llvm-optimization

To implement custom LLVM optimization passes, design and evaluate transformations using the New Pass Manager API and LLVM C++ APIs, targeting specific hot paths to boost runtime performance. You can register passes and run them over test modules to observe results.

Tuning LLVM inlining and vectorization passes requires analyzing existing transformations and applying a custom pass pipeline to critical hot paths. Validate the targeted speedups by running verification tooling and test suites over the modified modules.

Yes, designing compiler backends with this approach requires familiarity with the New Pass Manager API and LLVM C++ APIs. You also need knowledge of verification tooling to evaluate analysis-driven transformations and ensure correctness.

Create a simple function, register a pass using the New Pass Manager API, and run it over a test module to observe results. This minimal example workflow verifies that your optimization pipeline executes correctly and applies the intended transformations.

A custom LLVM loop optimization pass may fail to improve runtime performance if the targeted hot path is not correctly identified or if the analysis-driven transformations lack proper verification. Evaluate your pass pipeline using test suites to validate improvements.

Yes, LLVM analysis-driven transformations apply directly to research workflows involving loop optimizations, inlining, and vectorization. You can implement and evaluate custom passes to experiment with compiler backend performance enhancements.