigation of mobile societies and spaces, to emergency alerts and disaster relief using infrastructure-less networks, among others.

Thursday Keynote IIDistributed Knowledge Discovery Services in Grids and Clouds
    Domenico Talia ,  Università della Calabria and ICAR-CNR, Italy

Domenico Talia photo

Grid and Cloud computing systems were originally designed for dealing with problems involving compute-intensive tasks. Today, however, Grids and Clouds enlarged their horizon as they are going to run both scientific and business applications using large data sources for scientists, professionals and end users. To face those new challenges, such distributed computing environments must support adaptive knowledge discovery and data mining applications by offering resources, services, and decentralized data analysis methods. In particular, according to the service oriented architecture (SOA) model, data mining tasks and knowledge discovery processes can be delivered as services in Grid and Cloud computing infrastructures.

Through a service-based approach we can define integrated services for supporting distributed scientific data analysis tasks in Grids and Clouds. Those services can address all the tasks that must be considered in knowledge discovery processes from data selection and transport, to data analysis, knowledge model representation and visualization. We are working  along this direction by providing service-oriented architectures and services for distributed knowledge discovery.

This collection of data mining services composes an Open Service Framework for Distributed Knowledge Discovery. This framework allows developers to design distributed KDD processes as a composition of services that are available over HPC computers and large scale distributed infrastructures (e.g., from a single Cloud to Interclouds).

Here we describe a strategy based on the use of services for the design of open distributed knowledge discovery services and outline how Grid and Cloud application frameworks can be developed as a collection of services.


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