3 papers
cs.DC2026
COPUS: Co-adaptive Parallelism and Batch Size Selection in Large Language Model Training
Akhmed Sakip, Erland Hilman Fuadi, Omar Sayedelahl +6
Training large language models requires jointly configuring two interdependent aspects of the system: the global batch size, which governs statistical efficiency, and the 3D parall…
cs.LG2026
Log-Linear Attention
Han Guo, Songlin Yang, Tarushii Goel +3
The attention mechanism in Transformers is an important primitive for accurate and scalable sequence modeling. Its quadratic-compute and linear-memory complexity however remain sig…
cs.LG2025
Fast Matrix Multiplications for Lookup Table-Quantized LLMs
Han Guo, William Brandon, Radostin Cholakov +3
The deployment of large language models (LLMs) is often constrained by memory bandwidth, where the primary bottleneck is the cost of transferring model parameters from the GPU's gl…