Data-parallel computation in parallel and distributed environments

The past few decades have seen a considerable increase in the number of parallel and distributed systems. With the development of more complex applications, the need for more powerful systems has emerged and various parallel and distributed environments have been designed and implemented. Each of the environments, including hardware and software, has unique strengths and weaknesses. There is no single parallel environment that can be identified as the best environment for all applications with respect to hardware and software properties. The main goal of this thesis is to provide a novel way of performing data-parallel computation in parallel and distributed environments by utilizing the best characteristics of difference aspects of parallel computing. For the purpose of this thesis, three aspects of parallel computing were identified and studied. First, three parallel environments (shared memory, distributed memory, and a network of workstations) are evaluated to quantify theirsuitability for different parallel applications. Due to the parallel and distributed nature of the environments, networks connecting the processors in these environments were investigated with respect to their performance characteristics. Second, scheduling algorithms are studied in order to make them more efficient and effective. A concept of application-specific information scheduling is introduced. The application- specific information is data about the workload extractedfrom an application, which is provided to a scheduling algorithm. Three scheduling algorithms are enhanced to utilize the application-specific information to further refine their scheduling properties. A more accurate description of the workload is especially important in cases where the workunits are heterogeneous and the parallel environment is heterogeneous and/or non-dedicated. The results obtained show that the additional information regarding the workload has a positive impact on the performance of applications. Third, a programming paradigm for networks of symmetric multiprocessor (SMP) workstations is introduced. The MPIT programming paradigm incorporates the Message Passing Interface (MPI) with threads to provide a methodology to write parallel applications that efficiently utilize the available resources and minimize the overhead. The MPIT allows for communication and computation to overlap by deploying a dedicated thread for communication. Furthermore, the programming paradigm implements an application-specific scheduling algorithm. The scheduling algorithm is executed by the communication thread. Thus, the scheduling does not affect the execution of the parallel application. Performance results achieved from the MPIT show that considerable improvements over conventional MPI applications are achieved.

A framework for the development of parallel and distributed real-time embedded systems

Embedded real-time applications increasingly present high computation requirements, which need to be completed within specific deadlines, but that present highly variable patterns, depending on the set of data available in a determined instant. The current trend to provide parallel processing in the embedded domain allows providing higher processing power; however, it does not address the variability in the processing pattern. Dimensioning each device for its worst-case scenario implies lower average utilization, and increased available, but unusable, processing in the overall system. A solution for this problem is to extend the parallel execution of the applications, allowing networked nodes to distribute the workload, on peak situations, to neighbour nodes. In this context, this report proposes a framework to develop parallel and distributed real-time embedded applications, transparently using OpenMP and Message Passing Interface (MPI), within a programming model based on OpenMP. The technical report also devises an integrated timing model, which enables the structured reasoning on the timing behaviour of these hybrid architectures.

Parallel and distributed computing with Java

The Java language first came to public attention in 1995. Within a year, it was being speculated that Java may be a good language for parallel and distributed computing. Its core features, including being objected oriented and platform independence, as well as having built-in network support and threads, has encouraged this view. Today, Java is being used in almost every type of computer-based system, ranging from sensor networks to high performance computing platforms, and from enterprise applications through to complex research-based.simulations. In this paper the key features that make Java a good language for parallel and distributed computing are first discussed. Two Java-based middleware systems, namely MPJ Express, an MPI-like Java messaging system, and Tycho, a wide-area asynchronous messaging framework with an integrated virtual registry are then discussed. The paper concludes by highlighting the advantages of using Java as middleware to support distributed applications.

An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

A debugging engine for parallel and distributed programs

Dissertação apresentada para a obtenção do Grau de Doutor em Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

Parallel and Distributed Data Management. Introduction

The manipulation and handling of an ever increasing volume of data by current data-intensive applications require novel techniques for e?cient data management. Despite recent advances in every aspect of data management (storage, access, querying, analysis, mining), future applications are expected to scale to even higher degrees, not only in terms of volumes of data handled but also in terms of users and resources, often making use of multiple, pre-existing autonomous, distributed or heterogeneous resources.

Education Complex "Multi-agent Technologies for Parallel and Distributed Information Processing in Telecommunication Networks"

The paper describes education complex "Multi-agent Technologies for Parallel and Distributed Information Processing in Telecommunication Networks".

Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

ACKNOWLEDGEMENTS This research is based upon work supported in part by the U.S. ARL and U.K. Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the U.S. ARL, the U.S. Government, the U.K. Ministry of Defense or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

ACKNOWLEDGEMENTS This research is based upon work supported in part by the U.S. ARL and U.K. Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the U.S. ARL, the U.S. Government, the U.K. Ministry of Defense or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

A single-code software model for multiphysics analysis-engine on parallel and distributed computers with the PHYSICA toolkit

Abstract not available

Using attributed variables in the implementation of concurrent and parallel logic programming systems

Incorporating the possibility of attaching attributes to variables in a logic programming system has been shown to allow the addition of general constraint solving capabilities to it. This approach is very attractive in that by adding a few primitives any logic programming system can be turned into a generic constraint logic programming system in which constraint solving can be user deñned, and at source level - an extreme example of the "glass box" approach. In this paper we propose a different and novel use for the concept of attributed variables: developing a generic parallel/concurrent (constraint) logic programming system, using the same "glass box" flavor. We argüe that a system which implements attributed variables and a few additional primitives can be easily customized at source level to implement many of the languages and execution models of parallelism and concurrency currently proposed, in both shared memory and distributed systems. We illustrate this through examples and report on an implementation of our ideas.