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 use of the llvm.expect intrinsic to the ground truth provided by a profiling input.

The MisExpect checks in the LLVM backend follow a simple procedure: if there is a mismatch between the branch weights collected during profiling and those supplied by an llvm.expect intrinsic, then it will emit a diagnostic message to the user.

The most natural place to perform the verification is just prior to when branch weights are assigned to the target instruction in the form of branch weight metadata.

There are 3 key places in the LLVM backend where branch weights are created and assigned based on profiling information or the use of the llvm.expect intrinsic, and our implementation focuses on these places to perform the verification.

We calculate the threshold for emitting MisExpect related diagnostics based on the values the compiler assigns to llvm.expect intrinsics, which can be set through the -likely-branch-weight and -unlikely-branch-weight LLVM options. During verification, if the profile weights mismatch the calculated threshold, then we will emit a remark or warning detailing a potential performance regression. The diagnostic also reports the percentage of the time the annotation was correct during profiling to help developers reason about how to proceed.

The 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.

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

Profile Type



Profiling instrumentation added during compilation by the frontend, i.e. clang


Profiling instrumentation added during by the LLVM backend


Context Sensitive IR based profiles


Profiles collected through sampling with external tools, such as perf on Linux