What is inference pipeline graph partitioning in distributed ML systems?

Pipeline graph partitioning splits inference computation into optimized subgraphs executed across distributed nodes.

Large ML inference graphs are partitioned into subgraphs to optimize latency, memory usage, and hardware utilization. Each partition may run on different hardware (CPU, GPU, TPU). The partitioning algorithm considers operator dependencies, communication cost, and execution cost. Frameworks like TensorRT and ONNX Runtime use graph partitioning for performance optimization.