3 papers
cs.IR2026
Optimizing Effective Training Time for Large-Scale Recommendation Systems
Mingming Ding, Ruilin Chen, Yuzhen Huang +29
Lifecycle overhead silently consumes accelerator capacity across large-scale recommendation training fleets. Our largest recommendation workloads process tens of billions train- in…
cs.IR2026
Component Benchmark: Hierarchical Model Profiling for Large-scale Recommendation Systems
Dharak Kharod, Yuzhen Huang, Zhou Wang +11
Large-scale recommendation models pose distinct, under-explored profiling challenges. Most recommendation model architectures are structurally heterogeneous, intermixing memory-ban…
cs.AR2025
Sustainable AI Training via Hardware-Software Co-Design on NVIDIA, AMD, and Emerging GPU Architectures
Yashasvi Makin, Rahul Maliakkal
In particular, large-scale deep learning and artificial intelligence model training uses a lot of computational power and energy, so it poses serious sustainability issues. The fas…