{ p2pLevel = ncclTopoUserP2pLevel; goto compare; } // Don't use P2P through ARM CPUs int arch, vendor, model; NCCLCHECK(ncclTopoCpuType(system, &arch, &vendor, &model)); if (arch == NCCL_TOPO_CPU_ARCH_ARM) p2pLevel = PATH_PXB; if (arch == NCCL_TOPO_CPU_ARC...
‣ Support the use of a different NCCL_NET parameter per communicator. ‣ Add support for SHM and P2P transfers using cudaMemcpy. Fixed Issues The following issues have been resolved in NCCL 2.13.4: ‣ Fix multi-receive size encoding which could cause flush to be skipped in corner ...
xmlGetAttrIndex(gpuNode, "dev", &index)); if (index == -1) { if (nvmlDev == NULL) { WARN("No NVML, trying to use CUDA instead"); const char* busId; NCCLCHECK(xmlGetAttr(pciNode, "busid", &busId)); if (busId == NULL || cudaDeviceGetByPCIBusId(&dev...
CUDACHECK(cudaMalloc(&gpuPtr, GPU_BUF_SIZE)); ncclDebugNoWarn = NCCL_NET; if (ncclNetRegMr(sComm, gpuPtr, GPU_BUF_SIZE, NCCL_PTR_CUDA, &mHandle) == ncclSuccess) { NCCLCHECK(ncclNetDeregMr(sComm, mHandle)); NCCLCHECK(ncclNetRegMr(rComm, gpuPtr, GPU_BUF_SIZE, NCCL_PTR_CUDA, ...
CUDACHECK(cudaMalloc(&gpuPtr, GPU_BUF_SIZE)); ncclDebugNoWarn = NCCL_NET; if (ncclNetRegMr(sComm, gpuPtr, GPU_BUF_SIZE, NCCL_PTR_CUDA, &mHandle) == ncclSuccess) { NCCLCHECK(ncclNetDeregMr(sComm, mHandle)); NCCLCHECK(ncclNetRegMr(rComm, gpuPtr, GPU_BUF_SIZE, NCCL_PTR_CUDA, ...
然后通过ncclTopoCheckP2p检查当前GPU节点和其他所有的GPU节点之间是否可以使用p2p通信,其实就是判断gpu1到gpu2的路径type是否满足p2pLevel的限制,默认p2pLevel是PATH_SYS,如果用户没有通过环境变量设置的话就相当于没有限制,任意gpu之间都是支持p2p通信,另外如果路径类型为PATH_NVL的话,那么还支持p2p read。 ncclResult...
if ((props.ptrSupport & NCCL_PTR_CUDA) == 0) continue; // Allocate memory on the GPU and try to register it on the NIC. void *lComm = NULL, *sComm = NULL, *rComm = NULL; ncclNetHandle_t handle; void* gpuPtr = NULL; ...
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 + cu...
// 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);intc;for(c=nChannels;c<ncclMinNchannels();c++) {memcpy(ringPrev+c*nranks, ...
NCCL does not use cudaMemcpy aside from the initial setup (ncclCommInit*). Most of the time, the memory shared by the CPU and GPU is in CPU memory, and we then register it on the GPU using cudaHostRegister or allocating it with cudaHostAlloc. That way both the CPU and GPU can acce...