Analytical Modeling of High Performance Reconfigurable Computers: Prediction and Analysis of System Performance.

The use of a network of shared, heterogeneous workstations each harboring a Reconfigurable Computing (RC) system offers high performance users an inexpensive platform for a wide range of computationally demanding problems. However, effectively using the full potential of these systems can be challenging without the knowledge of the system’s performance characteristics. While some performance models exist for shared, heterogeneous workstations, none thus far account for the addition of Reconfigurable Computing systems. This dissertation develops and validates an analytic performance modeling methodology for a class of fork-join algorithms executing on a High Performance Reconfigurable Computing (HPRC) platform. The model includes the effects of the reconfigurable device, application load imbalance, background user load, basic message passing communication, and processor heterogeneity. Three fork-join class of applications, a Boolean Satisfiability Solver, a Matrix-Vector Multiplication algorithm, and an Advanced Encryption Standard algorithm are used to validate the model with homogeneous and simulated heterogeneous workstations. A synthetic load is used to validate the model under various loading conditions including simulating heterogeneity by making some workstations appear slower than others by the use of background loading. The performance modeling methodology proves to be accurate in characterizing the effects of reconfigurable devices, application load imbalance, background user load and heterogeneity for applications running on shared, homogeneous and heterogeneous HPRC resources. The model error in all cases was found to be less than five percent for application runtimes greater than thirty seconds and less than fifteen percent for runtimes less than thirty seconds. The performance modeling methodology enables us to characterize applications running on shared HPRC resources. Cost functions are used to impose system usage policies and the results of vii the modeling methodology are utilized to find the optimal (or near-optimal) set of workstations to use for a given application. The usage policies investigated include determining the computational costs for the workstations and balancing the priority of the background user load with the parallel application. The applications studied fall within the Master-Worker paradigm and are well suited for a grid computing approach. A method for using NetSolve, a grid middleware, with the model and cost functions is introduced whereby users can produce optimal workstation sets and schedules for Master-Worker applications running on shared HPRC resources

Parallel matrix multiplication on heterogeneous networks of workstations

Matrix multiplication is taken as a test bed for parallel processing on heterogeneous networks of workstations (local area networks) used as parallel machines. Two algorithms are proposed taking into account the specific kind of parallel hardware provided by local area networks, and experimentation is used to drive the evaluation and identification of possible performance loss. A specific broadcast communication between processes of a parallel application is also proposed, taking advantage of the Ethernet interconnection network to achieve optimized performance. A special emphasis is place on already installed networks of workstations, which provide a hardware zero cost parallel computer; but a homogeneous Beowulf-class system is used to show how the algorithms are also useful on current classical high performance parallel computing with clusters.Eje: LenguajesRed de Universidades con Carreras en Informática (RedUNCI

REMOTE DETECTION OF EPHEMERAL WETLANDS IN MID- ATLANTIC COASTAL PLAIN ECOREGIONS: LIDAR AND HIGH-THROUGHPUT COMPUTING

Ephemeral wetlands are ecologically important freshwater ecosystems that occur frequently throughout the Atlantic coastal plain ecoregions of North America. Despite the growing consensus of their importance and imperilment, these systems historically have not been a national conservation priority. They are often cryptic on the landscape and methods to detect ephemeral wetlands remotely have been ineffective at the landscape scales necessary for conservation planning and resource management. Therefore, this study fills information gaps by employing high-resolution light detection and ranging (LiDAR) data to create local relief models that elucidate small localized changes in concavity. Relief models were then processed with local indicators of spatial association (LISA) in order to automate their detection by measuring autocorrelation among model indices. Following model development and data processing, field validation of 114 predicted wetland locations was conducted using a random stratified design proportional to landcover, to measure model commission (α) and omission (β) error rates. Wetland locations were correctly predicted at 85% of visited sites with α error rate = 15% and β error rate = 5%. These results suggest that devised local relief models captured small geomorphologic changes that successfully predict ephemeral wetland boundaries in low-relief ecosystems. Small wetlands are often centers of biodiversity in forested landscapes and this analysis will facilitate their detection, the first step towards long-term management

Evaluation of a distributed numerical simulation optimization approach applied to aquifer remediation

AbstractIn this paper we evaluate a distributed approach which uses numerical simulation and optimization techniques to automatically find remediation solutions to a hypothetical contaminated aquifer. The repeated execution of the numerical simulation model of the aquifer through the optimization cycles tends to be computationally expensive. To overcome this drawback, the numerical simulations are executed in parallel using a network of heterogeneous workstations. Performance metrics for heterogeneous environments are not trivial; a new way of calculating speedup and efficiency for Bag-of-Tasks (BoT) applications is proposed. The performance of the parallel approach is evaluated

An Architectural Framework for Performance Analysis: Supporting the Design, Configuration, and Control of DIS /HLA Simulations

Technology advances are providing greater capabilities for most distributed computing environments. However, the advances in capabilities are paralleled by progressively increasing amounts of system complexity. In many instances, this complexity can lead to a lack of understanding regarding bottlenecks in run-time performance of distributed applications. This is especially true in the domain of distributed simulations where a myriad of enabling technologies are used as building blocks to provide large-scale, geographically disperse, dynamic virtual worlds. Persons responsible for the design, configuration, and control of distributed simulations need to understand the impact of decisions made regarding the allocation and use of the logical and physical resources that comprise a distributed simulation environment and how they effect run-time performance. Distributed Interactive Simulation (DIS) and High Level Architecture (HLA) simulation applications historically provide some of the most demanding distributed computing environments in terms of performance, and as such have a justified need for performance information sufficient to support decision-makers trying to improve system behavior. This research addresses two fundamental questions: (1) Is there an analysis framework suitable for characterizing DIS and HLA simulation performance? and (2) what kind of mechanism can be used to adequately monitor, measure, and collect performance data to support different performance analysis objectives for DIS and HLA simulations? This thesis presents a unified, architectural framework for DIS and HLA simulations, provides details on a performance monitoring system, and shows its effectiveness through a series of use cases that include practical applications of the framework to support real-world U.S. Department of Defense (DoD) programs. The thesis also discusses the robustness of the constructed framework and its applicability to performance analysis of more general distributed computing applications

A summary of research in system software and concurrency at the University of Malta : I/O and communication

Traditional operating systems and commodity hardware are never used to their full potential due to underlying design limitations. Applications that make use of blocking system calls incur large overheads on the operating systems and in turn end up wasting CPU resources. In addition, traditional solutions are not adequate for high-performance networking. In this report, we present a summary of the research conducted by the System Software Research Group (SSRG) at the University of Malta. We discuss some of the solutions we have developed and pinpoint their effectiveness to solve each of the above problems.peer-reviewe

Parallel Computing for Probabilistic Response Analysis of High Temperature Composites

The objective of this Phase I research was to establish the required software and hardware strategies to achieve large scale parallelism in solving PCM problems. To meet this objective, several investigations were conducted. First, we identified the multiple levels of parallelism in PCM and the computational strategies to exploit these parallelisms. Next, several software and hardware efficiency investigations were conducted. These involved the use of three different parallel programming paradigms and solution of two example problems on both a shared-memory multiprocessor and a distributed-memory network of workstations