NVIDIA giving a makeover to its CUDA parallel computing platform
Modern GPUs such as NVIDIA Ge-Force series are increasingly designed as massively parallel programmable processors. New architecture interfaces have alleviated the need for numerical application developers to deal with graphics programming and interfaces. For example CUDA programmers simply treat the GeForce 8800 GTX as a computing processor that consists of 128 processor cores, has a peak performance of 367 single-precision GFLOPS, contains 768 MB of main memory, incurs very little cost in creating thousands of threads, allows efficient data sharing and synchronization across subsets of threads. With millions of units already in use, it has also become arguably the largest installation of massively parallel system in history. In the coming decade, we are going to see continued performance scaling in this type of massively parallel computing engines. Such a dramatic increase in computation power will likely enable revolutionary work in science, engineering and any other disciplines. Like any other massively parallel computer systems, in order to achieve high performance, an application programmer currently has to understand the desirable parallel programming idioms, potential performance pitfalls and proven coding strategies for the platform. However, the programming and code optimization models of GPU computing design are quite different from those of traditional CPUs.
The computer graphics tutorials which describe this vision also focuses on building an infrastructure of programming tools, educational materials and architectural directions needed for application developers. A computer graphics tutorial also facilitates the application developers to fully exploit the hardware computer power of current and future GPU computing platform. Generally the developers at scientific companies look to GPU computing for speeding up applications for scientific and engineering computing. With this approach, GPU accelerated applications run the sequential part of their workload on the GPU while accelerating parallel processing GPU. The latest CUDA news states that the revised CUDA parallel computing platform carries three main changes that are supposed to make parallel programming with GPUs easier and faster.
When announced it was regarded as the world’s first solution for general computing on GPUs. It is one of the most reckoning force in the mobile graphics technology. It has also given a new meaning to this field. It can be proved by the looking at the latest about mobile graphics. In the consumer market, nearly every major consumer video application has been or will soon to be, accelerated by CUDA. These include products from Adobe, Sony, Elemental Technologies, MotionDSP and LoiLo, according to NVIDIA. It has dressed up this particular parallel computing platform. It has announced the launch of an online platform dedicated to GPU computing and high performances computing users in India. The informal special interest group will bring together GPU users from all fields and experience levels in India.
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