Download Designing Scientific Applications on GPUs by Raphael Couturier PDF

By Raphael Couturier

Many of today’s complicated clinical purposes now require an enormous quantity of computational energy. common function images processing devices (GPGPUs) let researchers in quite a few fields to learn from the computational energy of the entire cores to be had inside of snap shots cards.

Understand the advantages of utilizing GPUs for plenty of medical Applications

Designing clinical purposes on GPUs exhibits you the way to exploit GPUs for purposes in assorted medical fields, from physics and arithmetic to laptop technology. The booklet explains the tools useful for designing or porting your clinical software on GPUs. it's going to enhance your wisdom approximately snapshot processing, numerical functions, technique to layout effective functions, optimization tools, and lots more and plenty more.

Everything you must Design/Port Your medical program on GPUs

The first a part of the ebook introduces the GPUs and Nvidia’s CUDA programming version, presently the main common setting for designing GPU purposes. the second one half specializes in major photograph processing functions on GPUs. The 3rd half offers common methodologies for software program improvement on GPUs and the fourth half describes using GPUs for addressing numerous optimization difficulties. The 5th half covers many numerical functions, together with challenge difficulties, fluid simulation, and atomic physics versions. The final half illustrates agent-based simulations, pseudorandom quantity iteration, and the answer of huge sparse linear structures for integer factorization. a number of the codes offered within the booklet can be found online.

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H" 20 Designing Scientific Applications on GPUs #include 10 const i n t width =16; const i n t nbTh=width ∗ width ; const i n t s i z e =1024; const i n t s i z e M a t=s i z e ∗ s i z e ; 15 global void matmul ( f l o a t ∗d A , f l o a t ∗d B , f l o a t ∗ d C ) { i n t i= b l o c k I d x . y∗ blockDim . y+ t h r e a d I d x . y ; i n t j= b l o c k I d x . x∗ blockDim . x+ t h r e a d I d x . 5 Conclusion In this chapter, three simple CUDA examples have been presented. As we cannot present all the possibilities of the CUDA programming, interested readers are invited to consult CUDA programming introduction books if some issues regarding the CUDA programming are not clear.

Both can be partially hidden by introducing Instruction-Level Parallelism (ILP) and by increasing the data count outputted by each thread. Though such techniques may seem to break the NVIDIA occupancy paradigm, they can lead to dramatically higher data throughput values. The following sections illustrate these ideas and detail the design of the fastest CUDA median filter known to date. 4 A 3×3 median filter: using registers Designing a median filter dedicated to the smallest possible square window size is a good challenge to start using registers.

31 32 32 33 35 37 37 38 39 42 44 44 47 51 Introduction Median filtering is a well-known method used in a wide range of application frameworks as well as a standalone filter especially for salt and pepper denoising. It is able to greatly reduce the power of noise without blurring edges too much. That is actually why we originally focused on this filtering technique as a preprocessing stage when we were in the process of designing a GPU implementation of one region-based image segmentation algorithm [8].

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