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/*************************************************************************
* Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "devcomm.h"
#include "primitives.h"
#include "collectives.h"
template<int UNROLL, class FUNC, typename T>
__device__ void ncclReduceScatterRingKernel(struct CollectiveArgs* args) {
const int tid = threadIdx.x;
const int nthreads = args->nThreads-WARP_SIZE;
const int bid = args->bid;
struct ncclDevComm* comm = args->comm;
struct ncclChannel* channel = comm->channels+blockIdx.x;
struct ncclRing* ring = &channel->ring;
const ssize_t size = args->N;
const int nranks = comm->nRanks;
const int stepSize = channel->buffSize / (sizeof(T)*NCCL_STEPS);
const int chunkSize = stepSize * REDUCESCATTER_CHUNKSTEPS;
const ssize_t loopSize = args->nChannels*(ssize_t)chunkSize;
// Compute pointers
const T * __restrict__ thisInput = (const T*)args->ThisInput;
T * __restrict__ thisOutput = (T*)args->ThisOutput;
ncclPrimitives<UNROLL, REDUCESCATTER_CHUNKSTEPS/REDUCESCATTER_SLICESTEPS, REDUCESCATTER_SLICESTEPS, T, 1, 1, FUNC>
prims(tid, args->nThreads, &ring->prev, &ring->next, NULL, stepSize, channel, comm, args->opCount);
for (ssize_t gridOffset = 0; gridOffset < size; gridOffset += loopSize) {
int realChunkSize = min(chunkSize, DIVUP(size-gridOffset,args->nChannels));
ALIGN_SIZE(realChunkSize, nthreads*sizeof(uint64_t)/sizeof(T));
ssize_t chunkOffset = gridOffset + bid*realChunkSize;
/////////////// begin ReduceScatter steps ///////////////
ssize_t offset;
int nelem = min(realChunkSize, size-chunkOffset);
int rankDest;
// step 0: push data to next GPU
rankDest = ring->devUserRanks[nranks-1];
offset = chunkOffset + rankDest * size;
prims.send(thisInput+offset, nelem);
// k-2 steps: reduce and copy to next GPU
for (int j=2; j<nranks; ++j) {
rankDest = ring->devUserRanks[nranks-j];
offset = chunkOffset + rankDest * size;
prims.recvReduceSend(thisInput+offset, nelem);
}
// step k-1: reduce this buffer and data, which will produce the final result
rankDest = ring->devUserRanks[0];
offset = chunkOffset + rankDest * size;
prims.recvReduceCopy(thisInput+offset, thisOutput+chunkOffset, nelem);
}
}
template<int UNROLL, class FUNC, typename T>
__device__ void ncclReduceScatterTreeKernel(struct CollectiveArgs* args) { }
template<int UNUSED, class FUNC, typename T>
__device__ void ncclReduceScatterRingLLKernel(struct CollectiveArgs* args) {
const int tid = threadIdx.x;
const int bid = args->bid;
const int nthreads = args->nThreads;
struct ncclDevComm* comm = args->comm;
struct ncclChannel* channel = comm->channels+blockIdx.x;
struct ncclRing* ring = &channel->ring;
ncclLLPrimitives<T, FUNC, 1, 1> LLprims(tid, nthreads, &ring->prev, &ring->next, channel, comm, args->opCount);
const ssize_t size = args->N;
//const int rank = comm->rank;
const int nranks = comm->nRanks;
ssize_t chunkSize = NCCL_LL_SLICE_LINES * sizeof(uint64_t) / sizeof(T);
const ssize_t loopSize = args->nChannels*chunkSize;
// Compute pointers
const T * __restrict__ thisInput = (const T*)args->ThisInput;
T * __restrict__ thisOutput = (T*)args->ThisOutput;
for (ssize_t gridOffset = 0; gridOffset < size; gridOffset += loopSize) {
if (size-gridOffset < loopSize) {
chunkSize = args->lastChunkSize;
}
ssize_t chunkOffset = gridOffset + bid*chunkSize;
/////////////// begin ReduceScatter steps ///////////////
ssize_t offset;
int nelem = min(chunkSize, size-chunkOffset);
int rankDest;
// step 0: push data to next GPU
rankDest = ring->devUserRanks[nranks-1];
offset = chunkOffset + rankDest * size;
LLprims.send(thisInput+offset, nelem);
// k-2 steps: reduce and copy to next GPU
for (int j=2; j<nranks; ++j) {
rankDest = ring->devUserRanks[nranks-j];
offset = chunkOffset + rankDest * size;
LLprims.recvReduceSend(thisInput+offset, nelem);
}
// step k-1: reduce this buffer and data, which will produce the final
// result that we store in this data
rankDest = ring->devUserRanks[0];
offset = chunkOffset + rankDest * size;
LLprims.recvReduceCopy(thisInput+offset, thisOutput+chunkOffset, nelem);
}
}
template<int UNUSED, class FUNC, typename T>
__device__ void ncclReduceScatterTreeLLKernel(struct CollectiveArgs* args) { }
#include "prims_ll128.h"
template<int UNUSED, class FUNC, typename T>
__device__ void ncclReduceScatterRingLL128Kernel(struct CollectiveArgs* args) {
const int tid = threadIdx.x;
const int bid = args->bid;
const int nthreads = args->nThreads;
struct ncclDevComm* comm = args->comm;
struct ncclChannel* channel = comm->channels+blockIdx.x;
struct ncclRing* ring = &channel->ring;
ncclLL128Primitives<T, FUNC, 1, 1> LLprims(tid, nthreads, &ring->prev, &ring->next, channel, comm, args->opCount);
const ssize_t size = args->N;
//const int rank = comm->rank;
const int nranks = comm->nRanks;
ssize_t chunkSize = (NCCL_LL128_ELEMS_PER_THREAD*nthreads*NCCL_LL128_DATAELEMS*sizeof(uint64_t))/(NCCL_LL128_LINEELEMS*sizeof(T));
// We should not need the final /2 but it makes performance much, much smoother. Might be a bug somewhere.
const ssize_t minChunkSize = (NCCL_LL128_SHMEM_ELEMS_PER_THREAD*nthreads*NCCL_LL128_DATAELEMS*sizeof(uint64_t))/(NCCL_LL128_LINEELEMS*sizeof(T))/2;
const ssize_t loopSize = args->nChannels*chunkSize;
// Compute pointers
const T * __restrict__ thisInput = (const T*)args->ThisInput;
T * __restrict__ thisOutput = (T*)args->ThisOutput;
for (ssize_t gridOffset = 0; gridOffset < size; gridOffset += loopSize) {
chunkSize = min(DIVUP(size-gridOffset, args->nChannels*minChunkSize)*minChunkSize, chunkSize);
ssize_t chunkOffset = gridOffset + bid*chunkSize;
/////////////// begin ReduceScatter steps ///////////////
ssize_t offset;
int nelem = min(chunkSize, size-chunkOffset);
int rankDest;
// step 0: push data to next GPU
rankDest = ring->devUserRanks[nranks-1];
offset = chunkOffset + rankDest * size;
LLprims.send(thisInput+offset, nelem);
// k-2 steps: reduce and copy to next GPU
for (int j=2; j<nranks; ++j) {
rankDest = ring->devUserRanks[nranks-j];
offset = chunkOffset + rankDest * size;
LLprims.recvReduceSend(thisInput+offset, nelem);
}
// step k-1: reduce this buffer and data, which will produce the final
// result that we store in this data
rankDest = ring->devUserRanks[0];
offset = chunkOffset + rankDest * size;
LLprims.recvReduceCopy(thisInput+offset, thisOutput+chunkOffset, nelem);
}
}
template<int UNUSED, class FUNC, typename T>
__device__ void ncclReduceScatterTreeLL128Kernel(struct CollectiveArgs* args) { }
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