gpgpu

Lets assume that I have a computer which has a multicore processor and a GPU. I would like to write an OpenCL program which runs on all cores of the platform. Is this possible or do I need to choose a single device on which to run the kernel? In theory yes, you can, the CL API allows it. But the platform/implementation must support it, and i don't think most CL implementatations do. To do it, get the cl_device_id of the CPU device and the GPU device, and create a context with those two devices, using clCreateContext. No you can't span automagically a kernel on both CPU and GPU, it's either one

If not, what is the standard way to free up cudaMalloc ed memory when an exception is thrown? (Note that I am unable to use Thrust.) You can use RAII idiom and put your cudaMalloc() and cudaFree() calls to the constructor and destructor of your object respectively. Once the exception is thrown your destructor will be called which will free the allocated memory. If you wrap this object into a smart-pointer (or make it behave like a pointer) you will get your CUDA smart-pointer. 来源： https://stackoverflow.com/questions/16509414/is-there-a-cuda-smart-pointer

So lets say I have a global array of memory: |a|b|c| |e|f|g| |i|j|k| | There are four 'threads' (local work items in OpenCL) accessing this memory, and two possible patterns for this access (columns are time slices, rows are threads): 0 -> 1 -> 2 -> 3 t1 a -> b -> c -> . t2 e -> f -> g -> . t3 i -> j -> k -> . t4 . . . `> . The above pattern splits the array in to blocks with each thread iterating to and accessing the next element in a block per time slice. I believe this sort of access would work well for CPUs because it maximizes cache locality per thread. Also, loops utilizing this pattern

I'm using nsight 2.2 , Toolkit 4.2 , latest nvidia driver , I'm using couple gpu's in my computer. Build customize 4.2. I have set "generate GPU ouput" on CUDA's project properties, nsight monitor is on (everything looks great). I set several break points on my global - kernel function . nsight stops at the declaration of the function , but skips over several break points. it's just like nsight decide whether to hit a break point or skip over a break point. The funny thing is that nsight stops at for loops , but doesn't stop on simple assignment operations. One more problem is that I can't set

I have an application that stores a vector of structs. These structs hold information about each GPU on a system like memory and giga-flop/s. There are a different number of GPUs on each system. I have a program that runs on multiple machines at once and I need to collect this data. I am very new to MPI but am able to use MPI_Gather() for the most part, however I would like to know how to gather/receive these dynamically sized vectors. class MachineData { unsigned long hostMemory; long cpuCores; int cudaDevices; public: std::vector<NviInfo> nviVec; std::vector<AmdInfo> amdVec; ... }; struct

I would like to know wether you know of any way to step-by-step debug OpenCL Kernel using Windows (my IDE is Visual Studio) and running OpenCL Kernels on a NVidia GPU. What i found so far is: with NVidias NSight you can only profile OpenCL Applications, but not debug them the current version of the gDEBugger from AMD only supports ATI/AMD GPUs the old version of gDEBugger supports NVidia GPUs but work is discontinued in Dec '10 the GDB debugger seems to support it, but is only available under Linux the Intel OpenCL SDK brings a Debugger, but it only works while running the code on the CPU, not