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Use warp shuffles instead of shared memory access to speedup the inner reduction kernel.

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This commit is contained in:
Benoit Steiner 2016-01-14 21:45:14 -08:00
parent 8fe2532e70
commit aed4cb1269
1 changed files with 7 additions and 13 deletions
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20
unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
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@ -132,8 +132,6 @@ struct FullReducer<Self, Op, GpuDevice, Vectorizable> {
}; };
extern __shared__ float temp[];
template <int NumPerThread, typename Self, template <int NumPerThread, typename Self,
typename Reducer, typename Index> typename Reducer, typename Index>
__global__ void InnerReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs, __global__ void InnerReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
@ -183,17 +181,13 @@ __global__ void InnerReductionKernel(Reducer reducer, const Self input, Index nu
} }
} }
temp[threadIdx.x] = reduced_val; #pragma unroll
for (int offset = warpSize/2; offset > 0; offset /= 2) {
reducer.reduce(__shfl_down(reduced_val, offset), &reduced_val);
}
__syncthreads(); if ((threadIdx.x & (warpSize - 1)) == 0) {
const int warp_id = threadIdx.x & 31; atomicReduce(&(output[row]), reduced_val, reducer);
if (warp_id < 16) reducer.reduce(temp[threadIdx.x + 16], &temp[threadIdx.x]);
if (warp_id < 8) reducer.reduce(temp[threadIdx.x + 8], &temp[threadIdx.x]);
if (warp_id < 4) reducer.reduce(temp[threadIdx.x + 4], &temp[threadIdx.x]);
if (warp_id < 2) reducer.reduce(temp[threadIdx.x + 2], &temp[threadIdx.x]);
if (warp_id < 1) {
reducer.reduce(temp[threadIdx.x + 1], &temp[threadIdx.x]);
atomicReduce(&(output[row]), temp[threadIdx.x], reducer);
} }
} }
@ -224,7 +218,7 @@ struct InnerReducer<Self, Op, GpuDevice> {
EIGEN_UNUSED_VARIABLE(num_blocks) EIGEN_UNUSED_VARIABLE(num_blocks)
LAUNCH_CUDA_KERNEL((InnerReductionKernel<num_per_thread, Self, Op, Index>), LAUNCH_CUDA_KERNEL((InnerReductionKernel<num_per_thread, Self, Op, Index>),
num_blocks, block_size, block_size*sizeof(float), device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output); num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
} }
}; };