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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Accelerating ggplot2 based projection on r-map using NVIDIA GPU

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    12201051 & 12201036_CSE.pdf (646.7Kb)
    Date
    2016-08-17
    Publisher
    BRAC University
    Author
    Islam, Md. Zahidul
    Elahi, Md. Tausif
    Metadata
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    URI
    http://hdl.handle.net/10361/6398
    Abstract
    With an increasing amount of user and data demands for fast data processing, the optimization of database operations continues to be a challenging work. A common optimization technique is to leverage parallel hardware architectures. With the introduction of general-purpose GPU computing, massively parallel hardware has become available within commodity hardware. To efficiently exploit this technology, we introduce the method of speculative query processing. Moreover, as the dataset grows increasingly larger, multiple-thread spatial query sometimes cannot meet the performance requirement. The concept of GPU-accelerated parallel computing turns the massive computational power of a modern graphics accelerator's shader pipeline into general-purpose computing power, as opposed to being hard wired solely to do graphical operations. In certain applications requiring massive vector operations, this can yield several orders of magnitude higher performance than a conventional CPU. R is a free software environment for graphics and statistical computing that provides a programming language and built-in libraries of mathematics operations for data analysis, statistics, machine learning and much more. R programs tend to process large amounts of data, and often have significant independent data and task parallelism. Therefore, R applications stand to benefit from GPU acceleration. This way, R users can benefit from R’s high-level, user-friendly interface while achieving high performance. Thus focusing on accelerating R computations using CUDA libraries by calling our own parallel algorithms written in CUDA from R and profiling GPU-accelerated R applications using the CUDA Profiler.
    Keywords
    Matrix transpose; Simple mapping
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 44-45).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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