TrainBridge: Agentic NPU Training Optimization Guided by GPU Compilation Evidence
To make training on NPUs more efficient, we propose TrainBridge, an agentic approach for transferring GPU optimization guidance to NPU training.
ProofPaper ↗
Key points
- We study how to improve training efficiency on Ascend NPUs.
- In the NPU training programs we study, framework-level compilation optimizations such as operator fusion are only partially applied, leaving fragmented computation and avoidable execution overheads.
- TrainBridge uses the highly optimized execution produced by mature GPU compilation stacks as a task-specific reference for NPU tuning.
- Our GPU-to-NPU migration experiments show that TrainBridge achieves an average 1.7x speedup across four workloads covering LLM pretraining, image classification, and vision-language training, and an average speedup of nearly 2.0x across 27 Modded-NanoGPT speedrun records with highly optimized GPU implementations.
Sources (1)
- [1]TrainBridge: Agentic NPU Training Optimization Guided by GPU Compilation EvidencearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 04:00 AM
To make training on NPUs more efficient, we propose TrainBridge, an agentic approach for transferring GPU optimization guidance to NPU training.
We study how to improve training efficiency on Ascend NPUs.
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