CUDA Parallel Programming: Difference between revisions

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 <h4 align="left">1.Introduction</h4>
+<hr>
-<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.
-<hr>
 <h4 align="left">2.Definition of SNP-Genes</h4>