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Write efficient C and C++ by first defining what “efficient” means for your workload, then measuring where time or memory is actually spent. Choose an algorithm and data layout that address the measured bottleneck before tuning individual expressions; a lower-level rewrite or compiler flag is not automatically faster.
Start with a performance goal, not a hunch
Choose a metric that matches the problem. A latency-sensitive program may need to reduce the time for a particular request; a batch job may care more about throughput; an embedded application may be constrained by memory or energy; and a command-line tool may need a smaller binary. These goals can conflict, so record the target and workload before comparing changes.
| Metric | What to measure | Trade-off to watch |
|---|---|---|
| Latency | Time for a representative operation or request | A change that helps average latency may affect worst-case behavior or throughput. |
| Throughput | Work completed over a defined interval | More concurrency or buffering may consume more memory or increase individual-operation latency. |
| Memory | Peak and steady-state use | Compact storage may change access patterns or implementation complexity. |
| Binary size | Size of the built program or relevant component | Size-focused build choices may differ from those best for execution speed. |
| Energy | Energy use over a representative workload | Lower execution time alone does not establish lower energy use. |
The C++ Core Guidelines put the order plainly: “Don’t optimize without reason” (Per.1), “Don’t optimize prematurely” (Per.2), and “Don’t make claims about performance without measurements” (Per.6). Profile the complete program or system to locate significant costs, then use a focused benchmark to investigate a suspected hot path. A microbenchmark can help compare a narrow operation, but it cannot by itself show that the operation dominates real workloads.
Work from the largest measured cost downward
- Define the workload. Use representative input, operating conditions, and a target metric. Record the compiler, build settings, hardware, and relevant runtime conditions so later comparisons are meaningful.
- Profile the whole system. Find where time, memory, or other constrained resources go. Do not assume that the function that looks complicated is the bottleneck.
- Address algorithm and data layout. If a large share of work comes from an inefficient algorithm or costly access pattern, expression-level tuning is unlikely to be the most useful first change.
- Change one meaningful thing at a time. Keep behavior and workload comparable, then measure the result using the same compiler, flags, hardware, and test inputs.
- Check the trade-offs. Compare the relevant measures, including latency or throughput, peak and steady-state memory, allocation count, cache locality, code size, portability, numerical reproducibility, and implementation complexity.
Report the conditions and any measurement variation along with a result. The cited guidance does not establish a universal percentage speedup for a particular optimization, and results from one workload should not be presented as a general guarantee.
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Prefer simple code that preserves useful information
Complicated or low-level code is not inherently faster. The C++ Core Guidelines warn in Per.5: “Don’t assume that low-level code is necessarily faster than high-level code.” Clear, simple code can give an optimizing compiler more opportunity to reason about the program than hand-written machinery that obscures what it does.
Keep useful information visible in interfaces. Types, ranges, and sizes can help express what data an operation accepts and how it may be used. Avoid erasing those properties behind overly generic or untyped interfaces, such as a void*-style API, when a typed interface is suitable. This is not a rule against abstraction: judge an abstraction by clarity, correctness, and measured cost rather than assuming that either abstraction or manual low-level code always wins.
Reduce avoidable work in hot paths
When measurements identify a hot path, look for repeated work and unnecessary movement through memory or code. Compact structures, predictable access, and fewer allocations or deallocations can help when they fit the workload. Redundant aliases and indirections may also add cost or make access less predictable. The relevant question is whether a change reduces a cost that matters in the measured path.
Where suitable, move computation to compile time rather than repeating it at runtime. This is a targeted design choice, not a mandate to make code more complicated: account for its effect on readability, build behavior, and binary size as well as runtime performance.
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Hot-path performance depends on more than the number of instructions in a function. Allocation on a critical path, access patterns, cache behavior, synchronization, and context switches can all matter. Consider these costs while choosing data structures and boundaries between components, then verify their importance with measurements.
For concurrent code, check whether shared mutable state forces synchronization or creates unpredictable access patterns. Revisit the assumptions about data races and synchronization when changing a concurrent path; a speed-oriented change is not useful if it compromises correctness. There is no single data structure or concurrency pattern that is best for every workload, so compare alternatives using the workload and metrics that matter to the application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose release settings for the compiler and correctness needs
Build settings are toolchain-specific. Microsoft’s optimization guidance recommends Profile Guided Optimization (PGO) for final release builds when feasible. Without PGO, its guidance points to whole-program optimization with suitable /O1 or /O2 settings and linker configuration. These are MSVC recommendations, not universal flags for every C or C++ compiler; validate the supported options and effect with the actual toolchain and target.
| Build approach | When to consider it | What to verify |
|---|---|---|
| PGO for a final release build | When the MSVC workflow and representative profiling data make it feasible | That the profile reflects relevant usage and the final build improves the chosen metric. |
Whole-program optimization with suitable /O1 or /O2 settings |
When PGO is not being used and the target is an MSVC release build | Performance, binary size, linker configuration, and behavior on the intended workload. |
Floating-point options need a correctness decision, not just a speed comparison. They can trade execution speed against precision and floating-point exception semantics. Choose a mode only after deciding which numerical behavior the program requires and measuring the result; do not assume a faster option is interchangeable with the original behavior.
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Use standards-level guidance in context
The C++ Core Guidelines are a living guidance document, not a substitute for the ISO C++ language standard. ISO/IEC TR 18015:2006 is a technical report on C++ overheads, performance myths, performance-sensitive techniques, and efficient standard-library implementation. ISO lists it as a 197-page report published in September 2006 and records its confirmation in 2013. It offers conceptual background, but its age makes it important to check any practical advice against the current compiler, standard library, target architecture, and measurements.
The central discipline is to make performance claims specific: identify the workload, the measured cost, the configuration, and the trade-off. That makes it possible to distinguish a real improvement from an appealing but unverified rewrite.
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