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