FailBench: Evaluating Fault Tolerance Across Distributed Training Architectures

Abstract

Distributed deep learning relies on data, pipeline, tensor, and hybrid parallelism, yet fault-tolerance mechanisms are typically evaluated only on the architecture for which they were designed. This leaves practitioners with little guidance when choosing mechanisms across architectures. FailBench provides a unified evaluation harness covering seven distributed training architectures, eight crash-fault-tolerance mechanisms and a no-FT baseline, and single, concurrent, and cascading fail-stop failures. We evaluate 142 (architecture, mechanism, trace) combinations on an 8xV100 cluster, with per-rank checkpoint states ranging from 205 MB to 2.7 GB. Three findings emerge. First, no mechanism is universally best: on A2, disk checkpointing has the lowest steady-state overhead (0.5%), in-memory replication restores fastest (~17 ms), and just-in-time checkpointing avoids periodic steady-state checkpoint cost but incurs ~0.9 s upon failure. When process-group re-formation takes seconds, mechanisms differ more in runtime overhead than restore speed. Second, gossip training increases sample throughput by 17.9% after losing a worker, yet shows no detectable improvement in loss progress over a matched no-fault baseline. Third, mechanism cost depends strongly on architecture: in-memory replication overhead ranges from 3.7% to 176%. We translate these findings into a decision framework for selecting fault-tolerance mechanisms and release FailBench as an open artifact.