3 papers
cs.LG2026
FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost
Chenhao Feng, Haoli Zhang, Shakhzod Ali-Zade +17
Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictio…
cs.CV2025
db-SP: Accelerating Sparse Attention for Visual Generative Models with Dual-Balanced Sequence Parallelism
Siqi Chen, Ke Hong, Tianchen Zhao +4
Scaling Diffusion Transformer (DiT) inference via sequence parallelism is critical for reducing latency in visual generation, but is severely hampered by workload imbalance when ap…
cs.CV2025
PAROAttention: Pattern-Aware ReOrdering for Efficient Sparse and Quantized Attention in Visual Generation Models
Tianchen Zhao, Ke Hong, Xinhao Yang +8
In visual generation, the quadratic complexity of attention mechanisms results in high memory and computational costs, especially for longer token sequences required in high-resolu…