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Scaling Like a Pro: Zero Bubble Pipeline Parallelism Demystified
Pipeline parallelism is key to efficient distributed training for large-scale models, but its performance is often hindered by pipeline bubbles, which are gaps in computation that limit throughput. A recent paper introduces a breakthrough zero-bubble scheduling strategy, achieving up to 30% throughput improvement. In this post, we demystify the scheduling process with detailed, step-by-step illustrations, providing clarity and context that complement the original work. Whether you're new to ML systems or a seasoned researcher, this post bridges the gap between high-level concepts and practical understanding with fresh and accessible perspectives.