Python和c++代码实现高性能异构分布式并行互联系统

import torch.distributed as dist

def init_process(rank, size, backend='nccl'):
    dist.init_process_group(backend, rank=rank, world_size=size)
    torch.cuda.set_device(rank)

def send_tensor(tensor, target_rank):
    dist.send(tensor, dst=target_rank)

def receive_tensor(tensor, source_rank):
    dist.recv(tensor, src=source_rank)

def simple_scheduler(tasks, world_size):
    schedule = {i: [] for i in range(world_size)}
    for i, task in enumerate(tasks):
        schedule[i % world_size].append(task)
    return schedule

def execute_tasks(tasks):
    for task in tasks:
        task()

class DistributedCache:
    def __init__(self):
        self.cache = {}

    def get(self, key):
        return self.cache.get(key, None)

    def put(self, key, value):
        self.cache[key] = value

cache = DistributedCache()

def get_data(key):
    data = cache.get(key)
    if data is None:
        data = fetch_data_from_storage(key)  # 假设这个函数从存储中获取数据
        cache.put(key, data)
    return data

import torch

def monitor_load(rank):
    load = torch.cuda.memory_reserved(rank) / torch.cuda.max_memory_reserved(rank)
    return load

def balance_load(tasks, world_size):
    loads = [monitor_load(rank) for rank in range(world_size)]
    min_load_rank = loads.index(min(loads))
    execute_tasks(tasks[min_load_rank])

def check_node_alive(rank):
    try:
        dist.barrier()
        return True
    except Exception as e:
        print(f"Node {rank} failed: {e}")
        return False

def recover_from_failure(rank, tasks):
    if not check_node_alive(rank):
        redistribute_tasks(tasks)

def async_send_receive(tensor, target_rank, stream=None):
    if stream is None:
        stream = torch.cuda.current_stream()
    
    stream.synchronize()
    send_tensor(tensor, target_rank)
    receive_tensor(tensor, target_rank)
    stream.synchronize()

import logging

logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')

def log_event(event):
    logging.info(event)

def monitor_performance(rank):
    usage = monitor_load(rank)
    log_event(f"GPU {rank} load: {usage * 100}%")

def main(rank, size):
    init_process(rank, size)

    tasks = [lambda: torch.cuda.synchronize(rank) for _ in range(10)]
    schedule = simple_scheduler(tasks, size)
    
    # 执行任务
    execute_tasks(schedule[rank])
    
    # 监控和日志
    monitor_performance(rank)
    
    # 故障检测与恢复
    recover_from_failure(rank, tasks)

if __name__ == "__main__":
    world_size = 4
    torch.multiprocessing.spawn(main, args=(world_size,), nprocs=world_size, join=True)

// 使用NCCL进行GPU之间的通信
ncclComm_t comm;
ncclCommInitRank(&comm, numDevices, ncclId, rank);

// 发送数据
ncclSend(buffer, size, ncclInt, targetRank, comm, stream);

// 接收数据
ncclRecv(buffer, size, ncclInt, sourceRank, comm, stream);

ncclCommDestroy(comm);

// 简单的轮询调度算法
int nextNode = (currentNode + 1) % totalNodes;
sendTaskToNode(task, nextNode);

// 简单的分布式缓存实现
std::unordered_map<int, Data> cache;

if (cache.find(dataId) == cache.end()) {
    Data data = fetchDataFromStorage(dataId);
    cache[dataId] = data;
}

// 简单的负载均衡策略
if (nodeLoad[currentNode] > threshold) {
    migrateTaskToNode(task, findLeastLoadedNode());
}

// 简单的故障检测与恢复机制
if (!isNodeAlive(node)) {
    redistributeTasksFromNode(node);
    restartNode(node);
}

// 使用CUDA进行数据处理
__global__ void processData(float* data, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        data[idx] = sqrt(data[idx]);
    }
}
processData<<<blocks, threads>>>(deviceData, dataSize);

// 简单的日志记录功能
void logEvent(const std::string& event) {
    std::ofstream logFile("system.log", std::ios_base::app);
    logFile << "[" << getCurrentTime() << "] " << event << std::endl;
}