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copied!<p>Spend some time focusing on the copious documentation offered by NVIDIA.</p> <p>From the Programming Guide:</p> <pre><code>float* devPtr; cudaMalloc((void**)&devPtr, 256 * sizeof(*devPtr)); cudaMemset(devPtr, 0, 256 * sizeof(*devPtr)); </code></pre> <p>That's a simple example of how to allocate memory. Now, in your kernels, you should accept a pointer to a float like so:</p> <pre><code>__global__ void kernel1(float *some_neat_data) { some_neat_data[threadIdx.x]++; } __global__ void kernel2(float *potentially_that_same_neat_data) { potentially_that_same_neat_data[threadIdx.x] *= 0.3f; } </code></pre> <p>So now you can invoke them like so:</p> <pre><code>float* devPtr; cudaMalloc((void**)&devPtr, 256 * sizeof(*devPtr)); cudaMemset(devPtr, 0, 256 * sizeof(*devPtr)); kernel1<<<1,128>>>(devPtr); kernel2<<<1,128>>>(devPtr); </code></pre> <blockquote> <p>As this data is used in numerous functions, I would like it to be global.</p> </blockquote> <p>There are few good reasons to use globals. This definitely is not one. I'll leave it as an exercise to expand this example to include moving "devPtr" to a global scope.</p> <p>EDIT:</p> <p>Ok, the fundamental problem is this: your kernels can only access device memory and the only global-scope pointers that they can use are GPU ones. When calling a kernel from your CPU, behind the scenes what happens is that the pointers and primitives get copied into GPU registers and/or shared memory before the kernel gets executed.</p> <p>So the closest I can suggest is this: use cudaMemcpyToSymbol() to achieve your goals. But, in the background, consider that a different approach might be the Right Thing.</p> <pre><code>#include <algorithm> __constant__ float devPtr[1024]; __global__ void kernel1(float *some_neat_data) { some_neat_data[threadIdx.x] = devPtr[0] * devPtr[1]; } __global__ void kernel2(float *potentially_that_same_neat_data) { potentially_that_same_neat_data[threadIdx.x] *= devPtr[2]; } int main(int argc, char *argv[]) { float some_data[256]; for (int i = 0; i < sizeof(some_data) / sizeof(some_data[0]); i++) { some_data[i] = i * 2; } cudaMemcpyToSymbol(devPtr, some_data, std::min(sizeof(some_data), sizeof(devPtr) )); float* otherDevPtr; cudaMalloc((void**)&otherDevPtr, 256 * sizeof(*otherDevPtr)); cudaMemset(otherDevPtr, 0, 256 * sizeof(*otherDevPtr)); kernel1<<<1,128>>>(otherDevPtr); kernel2<<<1,128>>>(otherDevPtr); return 0; } </code></pre> <p>Don't forget '--host-compilation=c++' for this example.</p>

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