Puede usar MATLAB en una amplia gama de aplicaciones que incluyen procesamiento de se&241 ales e im&225 genes, comunicaciones, dise&241 o de sistemas de control, sistemas de prueba y medici&243 n. A Simple Example using Overloaded FunctionsCon MATLAB, podr&225 resolver problemas de c&225 lculo t&233 cnico m&225 s r&225 pidamente que con lenguajes de programaci&243 n tradicionales, tales como C, C++ y FORTRAN. Call the nexttile function to create the axes objects ax1 and ax2. Call the tiledlayout function to create a 2-by-1 tiled chart layout.
Since the original demo was made, the GPU functionsAvailable in MATLAB have grown. This blog post is inspired by a recent MATLAB Digest article on GPU Computing that I coauthored with one of our developers, Jill Reese. Code Changes to Transfer Data to the GPU and Back AgainIn this post, we first will introduce the basics of using the GPU with MATLAB and then move onto solving a 2nd-order waveEquation using this GPU functionality. Understanding and Limiting Your Data Transfer Overhead
Como Usar Matlab 2012 Code In This
A typical GPU comprises hundreds of these smaller processors. Unlike a traditionalCPU, which includes no more than a handful of cores, a GPU has a massively parallel array of integer and floating-point processors,As well as dedicated, high-speed memory. Additionally, small changes were made to enable easier explanation of the code in this blog format.The GPU functionality shown in this post requires the Parallel Computing Toolbox.Originally used to accelerate graphics rendering, GPUs are now increasingly applied to scientific calculations.
If we do that - we find that our acceleration is greatly reduced. However, to perform a realistic comparison, we should really include the time spent transferringThe vector to and from the GPU. This is pretty good, especially since fft is multi-threaded in core MATLAB. By running class(B2), we can see that it is also a GPUArray.To bring the data back to the CPU, we run gather.B2 is now on the CPU and has a class of double.In this simple example, we can calculate the speedup of performing the fft on our data.It looks like our fft is running ~3.5x faster on the GPU. However, it is still visible in the MATLAB workspace. The result, B2, is stored on the GPU.
Como Usar Matlab 2012 Serial Code Including
To be accurate, we should also retime the original serial code including the timeIt takes to generate the random matrix on the CPU.A5 = parallel.gpu.GPUArray.rand(3000,3000) This is better, although we still do see the influence of gathering the data back from the GPU. Let's see how that effects our time. There areSeveral constructors available as well as constructor-like functions such as meshgrid. However notAll hope is lost! By limiting the data transfer between the GPU and the CPU, we can still acheive speedup.Instead of creating the data on the CPU and transferring it to the GPU - we can just create on the GPU directly.
Fft and ifft, matrix multiplication) are all overloaded functions that work with the GPU. The operationsIn that part of the code (e.g. Specifically, we apply the Chebyshev spectral method, which uses Chebyshev polynomials as the basis functions.Our example is largely based on an example in Trefethen's book: "Spectral Methods in MATLAB" Code Changes to Run Algorithm on GPUWhen accelerating our alogrithm, we focus on speeding up the code within the main time stepping while-loop. To do this, weAre going to solve a second-order wave equation:The algorithm we use to solve the wave equation combines a spectral method in space and a second-order central finite differenceMethod in time. It is more efficient to performSeveral operations on the data while it is on the GPU, bringing the data back to the CPU only when required.To put the above example into context, let's use the same GPU functionality on a more "real-world" problem.
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