3 papers
cs.LG2024
Understanding and Alleviating Memory Consumption in RLHF for LLMs
Jin Zhou, Hanmei Yang, Steven +4
Fine-tuning with Reinforcement Learning with Human Feedback (RLHF) is essential for aligning large language models (LLMs). However, RLHF often encounters significant memory challen…
cs.DC2024
ProTrain: Efficient LLM Training via Memory-Aware Techniques
Hanmei Yang, Jin Zhou, Yao Fu +4
Memory pressure has emerged as a dominant constraint in scaling the training of large language models (LLMs), particularly in resource-constrained environments. While modern framew…
cs.PF2021
NumaPerf: Predictive and Full NUMA Profiling
Xin Zhao, Jin Zhou, Hui Guan +3
Parallel applications are extremely challenging to achieve the optimal performance on the NUMA architecture, which necessitates the assistance of profiling tools. However, existing…