TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference
Large language model (LLM) inference has evolved from an offline workload into a continuously operated software service, yet root-cause analysis remains difficult because a single request spans the inference engine, Python/C++ backend, host CUDA APIs, GPU kernels, and distributed communication. Existing profilers expose raw timelines, while log-based diagnosis often misses cross-layer execution semantics and request-level structure. We present TELLER, a non-intrusive Trace- and Log-aware LLM inference Root-cause analysis framework. TELLER first collects NVTX/CUPTI traces and service logs without modifying model binaries, then reconstructs per-request call-chain trees and aligns log lines ...
| Authors | Ruilin Xu, Junyi Li, Pengfei Chen et al. |
|---|---|
| Primary category | cs.SE |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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