AGENTIC-SYSTEMSApril 8, 20261 min read12 views

Instruction-Tuned LLMs for Parsing and Mining Unstructured Logs on Leadership HPC Systems

Researchers have developed a domain-adapted LLM framework using an 8B-parameter LLaMA model to parse and mine massive unstructured logs from leadership-class HPC systems with high accuracy and efficiency.

Computer Science > Artificial Intelligence

Title: Instruction-Tuned LLMs for Parsing and Mining Unstructured Logs on Leadership HPC Systems

Leadership-class HPC systems generate massive volumes of heterogeneous, largely unstructured system logs. Because these logs originate from diverse software, hardware, and runtime layers, they exhibit inconsistent formats, making structure extraction and pattern discovery extremely challenging. Therefore, robust log parsing and mining is critical to transform this raw telemetry into actionable insights that reveal operational patterns, diagnose anomalies, and enable reliable, efficient, and scalable system analysis. Recent advances in large language models (LLMs) offer a promising new direction for automated log understanding in leadership-class HPC environments.

To capitalize on this opportunity, we present a domain-adapted, instruction-following, LLM-driven framework that leverages chain-of-thought (CoT) reasoning to parse and structure HPC logs with high fidelity. Our approach combines domain-specific log-template data with instruction-tuned examples to fine-tune an 8B-parameter LLaMA model tailored for HPC log analysis. We develop a hybrid fine-tuning methodology that adapts a general-purpose LLM to domain-specific log data, enabling privacy-preserving, locally deployable, fast, and energy-efficient log-mining approach. We conduct experiments on a diverse set of log datasets from the LogHub repository. The evaluation confirms that our approach achieves parsing accuracy on par with significantly larger models, such as LLaMA 70B and Anthropic's Claude. We further validate the practical utility of our fine-tuned LLM model by parsing over 600 million production logs from the Frontier supercomputer over a four-week window, uncovering critical patterns in temporal dynamics, node-level anomalies, and workload-error log correlations.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference

Researchers have introduced Probabilistic Concept-Aware Steering (PCS), an inference-time intervention framework for LLMs. PCS provides fine-grained, safety-oriented semantic steering while preserving original task competence.

agentic-systems

MUX: Continuous Reasoning via Multiplexed Tokens

Researchers introduced MUX, a novel latent reasoning method that compresses discrete text-based reasoning steps into continuous multiplexed tokens. By enabling lossless superposition, MUX significantly boosts LLM reasoning efficiency and speed across complex problem-solving tasks.

agentic-systems

ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional, Uncertain Numeric Data

Researchers have introduced ProbSPARQL, an upward-compatible SPARQL extension designed to query multi-dimensional and uncertain numeric measurement data within Knowledge Graphs, providing significant query performance gains for complex industrial applications.

agentic-systems

Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)

Current AI/ML research on deepfakes focuses primarily on epistemic harms like fake news and scams, leaving a dangerous gap in addressing AI-generated non-consensual intimate imagery (AIG-NCII) and its dignity harms to victims.

NOW LET US Related – Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR

agentic-systems

Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR

Researchers introduced schema-derived constrained decoding for MLIR, enabling a 1.7B small language model to match or beat 15B-34B LLMs without retraining, while generating code 8x-25x faster.

agentic-systems

Beyond Accuracy and Cost: Latency-Aware LLM Query Routing for Dynamic Workloads

Researchers introduced a novel latency-aware LLM query router that jointly optimizes generation latency, accuracy, and cost. The framework achieves up to a 40% improvement in accuracy-cost utility while maintaining low latency under dynamic workloads.