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dc.contributor.advisorUddin, Dr. Jia
dc.contributor.authorIslam, Md. Zahidul
dc.contributor.authorElahi, Md. Tausif
dc.date.accessioned2016-09-08T09:38:35Z
dc.date.available2016-09-08T09:38:35Z
dc.date.copyright2016
dc.date.issued2016-08-17
dc.identifier.otherID 12201051
dc.identifier.otherID 12201036
dc.identifier.urihttp://hdl.handle.net/10361/6398
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 44-45).
dc.description.abstractWith 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.en_US
dc.description.statementofresponsibilityMd. Zahidul Islam
dc.description.statementofresponsibilityMd. Tausif Elahi
dc.format.extent47 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectMatrix transposeen_US
dc.subjectSimple mappingen_US
dc.titleAccelerating ggplot2 based projection on r-map using NVIDIA GPUen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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