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cs.DC2026
A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM
Shaoke Xi, ChonLam Lao, Boyi Jia +11
Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this scale enables rapid model advances, developing, debugging, and performance-tuning…
cs.DB2026
Efficient K-generalizable Learned Search
Yifan Peng, Jiafei Fan, Xingda Wei +7
Learned top-K search improves the accuracy-latency trade-off of graph-based vector search, but existing methods target a fixed K. Serving real-world workloads with various K values…
cs.DC2026
Simple is Better: Multiplication May Be All You Need for LLM Request Scheduling
Dingyan Zhang, Jinbo Han, Kaixi Zhang +6
High-quality LLM request scheduling requires meeting two key objectives: ensuring the routed instance has KVCache to accelerate request execution, and ensuring that the workload is…