profileguided

ProfileGuided, commonly referred to as profile-guided optimization (PGO), is a compiler optimization technique that uses runtime profiling data to guide optimization decisions. By profiling a representative workload, the compiler can tailor generated code to real execution patterns, improving performance on hot paths and reducing unnecessary work on less frequently executed code.

Profiling data is collected either through instrumentation, which records exact counts of how often functions and branches execute, or through sampling, which collects instruction pointers at intervals. The resulting profile data is then consumed by the compiler during a subsequent optimization phase, often in conjunction with link-time optimization. The outcome is a binary that is optimized for the observed workload.

Typical optimizations guided by profile data include inlining decisions, function layout and code density to improve instruction cache locality, hot-path splitting, branch prediction hints, and more effective register allocation. PGO can also influence devirtualization, dead-code elimination, and loop transformations when beneficial for the profile.

Limitations include the need for representative profiling data, potential growth in build times and complexity, and the risk that performance gains do not generalize to unseen inputs. If workloads differ substantially from the profiling scenario, optimizations may be neutral or even detrimental. Additionally, obtaining reliable profiles can be challenging for applications with highly variable workloads.

PGO is supported by major compilers such as GCC and LLVM/Clang, and by Microsoft Visual C++. It is commonly used for performance-critical software, including servers and games, but is less beneficial for short-lived programs or workloads with unpredictable behavior.