4 papers
AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning
Zepeng Li, Jie Ren, Zhanyong Tang +2
Large Language Models (LLMs) show promise for code compilation tasks, but applying them to runtime performance tuning is difficult due to complex microarchitectural effects and noi…
Taming GPU Underutilization via Static Partitioning and Fine-grained CPU Offloading
Gabin Schieffer, Ruimin Shi, Jie Ren +1
Advances in GPU compute throughput and memory capacity brings significant opportunities to a wide range of workloads. However, efficiently utilizing these resources remains challen…
ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training
Yuhang Liang, Xinyi Li, Jie Ren +3
Large Language Models (LLMs) have demonstrated remarkable performance in various natural language processing tasks. However, the training of these models is computationally intensi…
Harnessing Integrated CPU-GPU System Memory for HPC: a first look into Grace Hopper
Gabin Schieffer, Jacob Wahlgren, Jie Ren +2
Memory management across discrete CPU and GPU physical memory is traditionally achieved through explicit GPU allocations and data copy or unified virtual memory. The Grace Hopper S…