Fault Tolerance in High-Performance Computing for Distributed Large Language Model Training
fault tolerance; checkpointing; heartbeat; distributed training; large language models; data parallelism; high-performance computing; preemptible instances.
The distributed training of large language models (LLMs) in High-Performance Computing (HPC) architectures faces significant cost and fault tolerance challenges. As the number of compute nodes increases, so does the probability of failures, while cloud instance preemption and hardware faults can interrupt entire training runs and result in substantial computation loss. This research proposes the integration of DeLIA (Dependability Library for Iterative Applications), an application-level fault tolerance library for HPC, into data-parallel LLM training through the DeLIApy Python wrapper. The methodology comprises four stages: DeLIApy packaging and distribution, baseline training setup, integration and overhead evaluation, and resilient execution in the presence of failures. As a contribution, this work provides a fault tolerance approach accessible to the machine learning ecosystem and empirically evaluates its applicability to distributed LLM training.