C++ Performance Checklist for Low-Latency Systems

This checklist serves as a practical reference you can apply whenever you’re writing or reviewing performance-critical C++ code — particularly in low-latency systems like financial trading engines, real-time processing pipelines, or embedded systems.

✅ General Mindset

• Think about branch prediction early in design, not just during profiling.
• Assume every conditional in a hot path is a potential performance risk.
• Prefer predictability over flexibility in critical code paths — remove unnecessary indirection or generic dispatch.

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✅ Code Structuring for Prediction

• Write branches that follow stable patterns (e.g., hot paths consistently taken, cold paths consistently skipped).
• Split hot and cold paths into separate functions to prevent cold-path history from polluting hot-path prediction.
• Flatten nested branches into simpler structures, such as precomputed lookup tables or state machines.
• Prefer direct branches over indirect branches (virtual calls, function pointers) in tight loops.

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✅ Data Handling

• Pre-sort or batch-process data to make branch outcomes more predictable.
• Avoid mixing random data-dependent branches directly into performance-critical loops.
• Avoid branches that depend directly on timestamps, random numbers, or external entropy sources.

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✅ Compiler Hints and Optimizations

• Use [[likely]] and [[unlikely]] (C++20) or compiler-specific macros to help the compiler optimize branch layout.
• Enable Profile-Guided Optimization (PGO) to let the compiler optimize for real-world branch behavior.
• Pass the right optimization flags, e.g., -O3 -march=native to enable aggressive inlining and tuning.

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✅ Branch Removal (When Possible)

• Replace simple branches with conditional moves (CMOV) where supported.
• Use branchless arithmetic tricks (e.g., masks, bit shifts) to remove predictable conditionals.
• Consolidate logic into precomputed tables where applicable.

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✅ Measurement and Analysis

• Profile branch behavior with tools like:
  • perf (Linux)
  • VTune (Intel)
  • Callgrind (Valgrind)
• Track branch-miss rate alongside latency metrics in production — automate alerts if misprediction rate crosses acceptable thresholds.
• Use Celero or similar libraries for targeted microbenchmarking of specific branches or logic blocks.
• Compare branch prediction rates across different compilers, compiler flags, and hardware platforms.

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✅ Continuous Monitoring

• Add periodic branch miss rate profiling to CI or performance monitoring pipelines.
• Compare branch miss spikes against known latency outliers — does high misprediction correlate with P99+ spikes?
• Capture and analyze branch behavior under realistic load patterns (not just synthetic tests).

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✅ Review Checklist for Each Hot Path

When reviewing critical code, ask:

• How many branches are in this path?
• Are any branches data-dependent on external input?
• Are there virtual calls or function pointers inside tight loops?
• Are hot and cold paths clearly separated?
• Do branches follow a predictable pattern under normal operation?
• Have you measured branch miss rates before and after optimization?

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Example Workflow

When applying this checklist to a real low-latency C++ system, your process might look like this:

1. Identify hot functions using perf, VTune, or similar.
2. Inspect each branch in those functions — is it predictable?
3. Apply fixes: flatten logic, use branchless techniques, add [[likely]], or restructure loops.
4. Re-run profiling and compare branch miss rate.
5. Track impact on latency tail percentiles (P99, P999) to confirm real-world gains.
6. Commit changes with a note documenting before/after branch miss rates.

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Low-latency optimization isn’t about removing every branch — it’s about writing branches that work with, not against, the CPU’s branch predictor. This checklist, when combined with thoughtful design and rigorous profiling, helps ensure your code consistently achieves both correctness and speed.