Parallel Programming using CUDA

Parallel Programming for Everyone

Why CUDA

 
The era of parallel programming has already arrived. With the advent of multicore processors, such as Core2Duo, Quad Core etc., the softwares should now be written so as to exploit these resources (namely cores) as much as possible. The customers are demanding more and more exciting applications on their PCs, Laptops and on their portable gadgets. The users want better GUI (Graphics User Interface), HD quality video, faster virus scanners, real time network security systems, better realism in video games and faster access to data base. Moreover, the engineering and scientific community is, for example looking for deeper insights into the biological cells at molecular level. At such level microscopes are of no use, and thus only simulations involving calculations with hundreds of GFLOPS (Giga Floating Point Operations Per Second) can give valuable information. Thus there is a great pressure on the application designers to develop applications, (graphical or non-graphical i.e general) which should run many times faster than the present applications. The trend is such that today’s supercomputing applications will be tomorrow’s exciting applications, demanding more and more computational power. This is the reason why engineers, scientists and software developers across the globe are switching to parallel programming, writing code that executes simultaneously on multiple cores, in a multi core processor.      

Thanks to hundreds of cores in NVIDIA's modern GPUs and software architecture CUDA the world's first C compiler for GPUs, one can think of exploiting these valuable resources (i.e 100s of processor cores) and develop applications (graphical or non-graphical) that run 100 times and even faster. Thus putting the smile on the customer’s face by accelerating the applications such as virus scanners, video games, Image processing tools, network security systems, video editing tools and scientific simulations etc.

Above all the compatibility with the C programming language turns the learning curve very steep, and the hardware abstraction provided by CUDA makes the programmer’s life easier than ever before. The programmer need not aware of the graphics APIs (e.g, OpenGL) and can use C programming language to launch thousands of threads running in parallel on hundred’s of cores.          

The speed of the GPU is increasing at a a much higher rate as compared to the CPU (see below) making the GPUs as a co-processor for handling large number of calculations per second demanded by the customers. 

That is why NVIDIA says that CUDA would be the jazziest thing to possess in 2010.  

 
 

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