When developers use llvm.expect intrinsics, i.e., through use of __builtin_expect(...), they are trying to communicate how their code is expected to behave at runtime to the optimizer. These annotations, however, can be incorrect for a variety of reasons: changes to the code base invalidate them silently, the developer mis-annotated them (e.g., using LIKELY instead of UNLIKELY), or perhaps they assumed something incorrectly when they wrote the annotation. Regardless of why, it is useful to detect these situations so that the optimizer can make more useful decisions about the code.

MisExpect diagnostics are intended to help developers identify and address these situations, by comparing the branch weights added by the llvm.expect intrinsic to those collected through profiling. Whenever these values are mismatched, a diagnostic is surfaced to the user. Details on how the checks operate in the LLVM backed can be found in LLVM’s documentation.

By default MisExpect checking is quite strict, because the use of the llvm.expect intrinsic is designed for specialized cases, where the outcome of a condition is severely skewed. As a result, the optimizer can be extremely aggressive, which can result in performance degradation if the outcome is less predictable than the annotation suggests. Even when the annotation is correct 90% of the time, it may be beneficial to either remove the annotation or to use a different intrinsic that can communicate the probability more directly.

Because this may be too strict, MisExpect diagnostics are not enabled by default, and support an additional flag to tolerate some deviation from the exact thresholds. The -fdiagnostic-misexpect-tolerance=N accepts deviations when comparing branch weights within N% of the expected values. So passing -fdiagnostic-misexpect-tolerance=5 will not report diagnostic messages if the branch weight from the profile is within 5% of the weight added by the llvm.expect intrinsic.

MisExpect diagnostics are also available in the form of optimization remarks, which can be serialized and processed through the opt-viewer.py scripts in LLVM.


Enables optimization remarks for misexpect when profiling data conflicts with use of llvm.expect intrinsics.


Enables misexpect warnings when profiling data conflicts with use of llvm.expect intrinsics.


Relaxes misexpect checking to tolerate profiling values within N% of the expected branch weight. e.g., a value of N=5 allows misexpect to check against 0.95 * Threshold

LLVM supports 4 types of profile formats: Frontend, IR, CS-IR, and Sampling. MisExpect Diagnostics are compatible with all Profiling formats.

Profile Type Description
Frontend Profiling instrumentation added during compilation by the frontend, i.e. clang
IR Profiling instrumentation added during by the LLVM backend
CS-IR Context Sensitive IR based profiles
Sampling Profiles collected through sampling with external tools, such as perf on Linux