SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs

TL;DR

SPARe introduces a fault-tolerance framework for large-scale LLM pretraining that masks node failures with minimal overhead, significantly reducing training time at extreme GPU scales.

Contribution

It proposes SPARe, a novel fault-tolerance method combining stacked redundancy and adaptive reordering, optimized for 100k+ GPU systems with minimal overhead.

Findings

SPARe maintains high availability with only 2-3x overhead.

Simulation validates theoretical models for failure tolerance.

At 600k GPUs, SPARe cuts training time by 40-50%.

Abstract

In large-scale LLM pre-training systems with 100k+ GPUs, failures become the norm rather than the exception, and restart costs can dominate wall-clock training time. However, existing fault-tolerance mechanisms are largely unprepared for this restart-dominant regime. To address this challenge, we propose SPARe - Stacked Parallelism with Adaptive Reordering - a fault-tolerance framework that masks node failures during gradient synchronization by stacking redundant data shards across parallelism groups and adaptively reordering execution. SPARe achieves availability comparable to traditional replication while maintaining near-constant computation overhead of only 2~3x, even under high redundancy where traditional replication would require linearly inflating overhead. We derive closed-form expressions for endurable failure count and computation overhead, validate them via SimGrid-based discrete-event simulation, and jointly optimize redundancy and checkpointing to minimize time-to-train. At extreme scale with up to 600k GPUs, SPARe reduces time-to-train by 40~50% compared to traditional replication.