gpgpu

I am implementing compute intensive applications for iOS (i.e., iPhone or iPad) that heavily use fast Fourier transforms (and some signal processing operations such as interpolations and resampling). What are the best libraries and API that allows for running FFTs on iOS? I have briefly looked into Apple Metal as well as Apple vDSP. I wasn't sure that vDSP utilizes GPUs although it seems to be highly parallelized and utilizes SIMD. Metal seems to allow to access GPU for compute intensive apps, but I am not able to find libraries for FFT and basic signal processing operations (something like

I am writing a cuda program and trying to print something inside the cuda kernels using the printf function. But when I am compiling the program then I am getting an error error : calling a host function("printf") from a __device__/__global__ function("agent_movement_top") is not allowed error MSB3721: The command ""C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v4.2\bin\nvcc.exe" -gencode=arch=compute_10,code=\"sm_10,compute_10\" --use-local-env --cl-version 2008 -ccbin "c:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\bin" -I"C:\ProgramData\NVIDIA Corporation\NVIDIA GPU Computing

Unlike barrier() (which I think I understand), mem_fence() does not affect all items in the work group. The OpenCL spec says (section 6.11.10), for mem_fence() : Orders loads and stores of a work-item executing a kernel. (so it applies to a single work item). But, at the same time, in section 3.3.1, it says that: Within a work-item memory has load / store consistency. so within a work item the memory is consistent. So what kind of thing is mem_fence() useful for? It doesn't work across items, yet isn't needed within an item... Note that I haven't used atomic operations (section 9.5 etc). Is

As known, there are WARP (in CUDA) and WaveFront (in OpenCL): http://courses.cs.washington.edu/courses/cse471/13sp/lectures/GPUsStudents.pdf WARP in CUDA: http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#simt-architecture 4.1. SIMT Architecture ... A warp executes one common instruction at a time , so full efficiency is realized when all 32 threads of a warp agree on their execution path. If threads of a warp diverge via a data-dependent conditional branch, the warp serially executes each branch path taken, disabling threads that are not on that path, and when all paths complete

I'm trying to fill a 1D texture with values manually and pass that texture to a compute shader (these are 2 pixels that I want to set via code, they don't represent any image). Due to the current small amount of Metal examples, all examples I could find deal with 2D textures that load the texture by converting a loaded UIImage to raw bytes data, but creating a dummy UIImage felt like a hack for me. This is the "naive" way I started with - ... var manualTextureData: [Float] = [ 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0 ]; let region: MTLRegion = MTLRegionMake1D(0, textureDescriptor.width);

I am using NVIDIA's GeForce GTX 480 GPU on Wwindows 7 operating system on my ASUS laptop. I have already configured Visual Studio 2010 for CUDA 4.2. How to configure OpenCL for nvidia's gpu on visual studio 2010?? Have tries every possible way. Is it possible by any way to use 'CUDA toolkit (CUDA 4.2)' and 'nvidia's gpu computing sdk' to program OpenCL? If yes then How? If no then what is other way? KLee1 Yes. You should be able to use Visual Studio 2010 to program for OpenCL. It should simply be a case of making sure that you have the right include directories and libraries setup. Take a look