I got information from CUDA Profiler. I am so confused why Replays Instruction != Grobal memory replay + Local memory replay + Shared bank conflict replay?
In CUDA, there is a concept of a warp, which is defined as 开发者_JAVA技巧the maximum number of threads that can execute the same instruction simultaneously within a single processing element.For NVID
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I\'m working on an algorithm that does prettymuch the same operation a bunch of times. Since the operation consists of some linear algebra(BLAS), I thourght I would try using the GPU for this.
Why is this matrix transpose kernel faster, when the shared memory array is padded by one column? I found the kernel at PyCuda/Examples/MatrixTranspose.
Can I have two mixed chipset/generation AMD gpus in my desktop; a 6950 and 4870, and dedicate one gpu (4870) for opencl/gpgpu purposes only, eliminating the device from video output or display driving
I\'m currently implementing an algorithm that does allot of linear algebra on small matrices and vectors. the code is fast but I\'m wondering if it would make sense to implement it on a gpgpu instead
I have a CUDA program that seems to be hitting some sort of limit of some resource, but I can\'t figure out what that resource is.Here is the kernel function:
I have a CUDA application I\'m working on with an array of Objects; each object has a pointer to an array of std::pair<int, double>.I\'m trying to cudaMemcpy the array of objects over, then cuda
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