Distributed machine learning parallel computing frameworks

Build distributed machine learning systems using parallel computing frameworks for large-scale model training and inference. Distributed training strategies: 1. Data parallelism: split data across workers, synchronize gradients, parameter servers or all-reduce. 2. Model parallelism: split model layers, pipeline parallelism, tensor parallelism for large models. 3. Hybrid approaches: combine data and model parallelism, heterogeneous cluster optimization. Synchronization methods: 1. Synchronous SGD: barrier synchronization, consistent updates, communication bottlenecks. 2. Asynchronous SGD: independent worker updates, stale gradients, convergence challenges. 3. Semi-synchronous: bounded staleness, backup workers, fault tolerance. Frameworks and tools: 1. Horovod: distributed deep learning, MPI backend, multi-GPU training, easy integration. 2. PyTorch Distributed: DistributedDataParallel, process groups, NCCL communication. 3. TensorFlow Strategy: MirroredStrategy, MultiWorkerMirroredStrategy, TPU integration. Communication optimization: 1. Gradient compression: sparsification, quantization, error compensation, communication reduction. 2. All-reduce algorithms: ring all-reduce, tree all-reduce, bandwidth optimization. 3. Overlapping: computation and communication overlap, pipeline optimization. Fault tolerance: 1. Checkpoint/restart: periodic model saving, failure recovery, elastic training. 2. Redundant workers: backup workers, speculative execution, dynamic resource allocation. 3. Preemptible instances: spot instance usage, cost optimization, interruption handling. Large model training: 1. Zero redundancy optimizer: ZeRO stages, memory optimization, trillion-parameter models. 2. Gradient checkpointing: memory-time trade-off, recomputation strategies. 3. Mixed precision: FP16/BF16 training, automatic loss scaling, hardware acceleration, training efficiency optimization for multi-node clusters.