TempoTrace improves timing accuracy for tracing AI infrastructure
Accurate Distributed Tracing for Large-Scale AI Infrastructure: Time Synchronization as a Foundation for Reliable Observability
Distributed, Parallel, and Cluster Computing
Summary
When many computers work together to run artificial intelligence tasks, knowing the exact order of events is very important for fixing problems and understanding performance. The authors show that usual time synchronization methods often mix up the order of events, causing unreliable results. They designed TempoTrace, which uses very precise clock synchronization and smart tracking to keep events in the right order even in large GPU clusters. This system helps detect faults more reliably and lets users specify how precise timing should be.
What this means in practice
- •For cloud and data center engineers: Improve fault detection and performance diagnosis in large AI GPU clusters by ensuring accurate event order with precise time synchronization.
- •For network infrastructure teams: Use P4-based in-band telemetry combined with precise timing to correct network delay asymmetry and increase the accuracy of fault attribution in multi-tenant GPU fabric networks.
Authors
Hesham Elbakoury, Ankur Sharma
Abstract
Distributed tracing in large-scale AI infrastructure fails silently when clock accuracy is insufficient: causal events are misordered, fault attribution is corrupted, and performance diagnoses are unreliable. We present TempoTrace, a system that co-designs IEEE 1588v2 PTP time synchronization with distributed tracing to preserve causal ordering across heterogeneous, multi-tenant GPU clusters. We formally prove that NTP-grade clocks produce causal inversions at 25-30% of directed operation pairs under WAN/cloud conditions (11.3% under LAN NTP). A Laplace heavy-tail noise model extends the analysis beyond Gaussian assumptions, giving a weighted expected misorder rate of 27.76% vs. 29.37% Gaussian, confirming robustness to tail shape. TempoTrace reduces GPU-to-host timestamp uncertainty from 2.1 us to a design target of 0.056 us residual standard deviation via GPUDirect RDMA hardware timestamping, with sub-100 ns PTP synchronization. A bounded Sketch Vector Clock tracks causal relationships with false-positive rate below 10^-5 for up to four participants. A hybrid rule-based and XGBoost diagnosis engine achieves macro-F1 0.974 in controlled validation (1,800-incident corpus; McNemar p=1.25e-23). A formally proven multi-tenant model provides isolated logical clock domains. For RoCEv2/ECMP fabrics, P4-based in-band network telemetry corrects path-delay asymmetry, yielding 99.1% attribution accuracy. Application Confidence Policies let workloads specify precision requirements and degraded-mode fallback. Evaluation combines physical five-node measurements (NTP inversion rate 44.978%, Laplace fit preferred over Gaussian by AIC under congestion) and controlled synthetic validation with illustrative configurations from 512 to 16,384 H100 GPUs across InfiniBand and RoCEv2 fabrics.