CUDA Parallel Programming: Difference between revisions
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<h2 align="center">BINGHAMTON UNIVERSITY</h2> |
<h2 align="center">BINGHAMTON UNIVERSITY</h2> |
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<h2 align="center">SPRING 2012</h2> |
<h2 align="center">SPRING 2012</h2> |
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<h3 align="left">Alper ALIMOGLU</h3 > |
<h3 align="left">Alper ALIMOGLU</h3 > |
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<h3 align="center">INDEX</h3 > |
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<h4 align="left">1.Introduction</h4> |
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<p> CUDA stands for Compute Unified Device Architecture and is a new hardware and software architecture for issuing and managing computations on the GPU as a data-parallel computing device without the need of mapping them to a graphics API. CUDA includes a programming model along with hardware support that simplifies parallel implementation. CUDA is one of the main programming languages that increase the speed of result more than any other languages. Programmers need training in parallel programming to be fully effective in computer science. CUDA forms a platform that contains both high-performance applications for heterogeneous platforms that contain both central and graphics processing units. Data-parallel processing maps data elements to parallel processing threads. Many applications that process large data sets such as arrays can use a data-parallel programming model to speed up the computations. In that case I aimed to use CUDA in order to do a helpful analyze on the medical area (bad-genes). As a first step I search a string under a 1 Mb of a text file under parallel programming. My aim was to observe how parallel programming might increase the performance of the process.</p> |
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<h4 align="left">2.Definition of SNP-Genes</h4> |
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