Download Designing Scientific Applications on GPUs by Raphael Couturier PDF

By Raphael Couturier

Many of today’s complicated medical functions now require an enormous quantity of computational strength. common function photos processing devices (GPGPUs) permit researchers in a number of fields to profit from the computational strength of the entire cores on hand within snap shots cards.

Understand the advantages of utilizing GPUs for lots of clinical Applications

Designing medical purposes on GPUs indicates you the way to exploit GPUs for purposes in diversified clinical fields, from physics and arithmetic to computing device technological know-how. The publication explains the equipment useful for designing or porting your clinical program on GPUs. it's going to increase your wisdom approximately snapshot processing, numerical functions, method to layout effective purposes, optimization equipment, and masses more.

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

The first a part of the publication introduces the GPUs and Nvidia’s CUDA programming version, at the moment the main common surroundings 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 purposes, together with crisis 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. the various codes provided within the ebook can be found online.

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Extra info for Designing Scientific Applications on GPUs

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Matrices A and B are randomly initialized. Then arrays are transferred to the GPU memory with call to cudaMemcpy. So the first step for each thread of a block is to compute the corresponding row and column. x; the corresponding column. Then each thread has to compute the sum of the product of the row of A by the column of B. In order to use a register, the kernel matmul uses a variable called sum to compute the sum. Then the result is set into the matrix at the right place. The computation of CPU matrix-matrix multiplication is performed as described previously.

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 . . . . . .

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 . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . 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.

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