can see we no longer have dependent instructions immediately following each other. In this post I will show you some features of the Kepler GPU architecture which make reductions even faster: the shuffle (shfl) instruction and fast device memory atomic operations. X 0) atomicAdd(out, sum This approach requires fewer atomics than the warp atomic approach, executes only a single kernel, and does not require temporary memory, but it does use _syncthreads and shared memory.
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Note that width must be one of (2, 4, 8, 16, 32). . Keplers shuffle instruction (shfl) enables a thread to directly read code promo box cinema le trefle a register from another thread in the same warp (32 threads). Float32) @ -4,6 4,7 @ #include thrust/reduce. Perform multiple reductions at the same time by interleaving their instructions is more efficient because it increases ILP. Figure 3: Reduction Bandwidth on K20X. X 0) outcolIndex maxval; _syncthreads / namespace template void RowwiseMax( const int N, const int D, const float* x, float* y, cudacontext* context) rowwise_max_kernel std:min(N, caffe_maximum_NUM_blocks caffe_cuda_NUM_threads, 0, D, x, y template void ColwiseMax( const int N, const int D, const float* x, float*.