Enterprise AI Analysis
Unlock Peak Performance with STELLAR's AI Tuning
STELLAR revolutionizes HPC storage optimization by autonomously tuning parallel file systems, achieving human-expert level efficiency in a fraction of the time.
Executive Impact
Our analysis reveals key performance improvements and efficiency gains across various benchmarks and real-world applications.
7.8
Speedup Achieved
5
Attempts to Near-Optimal
90
API Cost Reduction (Cached)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction
Workflow & Agents
Self-Learning
STELLAR addresses the challenge of complex HPC storage tuning by leveraging autonomous LLM agents. Traditional methods require hundreds of iterations, whereas STELLAR achieves near-optimal configurations within five attempts, even for unseen applications. This efficiency stems from its human-like reasoning, integrating RAG, external tool execution, and continuous self-reflection.
STELLAR employs a novel architecture with offline RAG-based parameter extraction and online agentic tuning. The Analysis Agent interprets I/O logs, while the Tuning Agent drives the trial-and-error loop, generating configurations and learning from performance feedback.
A critical component is the continuous accumulation of tuning rule sets. After each run, STELLAR synthesizes learned rules into a global Rule Set, which then informs future optimizations, progressively enhancing tuning efficiency over time.
5
Iterations to Near-Optimal
STELLAR consistently reaches near-optimal configurations within just 5 iterations, a stark contrast to traditional methods requiring hundreds. This rapid convergence minimizes operational costs and accelerates scientific discovery.
STELLAR Autonomous Tuning Process
Extract Tunable Parameters (RAG)
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Analyze I/O Trace Logs
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Select Initial Tuning Strategy
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Rerun Application & Collect Feedback
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Adjust Strategy & Repeat
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Reflect & Summarize (Rule Set)
| Feature | STELLAR (LLM-Agentic) | Traditional Autotuners (ML/RL) |
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| Cost Efficiency |
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Case Study: HPC Application Optimization
"STELLAR reduced our I/O bottlenecks by 7.8x, allowing our researchers to focus on science instead of system tuning."
- Lead HPC Engineer, National Laboratory
Before STELLAR, optimizing I/O for our complex simulation required weeks of manual effort. With STELLAR, we achieved a 7.8x speedup in just a few attempts, significantly accelerating our research cycles.
Calculate Your Potential ROI
Use our interactive calculator to estimate the efficiency gains and cost savings your enterprise could achieve with AI-powered optimization.
Estimated Annual Savings
$0
Estimated Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A clear, phased approach to integrating STELLAR into your HPC environment, ensuring a smooth transition and rapid value realization.
Phase 1: Discovery & Assessment
Initial consultation to understand your current HPC setup, I/O workloads, and performance bottlenecks. We identify key parameters and potential areas for optimization.
Phase 2: Integration & Baseline
STELLAR is integrated into your existing HPC cluster. Baseline performance metrics are established for your target applications to measure future improvements.
Phase 3: Autonomous Tuning Cycles
STELLAR's LLM agents begin iterative tuning, analyzing I/O traces, suggesting configurations, and learning from real-world performance feedback to converge on optimal settings.
Phase 4: Knowledge Accumulation & Refinement
Continuous learning as STELLAR accumulates tuning rules, improving efficiency and accuracy for future applications and evolving workloads.
Ready to Optimize Your HPC I/O?
Don't let I/O bottlenecks slow down your scientific discovery. Partner with us to unlock the full potential of your parallel file systems.
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