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AI-Powered Optimization for High-Performance Computing in Scientific Simulations
Journal of Artificial Intelligence and Big Data
| Vol 4, Issue 1
Table 1. AI Techniques for HPC Optimization
| AI Technique | HPC Use Case | Reported Benefit |
| Supervised ML (Regression) | Performance modeling (e.g., job run times) | More accurate runtime predictions ⇒ improved scheduling [2] |
| Deep Reinforcement Learning | Job scheduling & resource allocation | Learned policies adapt to workload changes ⇒ outperforms heuristics [5] |
| Bayesian Optimization | Auto-tuning HPC code parameters | Rapid convergence on near-optimal configs ⇒ fewer trials needed [3] |
| Surrogate Modeling (DNNs) | Accelerating computationally expensive solvers | Up to 40–80× speedups in fluid dynamics simulations [8] |
| Anomaly Detection (ML) | Predictive maintenance / fault tolerance | Early warning on node failures ⇒ proactive fault recovery [6] |
| Data Analytics (Clustering) | In-situ analysis, data reduction | Identifies patterns, reduces I/O overhead [9] |