5 papers
Guard: Scalable Straggler Detection and Node Health Management for Large-Scale Training
Guanliang Liu, Abhinandan Patni, Congzhu Lin +14
Training frontier-scale foundation models involves coordinating tens of thousands of GPUs over multi-month runs, where even minor performance degradations can accumulate into subst…
Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
Peipei Xu, SiYuan Ma, Yaohua Liu +4
Generating feasible Pareto fronts for constrained bi-objective continuous optimization is central to multi-criteria decision-making. Existing methods usually rely on iterative scal…
CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF
Lankadinee Rathuwadu, Guanli Liu, Christopher Leckie +1
Cardinality estimation (CE), the task of predicting the result size of queries is a critical component of query optimization. Accurate estimates are essential for generating effici…
Benchmarking RL-Enhanced Spatial Indices Against Traditional, Advanced, and Learned Counterparts
Guanli Liu, Renata Borovica-Gajic, Hai Lan +1
Reinforcement learning has recently been used to enhance index structures, giving rise to reinforcement learning-enhanced spatial indices (RLESIs) that aim to improve query efficie…
DriftBench: Defining and Generating Data and Query Workload Drift for Benchmarking
Guanli Liu, Renata Borovica-Gajic
Data and workload drift are key to evaluating database components such as caching, cardinality estimation, indexing, and query optimization. Yet, existing benchmarks are static, of…