The application of artificial intelligence techniques to large distributed networks

Data accessibility and transfer of information, including the land resources information system pilot, are structured as large computer information networks. These pilot efforts include the reduction of the difficulty to find and use data, reducing processing costs, and minimize incompatibility between data sources. Artificial Intelligence (AI) techniques were suggested to achieve these goals. The applicability of certain AI techniques are explored in the context of distributed problem solving systems and the pilot land data system (PLDS). The topics discussed include: PLDS and its data processing requirements, expert systems and PLDS, distributed problem solving systems, AI problem solving paradigms, query processing, and distributed data bases

Exascale machines require new programming paradigms and runtimes

Extreme scale parallel computing systems will have tens of thousands of optionally accelerator-equiped nodes with hundreds of cores each, as well as deep memory hierarchies and complex interconnect topologies. Such Exascale systems will provide hardware parallelism at multiple levels and will be energy constrained. Their extreme scale and the rapidly deteriorating reliablity of their hardware components means that Exascale systems will exhibit low mean-time-between-failure values. Furthermore, existing programming models already require heroic programming and optimisation efforts to achieve high efficiency on current supercomputers. Invariably, these efforts are platform-specific and non-portable. In this paper we will explore the shortcomings of existing programming models and runtime systems for large scale computing systems. We then propose and discuss important features of programming paradigms and runtime system to deal with large scale computing systems with a special focus on data-intensive applications and resilience. Finally, we also discuss code sustainability issues and propose several software metrics that are of paramount importance for code development for large scale computing systems

New Algorithm for Clustering Distributed Data Using k-Means

The internet era and high speed networks have ushered in the capabilities to have ready access to large amounts of geographically distributed data. Individuals, businesses, and governments recognize the value of this available resource to those who can transform the data into information. These databases, though valuable as individual entities, become significantly more valuable when they function as parts of a federated database and their data can be aggregated for collective mining or computations. This requires new algorithms to shift their focus from working with single databases to efficiently working with federated databases. In this paper, we propose a new decomposable version of the popular k-means clustering algorithm that works in this desired manner with a set of networked databases. We show that it is possible to perform global computation in a reasonably secure manner for either horizontally or vertically distributed databases. The computation is completed by only exchanging a few local summaries among the databases. An empirical and analytical validation of our results is also presented

Performance Debugging and Tuning using an Instruction-Set Simulator

Instruction-set simulators allow programmers a detailed level of insight into, and control over, the execution of a program, including parallel programs and operating systems. In principle, instruction set simulation can model any target computer and gather any statistic. Furthermore, such simulators are usually portable, independent of compiler tools, and deterministic-allowing bugs to be recreated or measurements repeated. Though often viewed as being too slow for use as a general programming tool, in the last several years their performance has improved considerably. We describe SIMICS, an instruction set simulator of SPARC-based multiprocessors developed at SICS, in its rôle as a general programming tool. We discuss some of the benefits of using a tool such as SIMICS to support various tasks in software engineering, including debugging, testing, analysis, and performance tuning. We present in some detail two test cases, where we've used SimICS to support analysis and performance tuning of two applications, Penny and EQNTOTT. This work resulted in improved parallelism in, and understanding of, Penny, as well as a performance improvement for EQNTOTT of over a magnitude. We also present some early work on analyzing SPARC/Linux, demonstrating the ability of tools like SimICS to analyze operating systems

Making distributed computing infrastructures interoperable and accessible for e-scientists at the level of computational workflows

As distributed computing infrastructures evolve, and as their take up by user communities is growing, the importance of making different types of infrastructures based on a heterogeneous set of middleware interoperable is becoming crucial. This PhD submission, based on twenty scientific publications, presents a unique solution to the challenge of the seamless interoperation of distributed computing infrastructures at the level of workflows. The submission investigates workflow level interoperation inside a particular workflow system (intra-workflow interoperation), and also between different workflow solutions (inter-workflow interoperation). In both cases the interoperation of workflow component execution and the feeding of data into these components workflow components are considered. The invented and developed framework enables the execution of legacy applications and grid jobs and services on multiple grid systems, the feeding of data from heterogeneous file and data storage solutions to these workflow components, and the embedding of non-native workflows to a hosting meta-workflow. Moreover, the solution provides a high level user interface that enables e-scientist end-users to conveniently access the interoperable grid solutions without requiring them to study or understand the technical details of the underlying infrastructure. The candidate has also developed an application porting methodology that enables the systematic porting of applications to interoperable and interconnected grid infrastructures, and facilitates the exploitation of the above technical framework