Recitation 6: Monte-Carlo Integration - Caltech

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CURAND Device API ● Now, RNG states are stored entirely on GPU ▸ Still need to allocate space ▸ So, on the host we need to do: curandState* devStates; cudaMalloc(&devStates, numThreads * sizeof(curandState)); kernel_func>(devStates); cudaFree(devStates); // don’t free devStates if you want to use // them again in another kernel
CURAND Device API ● When states are allocated, initialize and use them in kernel: int x = threadIdx.x + blockIdx.x * blockDim.x; curand_init(seed, x, 0, &states[x]); // generate uniform float in [a, b] v[x] = curand_uniform(&states[x]) * (b - a) + a; ● Don't need to destroy states when done, just call cudaFree
Page 1 and 2: CS179 GPU Programming: Recitation 6
Page 3 and 4: Integration ● We can use discrete
Page 5 and 6: Monte-Carlo Integration ● Solutio
Page 7 and 8: Randomness on the GPU ● Remember
Page 9 and 10: Randomness on the GPU ● Solution:
Page 11 and 12: CURAND Host API ● ● Pretty easy
Page 13: CURAND Device API ● ● What if y
Page 17 and 18: Linear Algebra ● Many libraries a
Page 19 and 20: Reduction ● Recall our reduction

Recitation 6: Monte-Carlo Integration - Caltech

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