opentuner autotuning framework

**Performance Autotuning Frameworks** are the **systematic approaches that automatically search the space of program configuration parameters — tile sizes, unroll factors, thread block dimensions, memory layout choices — to find the combination that maximizes performance on a specific hardware target, eliminating the expert manual tuning effort that once required weeks of trial-and-error experimentation for each new architecture**. **The Autotuning Problem** A single GPU kernel may have 5-10 tunable parameters, each with 4-8 choices — the combinatorial search space reaches millions of configurations. Exhaustive search is infeasible (each evaluation takes seconds to minutes). Autotuning frameworks intelligently explore this space to find near-optimal configurations in hours. **Search Strategies** - **Random Search**: sample random configurations, surprisingly competitive baseline, embarrassingly parallel across machines. - **Bayesian Optimization**: build a surrogate model (Gaussian process or random forest) of performance vs parameters, use acquisition function (EI, UCB) to select next promising point. GPTune, ytopt, OpenTuner's Bayesian backend. - **Evolutionary / Genetic Algorithms**: population of configurations, crossover and mutation, selection by performance. Good for discrete search spaces. - **OpenTuner**: ensemble of search techniques (AUC Bandit Meta-Technique selects best-performing search algorithm dynamically). **Framework Examples** - **OpenTuner** (MIT): general-purpose, Python API, pluggable search techniques, used for GCC flags, CUDA kernels, FPGA synthesis. - **CLTune**: OpenCL kernel tuning (grid search + simulated annealing), JSON-based parameter spec. - **KTT (Kernel Tuning Toolkit)**: C++ API, CUDA/OpenCL/HIP, supports output validation and time measurement. - **ATLAS (Automatic Linear Algebra Software)**: architecture-specific BLAS tuning, influenced vendor library defaults. - **cuBLAS/oneDNN Heuristics**: vendor libraries include pre-tuned lookup tables (algorithm selection based on problem dimensions). **ML-Based Performance Models** - **Analytical roofline models**: predict performance from arithmetic intensity + hardware peak — fast but coarse. - **ML surrogate**: train regression model (XGBoost, neural net) on sampled configurations, use as cheap proxy for expensive hardware measurements. - **Transfer learning**: adapt a performance model from one GPU to another (related architectures share structure). **Autotuning in HPC Applications** - **FFTW**: planning phase measures multiple FFT algorithms at runtime, stores plan for repeated execution. - **MAGMA**: autotuned BLAS for GPU (tuning tile sizes per GPU model). - **Tensor expressions** (TVM, Halide): search over schedule space (loop ordering, tiling, vectorization) to find optimal execution plan. **Practical Workflow** 1. Define parameter space (types, ranges, constraints). 2. Define measurement function (compile + run + return time). 3. Run autotuner (hours on target hardware). 4. Save optimal configuration for deployment. 5. Re-tune when hardware or workload changes. Performance Autotuning is **the machine intelligence applied to the meta-problem of optimizing software — automatically discovering hardware-specific configurations that squeeze maximum performance from parallel hardware without requiring architectural expertise from every application developer**.