Scalable Programming for the Analysis of Aphasia Transcripts

Technologies designed for individuals with nonfluent aphasia focus on digitizing speech therapy methods and generating speech. To improve these technologies, the language characteristics of individuals with non- fluent aphasia must be further understood. Language corpuses, such as the AphasiaBank, provide a promising solution for informing technology usability in terms of navigation, interface, and content decisions. As a tool for informing such work, this research investigates the viability of a flexible and scalable multi-threaded software program for the analysis of AphasiaBank transcripts. Results show that the program allows rapid analysis of all transcriptions by optimizing core functionality and minimizing the number of areas for synchronization. This research aims to improve the access to information, products, and services in technology for individuals with non-fluent aphasia

Multi-core computation of transfer matrices for strip lattices in the Potts model

The transfer-matrix technique is a convenient way for studying strip lattices in the Potts model since the compu- tational costs depend just on the periodic part of the lattice and not on the whole. However, even when the cost is reduced, the transfer-matrix technique is still an NP-hard problem since the time T(|V|, |E|) needed to compute the matrix grows ex- ponentially as a function of the graph width. In this work, we present a parallel transfer-matrix implementation that scales performance under multi-core architectures. The construction of the matrix is based on several repetitions of the deletion- contraction technique, allowing parallelism suitable to multi-core machines. Our experimental results show that the multi-core implementation achieves speedups of 3.7X with p = 4 processors and 5.7X with p = 8. The efficiency of the implementation lies between 60% and 95%, achieving the best balance of speedup and efficiency at p = 4 processors for actual multi-core architectures. The algorithm also takes advantage of the lattice symmetry, making the transfer matrix computation to run up to 2X faster than its non-symmetric counterpart and use up to a quarter of the original space

RTL implementation of one-sided jacobi algorithm for singular value decomposition

Multi-dimensional digital signal processing such as image processing and image reconstruction involve manipulating of matrix data. Better quality images involve large amount of data, which result in unacceptably slow computation. A parallel processing scheme is a possible solution to solve this problem. This project presented an analysis and comparison to various algorithms for widely used matrix decomposition techniques and various computer architectures. As the result, a parallel implementation of one-sided Jacobi algorithm for computing singular value decomposition (SVD) of a 2х2 matrix on field programmable gate arrays (FPGA) is developed. The proposed SVD design is based on pipelined-datapath architecture The design process is started by evaluating the algorithm using Matlab, design datapath unit and control unit, coding in SystemVerilog HDL, verification and synthesis using Quartus II and simulated on ModelSim-Altera. The original matrix size of 4x4 and 8x8 is used to with the SVD processing element (PE). The result are compared with the Matlab version of the algorithm to evaluate the PE. The computation of SVD can be speed-up of more than 2 by increasing the number of PE at the cost of increased in circuit area

Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems

The optimum penetration of distributed generations into the distribution grid provides several technical and economic benefits. However, the computational time required to solve the constrained optimization problems increases with the increasing network scale and may be too long for online implementations. This paper presents a parallel solution of a multi-objective distributed generation (DG) allocation and sizing problem to handle a large number of computations. The aim is to find the optimum number of processors in addition to energy loss and DG cost minimization. The proposed formulation is applied to a 33-bus test system, and the results are compared with themselves and with the base case operating conditions using the optimal values and three popular multi-objective optimization metrics. The results show that comparable solutions with high-efficiency values can be obtained up to a certain number of processors

Using utilization profiles in allocation and partitioning for multiprocessor systems

Journal ArticleThe problems of multiprocessor partitioning and program allocation are interdependent and critical to the performance of multiprocessor systems. Minimizing resource partitions for parallel programs on partitionable multiprocessors facilitates greater processor utilization and throughput. The processing resource requirements of parallel programs vary during program, execution and are allocation dependent. Optimal resource utilization requires that resource requirements be modeled as variable over time. This paper investigates the use of program profiles in allocating programs and partitioning multiprocessor systems. An allocation method is discussed. The goals of this method are to (1) minimize program execution time, (2) minimize t h e total number of processors used, (3) characterize variation in processor requirements over the lifetime of a program, (4) to accurately predict the impact on run time of the number of processors available at any point in time and (5) to minimize fluctuations in processor requirements to facilitate efficient sharing of processors between partitions on a partitionable multiprocessor. An application to program partitioning is discussed that improves partition run times compared to other methods

Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Advances in sensor technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms remotely sensed data are collected, stored and processed. This exponential growth of data archives and the increasing user’s demand for real-and near-real time remote sensing data products has pressurized remote sensing service providers to deliver the required services. The remote sensing community has recognized the challenge in processing large and complex satellite datasets to derive customized products. To address this high demand in computational resources, several efforts have been made in the past few years towards incorporation of high-performance computing models in remote sensing data collection, management and analysis. This study adds an impetus to these efforts by introducing the recent advancements in distributed computing technologies, MapReduce programming paradigm, to the area of remote sensing. The MapReduce model which is developed by Google Inc. encapsulates the efforts of distributed computing in a highly simplified single library. This simple but powerful programming model can provide us distributed environment without having deep knowledge of parallel programming. This thesis presents a MapReduce based processing of large satellite images a use case scenario of edge detection methods. Deriving from the conceptual massive remote sensing image processing applications, a prototype of edge detection methods was implemented on MapReduce framework using its open-source implementation, the Apache Hadoop environment. The experiences of the implementation of the MapReduce model of Sobel, Laplacian, and Canny edge detection methods are presented. This thesis also presents the results of the evaluation the effect of parallelization using MapReduce on the quality of the output and the execution time performance tests conducted based on various performance metrics. The MapReduce algorithms were executed on a test environment on heterogeneous cluster that supports the Apache Hadoop open-source software. The successful implementation of the MapReduce algorithms on a distributed environment demonstrates that MapReduce has a great potential for scaling large-scale remotely sensed images processing and perform more complex geospatial problems