Scheduling Data to Compute: Hierarchical Data Pipelining for LLM Training on AMD GPUs

Large language models (LLM) have been the key focus of recent machine learning breakthroughs. As the model size grows exponentially, the computation needed for training is astronomical. A GPU can do a huge amount of arithmetic. However, the data its compute units need is not always available when they are ready. This data may still be in slow memory or on another GPU. While the chip waits, expensive compute sits idle, and at scale even a small drop in utilization translates into substantial cos…

Source: arXiv cs.AR (Hardware Architecture), published 2026-11-05. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern