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Ziminli
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| // Dot product kernel: computes dot(a, b) = sum(a * b) | ||
| // template <unsigned int BLOCK_SIZE, typename Tdata, typename Tcompute> | ||
| // __global__ void dot_kernel( | ||
| // Tcompute *result, | ||
| // const Tdata *a, | ||
| // const Tdata *b, | ||
| // size_t n, | ||
| // ptrdiff_t a_stride, | ||
| // ptrdiff_t b_stride) { | ||
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| // Tcompute sum = 0; | ||
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| // // Each thread computes partial dot product | ||
| // for (size_t i = threadIdx.x; i < n; i += BLOCK_SIZE) { | ||
| // Tcompute a_val = Tcompute(a[i * a_stride]); | ||
| // Tcompute b_val = Tcompute(b[i * b_stride]); | ||
| // sum += a_val * b_val; | ||
| // } | ||
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| // // Use CUB block-level reduction | ||
| // using BlockReduce = cub::BlockReduce<Tcompute, BLOCK_SIZE>; | ||
| // __shared__ typename BlockReduce::TempStorage temp_storage; | ||
| // Tcompute block_sum = BlockReduce(temp_storage).Sum(sum); | ||
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| // // Write result (only thread 0, since we only launch 1 block) | ||
| // if (threadIdx.x == 0) { | ||
| // *result = block_sum; | ||
| // } | ||
| // } |
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