Distributed Learning

The most common distributed training strategy where the complete neural network model is replicated across all worker nodes. Each worker receives a distinct mini-batch of the PA measurement dataset, computes gradients independently, and then synchronizes these gradients through an all-reduce operation before updating model weights. This approach excels when the model fits comfortably in a single GPU's memory but the dataset is too large to process sequentially. The primary bottleneck is the communication overhead of gradient synchronization, which scales with model size.

A distribution strategy where the neural network architecture is partitioned across multiple devices, with each worker responsible for computing a subset of layers or neurons. This is critical for extremely large predistorter models—such as deep residual networks with millions of parameters—that exceed the memory capacity of a single accelerator. Two variants exist:

• Pipeline parallelism: Layers are assigned to sequential devices, with micro-batches streamed through the pipeline
• Tensor parallelism: Individual layer operations are sharded across devices Model parallelism introduces device-to-device communication during both forward and backward passes.

Combines data and model parallelism to scale training across hundreds or thousands of accelerators. In a typical configuration for large-scale PA behavioral modeling, model parallelism is applied within a node (using high-bandwidth NVLink interconnects), while data parallelism is used across nodes (using InfiniBand or Ethernet). This hierarchical approach minimizes communication bottlenecks by aligning the parallelism strategy with the underlying hardware topology. Frameworks like DeepSpeed and PyTorch FSDP automate this complexity.

Two fundamental coordination protocols for gradient aggregation:

• Synchronous training: All workers must complete their forward and backward passes before a coordinated weight update occurs. This guarantees identical model parameters across workers but suffers from straggler effects, where the slowest device dictates iteration speed.
• Asynchronous training: Workers update a central parameter server independently without waiting for peers. This eliminates straggler bottlenecks but introduces stale gradient problems, where a worker computes gradients using outdated model weights, potentially harming convergence stability for predistorter training.

Techniques to reduce the communication overhead of synchronizing high-dimensional gradient tensors across nodes, which is often the dominant bottleneck in distributed predistorter training. Key methods include:

• Gradient quantization: Reducing 32-bit floating-point gradients to 8-bit or lower precision before transmission
• Gradient sparsification: Transmitting only the top-k gradient elements by magnitude, discarding near-zero updates
• Deep gradient compression: Combining momentum correction, local gradient clipping, and momentum factor masking to achieve over 600x compression ratios without significant accuracy loss These techniques are critical when training over commodity Ethernet interconnects.

Mechanisms that allow distributed training jobs to survive node failures and dynamically scale resources. For long-running PA model training sessions that may span days, checkpointing periodically saves model state and optimizer momentum buffers to durable storage. Elastic training enables adding or removing worker nodes mid-training without restarting, adjusting the global batch size and learning rate accordingly. Frameworks like Horovod and PyTorch Elastic provide native support for preemption handling and dynamic resizing in Kubernetes-managed GPU clusters.

The dominant distributed training strategy where the complete neural network model is replicated across all worker nodes. Each worker processes a distinct mini-batch of the PA measurement dataset, computes local gradients, and synchronizes with peers via an all-reduce operation before updating weights.

• Synchronous SGD: All workers wait for the slowest node before proceeding
• Asynchronous SGD: Workers update a central parameter server independently
• Critical for scaling batch sizes when training on massive wideband I/Q captures

A partitioning strategy where different layers or sub-networks of the neural network predistorter are assigned to separate compute nodes. Essential when a single deep network—such as a cascade of augmented Hammerstein blocks—exceeds the memory capacity of a single GPU.

• Pipeline parallelism: Layers are streamed sequentially across devices
• Tensor parallelism: Individual weight matrices are sharded across nodes
• Reduces per-device memory footprint for very deep residual architectures

The collective communication primitive that aggregates gradient vectors from all distributed workers and distributes the summed result back to every node. Ring all-reduce and tree-based all-reduce topologies minimize bandwidth contention during the synchronization barrier.

• Gradient averaging ensures mathematical equivalence to single-node training
• Communication overhead becomes the primary bottleneck at scale
• Overlapped computation and communication via gradient bucketing

A centralized or sharded key-value store that maintains the master copy of all neural network weights. Worker nodes pull the latest parameters, compute gradients on their local PA data shards, and push updates back to the server. Enables asynchronous training where stragglers do not block the global model.

• Supports elastic scaling with dynamic worker addition
• Stale gradient tolerance must be tuned for PA model convergence
• Often combined with consistent hashing for distributed parameter shards

A communication-efficient algorithm where workers perform multiple local SGD steps on their private PA measurement partitions before sending model updates to a central coordinator. The coordinator averages the received weights to produce a new global model.

• Reduces communication rounds by 10-100× compared to synchronous SGD
• Naturally handles non-IID data distributions across measurement campaigns
• Critical for privacy-preserving DPD when PA data cannot leave the test bench

Techniques to reduce the communication bottleneck by transmitting sparsified or quantized gradients instead of full 32-bit floating-point tensors. Top-k sparsification sends only the largest-magnitude gradient elements, while 8-bit quantization reduces per-element bandwidth by 4×.

• Error feedback accumulation preserves convergence guarantees
• Deep gradient compression achieves 300-600× bandwidth reduction
• Essential for distributed training over bandwidth-constrained lab networks