TurboGR trains up to 0.2B-parameter generative recommendation models on Ascend NPUs at 54.71% MFU with 0.97 near-linear scalability via jagged acceleration, hierarchical parallelism, and negative sampling optimizations.
Fuxi- \gamma: Efficient sequential recommendation with exponential-power temporal encoder and diagonal-sparse positional mechanism
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TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation
TurboGR trains up to 0.2B-parameter generative recommendation models on Ascend NPUs at 54.71% MFU with 0.97 near-linear scalability via jagged acceleration, hierarchical parallelism, and negative sampling optimizations.