with smaller windows for the remaining part (possibly. In the kernel of reduction_tiled_1, the number of active threads is reduced by half in each iteration and dispersed across the entire tile. However this approach requires some fine tuning for the best performance and it has been omitted from the sample for the sake of simplicity. WordPress, cMS, dNS Records, nameservers m m host value ttl m 299 host value ttl m m 300 m m 300 host value ttl m m 300 host value ttl m Mname: m Rname: m Serial: Refresh: 3600 Retry: 900 Expire: 604800 Minimum-ttl: host value. The heart of the kernel is then the following: float sum.f; for(unsigned i 0; i window_width; i) sum aidx i * s; aidx sum; The one drawback though is more complex computation of the tail sum, effectively executed serially and leading to a notable. For(int s _tile_size / 2; s 0; s / 1) if (tid s) tile_datatid tile_datatid s; rrier. During the execution, all threads in a tile read an element from the global memory cooperatively into tile_static array once and operate on the data in the tile_static memory afterwards. Now all threads have to calculate a partial sum within the tile like in reduction_tiled_3.
This post will talk about different implementations of reduction and optimization from one implementation to the other. Consecutive tile_static memory is accessed sequentially by threads like below. After all kernel invocations, the sum is stored in the 0th element of the array.
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