Hybrid parallelism underpins large-scale LLM training across tens of thousands of GPUs. At such scale, hardware failures on individual devices lead to performance skew across devices, diminishing overall training efficiency. Existing resilient systems overlook sequence length variability in datasets and device performance skew under hybrid parallelism. As a result, (1) iteration time fluctuations induced by sequence length variability can trigger spurious fail-slow detections, and (2) failures are mitigated through individual adaptations in hybrid parallelism, leading to unnecessary detection overhead and inefficient resilient training.

Memory-based Temporal Graph Neural Networks (M-TGNNs) demonstrate superior performance in dynamic graph learning tasks. Their success attributes to a memory module, which captures historical information for each node and implicitly creates a memory dependency constraint among chronologically ordered minibatches. This unique characteristic of M-TGNN introduces new challenges for parallel training that have not been encountered before. Existing parallelism strategies for M-TGNN either sacrifice memory accuracy (minibatch parallelism and epoch parallelism) or compromise space efficiency (memory parallelism) to optimize runtime. This paper proposes a pipeline parallel approach for multi-GPU M-TGNN training that effectively addresses both inter-minibatch memory dependencies and intra-minibatch task dependencies, based on a runtime analysis DAG for M-TGNNs. We further optimize pipeline efficiency by incorporating improved scheduling, finer-grained operation reorganization, and targeted communication optimizations tailored to the specific training properties of M-TGNN. These enhancements significantly reduce GPU waiting and idle time caused by memory dependencies and frequent communication and result in zero pipeline bubbles for common training configurations. Extensive evaluations demonstrate that PipeTGL achieves a speedup of 1.27x to 4.74x over other baselines while also improving the accuracy of M-TGNN training across multiple GPUs.