NVIDIA NCCL 源码学习（七）- 机器间channel连接

上节中完成了单机内部的channel搜索，仍然以ringGraph为例的话，相当于在单台机器内部搜索出来了一系列的环，接下来需要将机器之间的环连接起来。

为了方便理解假设两机十六卡的情况下第一台机器的一个ring为：

graph->intra: GPU/0 GPU/7 GPU/6 GPU/3 GPU/2 GPU/5 GPU/4 GPU/1
graph->inter: NET/0 NET/0

第二个机器对应的ring为：

graph->intra: GPU/10 GPU/9 GPU/8 GPU/13 GPU/12 GPU/15 GPU/14 GPU/11
graph->inter: NET/0 NET/0

allGather3Data用于rank间聚合channel的信息，ncclGraphInfo记录了环的信息，比如speed和type

struct ncclGraphInfo {int sameChannels;float speedIntra;float speedInter;int typeIntra;};struct {int cudaCompCap;int fullCudaCompCap;int nChannels;struct ncclGraphInfo tree;struct ncclGraphInfo ring;struct ncclGraphInfo collNet;struct ncclTopoRanks topoRanks;} *allGather3Data;NCCLCHECK(ncclCalloc(&allGather3Data, nranks));allGather3Data[rank].cudaCompCap = ncclCudaCompCap();allGather3Data[rank].nChannels = comm->nChannels = treeGraph.nChannels = ringGraph.nChannels =std::min(treeGraph.nChannels, ringGraph.nChannels);...allGather3Data[rank].ring.sameChannels = ringGraph.sameChannels;allGather3Data[rank].ring.speedIntra = ringGraph.speedIntra;allGather3Data[rank].ring.speedInter = ringGraph.speedInter;allGather3Data[rank].ring.typeIntra = ringGraph.typeIntra;...

然后开始设置ncclTopoRanks，获取当前rank在ring中的prev和next，其中第一个rank的prev和最后一个rank的next为-1，如rank6的prev为7，next为3；获取当前ring的ringRecv和ringSend，即ring的第一个节点和最后一个节点，最后将搜索到的环复制了一遍，这里在官方issue中看到相关解释是为了进一步的并行以充分利用带宽。

struct ncclTopoRanks {int ringRecv[MAXCHANNELS];int ringSend[MAXCHANNELS];int ringPrev[MAXCHANNELS];int ringNext[MAXCHANNELS];int treeUpRecv[MAXCHANNELS];int treeUpSend[MAXCHANNELS];int treeDnRecv[MAXCHANNELS];int treeDnSend[MAXCHANNELS];
};ncclResult_t ncclTopoPreset(struct ncclComm* comm,struct ncclTopoGraph* treeGraph, struct ncclTopoGraph* ringGraph, struct ncclTopoGraph* collNetGraph,struct ncclTopoRanks* topoRanks) {int rank = comm->rank;int localRanks = comm->localRanks;int nChannels = comm->nChannels;for (int c=0; c<nChannels; c++) {struct ncclChannel* channel = comm->channels+c;channel->ring.prev = channel->ring.next = -1;...int* ringIntra = ringGraph->intra+c*localRanks;int* treeIntra = treeGraph->intra+c*localRanks;int* collNetIntra = collNetGraph->intra+c*localRanks;for (int i=0; i<localRanks; i++) {if (ringIntra[i] == rank) {topoRanks->ringRecv[c] = ringIntra[0];topoRanks->ringSend[c] = ringIntra[localRanks-1];channel->ring.prev = (i == 0) ? -1 : ringIntra[i-1];channel->ring.next = (i == localRanks-1) ? -1 : ringIntra[i+1];}...}topoRanks->ringPrev[c] = channel->ring.prev;topoRanks->ringNext[c] = channel->ring.next;}// Duplicate channels rings/treesstruct ncclChannel* channel0 = comm->channels;struct ncclChannel* channel1 = channel0+nChannels;memcpy(channel1, channel0, nChannels*sizeof(struct ncclChannel));return ncclSuccess;
}

然后通过bootstrapAllGather获取全局的allGather3Data信息，计算出当前rank所在的node保存在comm->node，以及每个node的第一个rank保存在nodesFirstRank，因此例子中：

nodesFirstRank[0]: 0
nodesFirstRank[1]: 10

然后开始将每个机器的环首尾相连组成大环。

ncclResult_t ncclTopoPostset(struct ncclComm* comm, int* firstRanks, struct ncclTopoRanks** allTopoRanks, int* rings) {// Gather data from all ranksint *ringRecv, *ringSend, *ringPrev, *ringNext, *treeUpRecv, *treeUpSend, *treeDnRecv,*treeDnSend;int nranks = comm->nRanks;int nChannels = comm->nChannels;NCCLCHECK(ncclCalloc(&ringRecv, nranks*MAXCHANNELS));NCCLCHECK(ncclCalloc(&ringSend, nranks*MAXCHANNELS));NCCLCHECK(ncclCalloc(&ringPrev, nranks*MAXCHANNELS));NCCLCHECK(ncclCalloc(&ringNext, nranks*MAXCHANNELS));NCCLCHECK(ncclCalloc(&treeUpRecv, nranks*MAXCHANNELS));NCCLCHECK(ncclCalloc(&treeUpSend, nranks*MAXCHANNELS));NCCLCHECK(ncclCalloc(&treeDnRecv, nranks*MAXCHANNELS));NCCLCHECK(ncclCalloc(&treeDnSend, nranks*MAXCHANNELS));for (int i=0; i<nranks; i++) {for (int c=0; c<nChannels;c++) {ringRecv[c*nranks+i] = allTopoRanks[i]->ringRecv[c];ringSend[c*nranks+i] = allTopoRanks[i]->ringSend[c];ringPrev[c*nranks+i] = allTopoRanks[i]->ringPrev[c];ringNext[c*nranks+i] = allTopoRanks[i]->ringNext[c];treeUpRecv[c*nranks+i] = allTopoRanks[i]->treeUpRecv[c];treeUpSend[c*nranks+i] = allTopoRanks[i]->treeUpSend[c];treeDnRecv[c*nranks+i] = allTopoRanks[i]->treeDnRecv[c];treeDnSend[c*nranks+i] = allTopoRanks[i]->treeDnSend[c];}}// Connect rings and trees. This should also duplicate the channels.NCCLCHECK(connectRings(comm, ringRecv, ringSend, ringPrev, ringNext, firstRanks));NCCLCHECK(connectTrees(comm, treeUpRecv, treeUpSend, treeDnRecv, treeDnSend, firstRanks));// Duplicate ringPrev/ringNext for ncclBuildRingmemcpy(ringPrev+nChannels*nranks, ringPrev, nChannels*nranks*sizeof(int));memcpy(ringNext+nChannels*nranks, ringNext, nChannels*nranks*sizeof(int));// Duplication should be complete nownChannels = comm->nChannels = std::min(MAXCHANNELS,nChannels*2);// Honor NCCL_MIN_NRINGS/NCCL_MAX_NRINGS.// We permit combining max, then min, to only use the first channels, then duplicate them.nChannels = comm->nChannels = std::min((int)ncclMaxNchannels(), nChannels);int c;for (c=nChannels; c<ncclMinNchannels(); c++) {memcpy(ringPrev+c*nranks, ringPrev+(c-nChannels)*nranks, nranks*sizeof(int));memcpy(ringNext+c*nranks, ringNext+(c-nChannels)*nranks, nranks*sizeof(int));memcpy(comm->channels+c, comm->channels+c-nChannels, sizeof(struct ncclChannel));}nChannels = comm->nChannels = c;// Create rings array and check all is fineNCCLCHECK(ncclBuildRings(nChannels, rings, comm->rank, comm->nRanks, ringPrev, ringNext));free(ringRecv);free(ringSend);free(ringPrev);free(ringNext);free(treeUpRecv);free(treeUpSend);free(treeDnRecv);free(treeDnSend);return ncclSuccess;
}

这里将所有channel的prev，next，send，recv信息打平到数组中，例如recv[0]表示第一个ring中rank0的recv是哪个rank，然后开始计算当前机器第一个rank的prev和最后一个rank的next。

static ncclResult_t connectRings(struct ncclComm* comm, int* ringRecv, int* ringSend, int* ringPrev, int* ringNext, int* firstRanks) {int nChannels = comm->nChannels;int nNodes = comm->nNodes;for (int c=0; c<nChannels; c++) {int* recv = ringRecv+c*comm->nRanks;int* send = ringSend+c*comm->nRanks;int* prev = ringPrev+c*comm->nRanks;int* next = ringNext+c*comm->nRanks;struct ncclChannel* channel0 = comm->channels+c;struct ncclChannel* channel1 = channel0+nChannels;for (int n=0; n<nNodes; n++) {int recvRank = recv[firstRanks[n]];int prevSendRank = send[firstRanks[(n-1+nNodes)%nNodes]];prev[recvRank] = prevSendRank;if (comm->rank == recvRank) {channel0->ring.prev = prevSendRank;channel1->ring.prev = prevSendRank;}int sendRank = send[firstRanks[n]];int nextRecvRank = recv[firstRanks[(n+1)%nNodes]];next[sendRank] = nextRecvRank;if (comm->rank == sendRank) {channel0->ring.next = nextRecvRank;channel1->ring.next = nextRecvRank;}}TRACE(NCCL_GRAPH, "Ring %d : %d -> %d -> %d", c, channel0->ring.prev, comm->rank, channel0->ring.next);TRACE(NCCL_GRAPH, "Ring %d : %d -> %d -> %d", c+nChannels, channel1->ring.prev, comm->rank, channel1->ring.next);}return ncclSuccess;
}

如上所示，当前机器recv rank的prev就是前一个机器的send rank，当前机器send rank的next就是下一个机器的recv rank。然后执行ncclBuildRings按照大环的顺序依次记录rank到rings。

ncclResult_t ncclBuildRings(int nrings, int* rings, int rank, int nranks, int* prev, int* next) {for (int r=0; r<nrings; r++) {char prefix[30];int current = rank;for (int i=0; i<nranks; i++) {rings[r*nranks+i] = current;current = next[r*nranks+current];}...// Check that all ranks are therefor (int i=0; i<nranks; i++) {int found = 0;for (int j=0; j<nranks; j++) {if (rings[r*nranks+j] == i) {found = 1;break;}}if (found == 0) {WARN("Error : ring %d does not contain rank %d", r, i);return ncclInternalError;}}}return ncclSuccess;
}

还是以上述为例，其中rank6记录的rings的第一个大环为：

GPU/6 GPU/3 GPU/2 GPU/5 GPU/4 GPU/1 GPU/10 GPU/9 GPU/8 GPU/13 GPU/12 GPU/15 GPU/14 GPU/11 GPU/0 GPU/7

到这里就完成了机器之间大环建立，每个rank都知道自己的上一个和下一个rank是谁，那么就可以建立实际的通信链路了。

接下来每个rank都要为通信分配一些内存，为了提高性能，这里会在分配buffer之前设置cpu亲和性，使得分配的内存尽量是当前numa本地的。

  cpu_set_t affinitySave;sched_getaffinity(0, sizeof(cpu_set_t), &affinitySave);NCCLCHECK(ncclTopoSetAffinity(comm->topo, comm->rank));ncclResult_t ncclTopoSetAffinity(struct ncclTopoSystem* system, int rank) {struct ncclTopoNode* cpu = NULL, *gpu = NULL;for (int g=0; g<system->nodes[GPU].count; g++) {if (system->nodes[GPU].nodes[g].gpu.rank == rank) {gpu = system->nodes[GPU].nodes+g;// Find closer CPUint cpuIndex = -1, minHops = 0;for (int c=0; c<system->nodes[CPU].count; c++) {int nHops = system->nodes[GPU].nodes[g].paths[CPU][c].count;if (cpuIndex == -1 || nHops < minHops) {cpuIndex = c;minHops = nHops;}}cpu = system->nodes[CPU].nodes+cpuIndex;}}if (cpu == NULL) {WARN("Set CPU affinity : unable to find GPU/CPU for rank %d", rank);return ncclInternalError;}// Query the CPU affinity set we were providedcpu_set_t mask;SYSCHECK(sched_getaffinity(0, sizeof(cpu_set_t), &mask), "sched_getaffinity");// Get the affinity of the CPU close to our GPU.cpu_set_t cpuMask = cpu->cpu.affinity;cpu_set_t finalMask;if (ncclParamIgnoreCpuAffinity())// Ignore the CPU affinity set and use the GPU one insteadfinalMask = cpuMask;else// Use a subset of the GPU affinity setCPU_AND(&finalMask, &mask, &cpuMask);// If there is a non empty set, use it to set affinityif (CPU_COUNT(&finalMask)) {char affinityStr[sizeof(cpu_set_t)*2];NCCLCHECK(ncclCpusetToStr(&finalMask, affinityStr));INFO(NCCL_INIT, "Setting affinity for GPU %d to %s", gpu->gpu.dev, affinityStr);SYSCHECK(sched_setaffinity(0, sizeof(cpu_set_t), &finalMask), "sched_setaffinity");}return ncclSuccess;
}

首先获取当前线程的cpu亲和性保存到affinitySave，分配好buffer之后会用affinitySave来恢复亲和性。

然后通过ncclTopoSetAffinity设置cpu亲和性，找到当前rank对应的cpu节点之后，可以获取到该cpu对应的core，即cpuMask，然后获取当前线程对应的亲和性，即mask，默认会取cpuMask和mask的交集finalMask，如果交集不为空的话，会将finalMask设置给当前线程。

struct ncclConnect {char data[CONNECT_SIZE];
};  struct ncclConnect *connect;NCCLCHECKGOTO(ncclCalloc(&connect, 2), ret, affinity_restore);for (int c=0; c<comm->nChannels; c++) {struct ncclChannel* channel = comm->channels+c;NCCLCHECKGOTO(setupChannel(comm, c, rank, nranks, rings+c*nranks), ret, affinity_restore);if (comm->nRanks == 1) continue;NCCLCHECKGOTO(ncclTransportP2pSetup(comm, &ringGraph, channel, 1, &channel->ring.prev, 1, &channel->ring.next), ret, affinity_restore);...}

然后简单看下ncclChannel数据结构，其中collectives保存了用户向nccl提交的通信操作，比如ncclSend，ncclRecv等都会向collectives里加一项，ncclColl则保存了这些操作对应的参数；collectives是一个环形队列，所以collStart指向了开始位置，collCount表示队列中操作数量；FifoHead和FifoTail用于协调kernel产出数据和NET发送数据，其实就是生产者消费者，ncclPeer保存了通信相关的信息，后续再具体介绍。

struct ncclRing {// Shortcuts for userRanks[1] and userRanks[n-1]int prev;  // 记录环中当前rank的上一个rankint next;  // 记录环中当前rank的下一个rank// Maps an internal nccl index to user-specified rank order. This is necessary// since we need to know how the user expects data to be ordered across// devices. Ordered from current device.int* userRanks;  // 以当前rank为起点记录整个环int* devUserRanks;  // device断的userRanks
};struct ncclChannel {union {struct {struct ncclRing ring;struct ncclTree treeUp;struct ncclTree treeDn;struct ncclTree collTreeUp;struct ncclTree collTreeDn;int id; // Communication structuresstruct ncclPeer* peers;struct ncclPeer* devPeers;// Operation list for aggregationstruct ncclColl* collectives;int collStart;int collCount;int collFifoHead; // Only used by GPUint collFifoTail; // Only used by CPU};  int data[0x80];};
};

然后开始初始化channel，initChannel主要是buffer的分配，分配userRanks和devUserRanks，设置ncclPeer，分配collectives，因为host和device都会访问collectives这个数据结构，所以需要通过cudaHostAlloc分配host端的锁页内存，并通过flag cudaHostAllocMapped将其映射到cuda的地址空间。不过在uva系统上，cudaMallocHost，cudaHostAlloc + cudaHostAllocDefault以及cudaHostAlloc + cudaHostAllocMapped这三种方式没啥区别，host和device都可以访问。

ncclResult_t initChannel(struct ncclComm* comm, int channelid) {struct ncclChannel* channel = comm->channels+channelid;if (channel->id != -1) return ncclSuccess;channel->id = channelid;// Ring index to user rank table.NCCLCHECK(ncclCudaCalloc(&channel->ring.devUserRanks, comm->nRanks));NCCLCHECK(ncclCalloc(&channel->ring.userRanks, comm->nRanks));// Communication structures with peers.NCCLCHECK(ncclCudaCalloc(&channel->devPeers, comm->nRanks+1)); // The extra one rank is for collnet root (i.e. network)NCCLCHECK(ncclCalloc(&channel->peers, comm->nRanks+1));for (size_t i=0; i<comm->nRanks+1; ++i) {channel->peers[i].send.comm = comm;channel->peers[i].recv.comm = comm;}// Per-channel operation list.NCCLCHECK(ncclCudaHostCalloc(&channel->collectives, NCCL_MAX_OPS));return ncclSuccess;
}template <typename T>
static ncclResult_t ncclCudaHostCalloc(T** ptr, size_t nelem) {CUDACHECK(cudaHostAlloc(ptr, nelem*sizeof(T), cudaHostAllocMapped));memset(*ptr, 0, nelem*sizeof(T)); return ncclSuccess;
}

然后从当前rank为起点，将环写到userRanks。

static ncclResult_t setupChannel(struct ncclComm* comm, int channelId, int rank, int nranks, int* ringRanks) {TRACE(NCCL_INIT, "rank %d nranks %d", rank, nranks);NCCLCHECK(initChannel(comm, channelId));struct ncclRing* ring = &comm->channels[channelId].ring;// Reorganize ranks to start with rank.int shift;for (shift = 0; shift<nranks; shift++) {if (ringRanks[shift] == rank) {break;}}for (int i=0; i<nranks; i++) {ring->userRanks[i] = ringRanks[(i+shift)%nranks];}return ncclSuccess;
}

然后执行ncclTransportP2pSetup建立当前rank和prev，next的通信链路。

到这里就完成了机器之间channel的连接，下节会了解到通信链路的建立过程。

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