Adaptive Performance Profiling for Scientific Computation Workflows

Create a meta-optimization framework that dynamically profiles and adjusts computational resource allocation for complex scientific simulation workflows. The system must automatically detect computational bottlenecks, recommend hardware/software optimizations, and generate predictive performance models across heterogeneous computing environments. Include machine learning-based predictive algorithms that can learn from previous computational executions.