cavis/libnd4j/include/loops/cuda/specials/accumulateKernel.cu

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2019-06-06 14:21:15 +02:00
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
// @author Yurii Shyrma, created on 15.11.2018
//
#include <loops/special_kernels.h>
namespace nd4j {
///////////////////////////////////////////////////////////////////////
/**
* This kernel accumulates X arrays, and stores z into Z
*
* @tparam T
* @param x
* @param z
* @param n
* @param length
*/
template<typename T>
__device__ void accumulateKernel(void **vx, void *vz, int n, const Nd4jLong length) {
auto x = reinterpret_cast<T **>(vx);
auto z = reinterpret_cast<T *>(vz);
__shared__
T *shmem;
if (threadIdx.x == 0) {
extern __shared__ unsigned char sharedmem[];
shmem = (T *) sharedmem;
}
__syncthreads();
for (int r = blockDim.x * blockIdx.x; r < length; r += blockDim.x * gridDim.x) {
shmem[threadIdx.x] = 0.0f;
Nd4jLong baseIdx = r;
// aggregation step, we roll over all arrays
for (int ar = 0; ar < n; ar++) {
T *cdata = (T *) x[ar];
cdata += baseIdx;
if (baseIdx + threadIdx.x < length)
shmem[threadIdx.x] += cdata[threadIdx.x];
}
T *wdata = z + baseIdx;
// saving accumulated values
if (baseIdx + threadIdx.x < length)
wdata[threadIdx.x] = shmem[threadIdx.x];
}
}
///////////////////////////////////////////////////////////////////////
template<typename T>
__global__ void execAccumulateKernel(void **vx, void *vz, int n, const Nd4jLong length) {
accumulateKernel<T>(vx, vz, n, length);
}
///////////////////////////////////////////////////////////////////////
template<typename T>
__host__ void
accumulateKernelGeneric(dim3 &launchDims, cudaStream_t *stream, void **vx, void *vz, int n, const Nd4jLong length) {
execAccumulateKernel<T><<< launchDims.x, launchDims.y, launchDims.z, *stream>>> (vx, vz, n, length);
nd4j::DebugHelper::checkErrorCode(stream, "accumulate(...) failed");
}
BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT accumulateKernelGeneric, (dim3 & launchDims, cudaStream_t * stream, void * *vx, void * vz, int n, const Nd4jLong length), LIBND4J_TYPES);
}