By Raphael Couturier
Many of today’s complicated clinical functions now require an enormous quantity of computational strength. basic objective pix processing devices (GPGPUs) allow researchers in quite a few fields to learn from the computational energy of the entire cores to be had inside of images cards.
Understand the advantages of utilizing GPUs for lots of medical Applications
Designing medical functions on GPUs indicates you ways to exploit GPUs for purposes in diversified medical fields, from physics and arithmetic to laptop technology. The booklet explains the tools helpful for designing or porting your medical software on GPUs. it is going to enhance your wisdom approximately picture processing, numerical purposes, technique to layout effective functions, optimization tools, and masses more.
Everything you want to Design/Port Your medical program on GPUs
The first a part of the ebook introduces the GPUs and Nvidia’s CUDA programming version, at present the main frequent atmosphere for designing GPU purposes. the second one half makes a speciality of major photograph processing purposes on GPUs. The 3rd half provides basic 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 hindrance difficulties, fluid simulation, and atomic physics types. The final half illustrates agent-based simulations, pseudorandom quantity iteration, and the answer of enormous sparse linear structures for integer factorization. a few of the codes awarded within the booklet can be found online.
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Additional info for Designing Scientific Applications on GPUs
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. One first issue is that the exclusive use of registers forbids us to implement a naive histogram-based method. , more than four times the maximum register count allowed per thread (63). Considering that a 3 × 3 median filter involves only 9 pixel values per thread, it seem obvious they can be sorted within the 63-register limit. , the median value.
1 The simplest way . . . . . . . . . . . . . . . . . . . . 2 Further optimization . . . . . . . . . . . . . . . . . . 1 Reducing register count . . . . . . . . . . 2 More data output per thread . . . . . . . A 5×5 and more median filter . . . . . . . . . . . . . . . . . 1 A register-only 5×5 median filter . . . . . . . . . . . . 2 Fast approximated n × n median filter . . . . . . .
The CPU computation is performed and the time for this is measured. In order to compute the same result for the GPU, first of all, data from the CPU need to be copied into the memory of the GPU. For that, it is possible to use CUBLAS function cublasSetVector. This function has several arguments. More precisely, the first argument represents the number of elements to transfer, the second arguments is the size of each element, the third element represents the source of the array to transfer (in the GPU), the fourth is an offset between each element of the source (usually this value is set to 1), the fifth is the destination (in the GPU), and the last is an offset between each element of the destination.
Designing Scientific Applications on GPUs by Raphael Couturier