ai ml for hpc optimization

**AI/ML for HPC Optimization** represents an **emerging paradigm leveraging machine learning to automate parameter tuning, performance modeling, and resource scheduling, addressing the exponential complexity of modern HPC systems tuning.** **ML-Based Autotuning (OpenTuner, Bayesian Optimization)** - **Autotuning Problem**: Optimize kernel parameters (block size, loop unroll factor, cache tiling dimensions) for performance. Exponential search space (10^6+ combinations). - **OpenTuner Framework**: Bandit-based algorithm sampling parameter space intelligently. Focuses search on promising regions, eliminates poor performers early. - **Bayesian Optimization**: Probabilistic model of objective function (kernel performance vs parameters). Samples most promising points, refines model iteratively. - **Performance Gain**: Autotuning typically achieves 80-95% of hand-optimized performance with zero manual tuning. Speedup: 2-10x over baseline default parameters. **Neural Network Performance Models** - **Prediction Task**: Input = kernel code, parameters, hardware. Output = predicted execution time (GFLOP/s, memory bandwidth). - **Training Data**: Run kernel on hardware with various parameter combinations. Collect statistics (memory bandwidth, cache hits, branch mispredictions). - **Model Architecture**: Multi-layer neural network (5-10 layers, 100-1000 neurons). ReLU activations, batch normalization. Trained via supervised learning (MSE loss). - **Accuracy**: Typical error: 10-30% (acceptable for ranking kernels, less suitable for absolute performance). Accuracy sufficient for optimization decisions. **Roofline Prediction via ML** - **Roofline Model Integration**: ML model predicts arithmetic intensity (FLOP/byte) and achieved occupancy. Roofline model maps to performance ceiling. - **Hybrid Approach**: ML predicts occupancy + arithmetic intensity; roofline formula yields performance. More accurate than direct performance regression. - **Symbolic Execution**: Code analysis (loop depth, memory access patterns) extracts symbolic features. ML model trained on (features, performance) pairs. - **Transfer Learning**: Model trained on one GPU, transfers to similar GPU with fine-tuning. Reduces training data requirement. **Reinforcement Learning for HPC Job Scheduling** - **Scheduling Problem**: Assign jobs to nodes, optimize for throughput, latency, fairness. Combinatorial search space (exponential in job count). - **RL Formulation**: State = job queue, node status. Action = assign job to node (or defer). Reward = throughput increase (negative penalty for idle nodes). - **Agent Training**: Deep Q-learning (DQN) or policy gradient (PPO) trained via simulation. Agent learns optimal scheduling policy. - **Benchmark Results**: RL-based scheduler (e.g., Deepmind Borg model) outperforms heuristic schedulers (first-fit, best-fit) by 10-20% throughput improvement. **AI-Guided Compiler Optimization** - **Compiler Problem**: Select best optimization order (loop unroll → vectorization → inlining) for input program. Order impacts final performance (10-30% variation). - **ML Integration in LLVM**: ML model predicts which optimization sequence yields best performance for given function. Replaces hand-written heuristics. - **Feature Engineering**: Extract program features (instruction count, loop depth, call-graph properties). Train model on (features, optimization sequence, performance) triplets. - **Production Deployment**: Compiler leverages model during optimization phase. Transparently improves optimization quality without user awareness. **Learned Prefetching and Memory Optimization** - **Prefetch Policy Prediction**: ML model learns data access pattern from instruction history. Predicts next memory address, pre-fetches from DRAM. - **Address Pattern Recognition**: Recurrent neural networks (LSTM) model access sequences. Train on execution traces (millions of memory accesses). - **Performance Improvement**: 10-20% speedup on memory-bound kernels (FFT, GEMM variants). Trade-off: prefetcher power overhead. - **Hardware Implementation**: Prefetcher implemented in CPU microarchitecture (no ISA changes). Transparent to software. **AI for Power Management in HPC Centers** - **Power Prediction**: ML model predicts power consumption (watts) per job, given parameters (clock frequency, core count, vectorization level). - **Dynamic Frequency Scaling (DVFS)**: Adjust clock frequency per node based on power budget. ML model optimizes frequency for power constraint while maintaining performance. - **Thermal Management**: Predict temperature rise; throttle hot nodes, boost cool nodes. Uniform temperature distribution achieved via ML-guided DVFS. - **Data Center Savings**: Power oversubscription enables 20-40% cost reduction (fewer power supplies, cooler requirements). ML-guided power management maintains reliability. **Current Limitations and Future Directions** - **Generalization Challenge**: ML models trained on specific hardware (GPU architecture, interconnect topology). Transfer to different hardware requires retraining. - **Interpretability**: "Black box" ML models don't explain optimization decisions. Hard to debug if model performance degrades. - **Data Requirements**: Large training datasets necessary (100k+ kernel runs). Expensive to collect; limits applicability to niche domains. - **Emerging Trends**: AutoML techniques (neural architecture search) automatically design model architectures. Federated learning enables knowledge sharing across systems without data centralization.