![]() • • • • • • • • • • • • Overview In this post, we first will introduce the basics of using the GPU with MATLAB and then move onto solving a 2nd-order wave equation using this GPU functionality. This blog post is inspired by a recent MATLAB Digest on GPU Computing that I coauthored with one of our developers, Jill Reese. Since the original demo was made, the GPU functions available in MATLAB have grown. If you compare the code below to the code in the paper- they are slightly different, reflecting these new capabilites. Cheb1ord uses the Chebyshev lowpass filter order prediction formula described in. The function performs its calculations in the analog domain for both analog and digital cases. C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™. Usage notes and limitations: All inputs must be constants. Expressions or variables are. Additionally, small changes were made to enable easier explanation of the code in this blog format. How to play samsung pro cricket 2015 batting. The GPU functionality shown in this post requires the Parallel Computing Toolbox. GPU Background Originally used to accelerate graphics rendering, GPUs are now increasingly applied to scientific calculations. Unlike a traditional CPU, 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. A typical GPU comprises hundreds of these smaller processors. These processors can be used to greatly speed-up particular types of applications. A good rule of thumb is that your problem may be a good fit for the GPU if it is. • Computationally intensive: The time spent on computation significantly exceeds the time spent on transferring data to and from GPU memory. Because a GPU is attached to the host CPU via the PCI Express bus, the memory access is slower than with a traditional CPU. This means that your overall computational speedup is limited by the amount of data transfer that occurs in your algorithm. Applications that do not satisfy these criteria might actually run more slowly on a GPU than on a CPU. Learning About Our GPU With that background, we can now start working with the GPU in MATLAB. Aslak Grinsted replied on: 6 of 14 I have been wanting to use the GPUarray for calculating something similar to gevlike and the other similar loglikelihood functions. This function involves doing the same calculation on every single element in a long vector, and then adding all the results. I have had a case where i had to calculate this so many times that it took me a week to complete. I believe GPUarray would speed it up immensely. Unfortunately my gpu does not fulfill the requirements (it is nvidia, but just a little too old). Sarah Wait Zaranek replied on: 10 of 14 @Najeeb - If I am understanding what you want to do correctly - I think repmat or bsxfun would the appropriate way to do it on the CPU. When moving to the GPU, repmat is currently supported - so I would do the following: A = rand(4096,4096);% Using rand as a proxy for your data Avector = rand(4096,1);% Using rand as a proxy for your data% Calculation on CPU AV = repmat(Avector,1,4096); B = A.*AV;% Caclulation on GPU A = gpuArray(A);% Transfering to GPU Avector = gpuArray(Avector);% Transfering to GPU AV = repmat(Avector,1,4096); B = A.*AV; Hope this helps. Cheers, Sarah. Sarah Wait Zaranek replied on: 11 of 14 @Aditya - I am not sure specifically what you are wanting to do. If you want to do something besides using GPUArrays and overloaded functions, you can use the arrayfun option.
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