Distributed Deep Learning Training

  1. Performance Optimization and Tuning
    1. Profiling Distributed Training
      1. Communication Profiling
        1. Bandwidth Utilization
          1. Latency Measurement
            1. Bottleneck Identification
            2. Computation Profiling
              1. GPU Utilization
                1. Memory Usage Analysis
                  1. Kernel Performance
                  2. End-to-End Performance Analysis
                    1. Training Throughput
                      1. Scaling Efficiency
                        1. Resource Utilization
                      2. Hyperparameter Tuning
                        1. Learning Rate Scaling
                          1. Batch Size Selection
                            1. Communication Frequency
                              1. Gradient Accumulation Steps
                              2. Load Balancing
                                1. Work Distribution
                                  1. Dynamic Load Balancing
                                    1. Straggler Mitigation
                                      1. Resource Monitoring
                                      2. Memory Management
                                        1. Memory Pool Optimization
                                          1. Garbage Collection Tuning
                                            1. Memory Fragmentation Reduction
                                              1. Out-of-Memory Prevention

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                                            8. Frameworks and Libraries

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                                            10. Practical Implementation