Designing Scientific Applications on GPUs by Raphael Couturier

By Raphael Couturier

Many of today’s advanced medical purposes now require an unlimited quantity of computational energy. common goal photographs processing devices (GPGPUs) permit researchers in quite a few fields to profit from the computational strength of the entire cores on hand within photos cards.

Understand some great benefits of utilizing GPUs for plenty of medical Applications

Designing clinical functions on GPUs exhibits you the way to take advantage of GPUs for purposes in diversified clinical fields, from physics and arithmetic to desktop technological know-how. The e-book explains the tools useful for designing or porting your medical program on GPUs. it is going to increase your wisdom approximately snapshot processing, numerical functions, technique to layout effective functions, optimization equipment, and masses more.

Everything you want to Design/Port Your medical software on GPUs

The first a part of the e-book introduces the GPUs and Nvidia’s CUDA programming version, at the moment the main common surroundings for designing GPU purposes. the second one half specializes in major photograph processing purposes on GPUs. The 3rd half offers normal methodologies for software program improvement on GPUs and the fourth half describes using GPUs for addressing a number of optimization difficulties. The 5th half covers many numerical functions, together with difficulty difficulties, fluid simulation, and atomic physics types. The final half illustrates agent-based simulations, pseudorandom quantity iteration, and the answer of enormous sparse linear platforms for integer factorization. a few of the codes provided within the ebook can be found online.

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Extra resources for Designing Scientific Applications on GPUs

Sample text

NVIDIA GPU tuning recipes . . . . . . . . . . . . . . . . . . A 3×3 median filter: using registers . . . . . . . . . . . . . . . 1 The simplest way . . . . . . . . . . . . . . . . . . . . 2 Further optimization . . . . . . . . . . . . . . . . . . 1 Reducing register count . . . . . . . . . . 2 More data output per thread . . . . . . . A 5×5 and more median filter . . . . . . . . . . . . .

Cu file featuring one kernel skeleton t e x t u r e t e x i m g i n global ; void k e r n e l i d e n t ( short ∗ output , i n t w) { 5 int j = int i = m u l 2 4 ( b l o c k I d x . x , blockDim . x ) + t h r e a d I d x . x ; m u l 2 4 ( b l o c k I d x . y , blockDim . y ) + t h r e a d I d x . 3. generic makefile based on those provided by NVIDIA SDK EXECUTABLE CUFILES include := f a s t m e d i a n := main . cu . / . / common/common . 01 ms. Again, the easiest way of doing so is to use the helper functions of the cutil library.

3. Scalability of GPU. 7 Memory hierarchy The memory hierarchy of GPUs is different from that of CPUs. In practice, there are registers, local memory, shared memory, cache memory, and global memory. As previously mentioned each thread can access its own registers. It is important to keep in mind that the number of registers per block is limited. On recent cards, this number is limited to 64Kb per SM. Access to registers is very fast, so it is a good idea to use them whenever possible. Likewise each thread can access local memory which, in practice, is much slower than registers.

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