Is < faster than <=?

In nearly all modern compilers and CPUs, there is no significant performance difference between < and <=. Any difference, if it exists, is almost always negligible compared to other factors like pipeline stalls, cache misses, or higher-level design inefficiencies.

Why There’s (Almost) No Difference

1. CPU Instruction Set

   • At the assembly level, comparison instructions (e.g., CMP on x86) set flags in a register (such as the Zero Flag, Sign Flag, Carry Flag). The subsequent branch or conditional operation then checks these flags.
   • The difference between < (“less than”) and <= (“less than or equal to”) is often just a different branch instruction (like JL vs. JLE on x86). Both instructions usually take the same amount of time to execute.

2. Compiler Optimizations

   • Modern compilers optimize code extensively. For a simple comparison, whether you write < or <=, the compiler typically generates equally efficient code.
   • If any difference arises (rarely), it might be an artifact of how the compiler arranges instructions for pipeline scheduling, rather than the comparison itself.

3. Context Matters More Than the Operator

   • Far more impactful to performance are factors like loop unrolling, branch prediction, memory access patterns, and overall algorithmic complexity.
   • In other words, < vs. <= is almost never the bottleneck.

When Could It Possibly Matter?

• Highly Specialized Code: In extremely tight loops optimized at the microarchitecture level—like certain DSP (Digital Signal Processing) or embedded code—some compilers or custom assembly routines might reveal a minuscule difference.
• Branch Prediction: The main performance factor with conditionals is branch prediction. However, the difference between < and <= in branch prediction success is usually zero or trivial in typical programs.

Real-World Takeaway

• Use < vs. <= based on correctness and clarity of logic, not on assumed performance differences.
• If performance is critical, focus on higher-leverage optimizations (e.g., reducing complexity from O(n^2) to O(n log n), improving data locality, eliminating unnecessary branches).

Level Up Your Coding Skills

If you want to dive deeper into performance considerations and systematically improve your problem-solving techniques, check out these courses from DesignGurus.io:

1. Grokking Data Structures & Algorithms for Coding Interviews

   • Build a robust foundation in analyzing and optimizing code, focusing on algorithmic complexity, data access patterns, and more.

2. Grokking the Coding Interview: Patterns for Coding Questions

   • Master the patterns behind common interview questions, so you can quickly write efficient, optimized solutions—regardless of whether you’re using < or <= in your loops.

In short, don’t worry about < vs. <= from a performance standpoint. Concentrate on writing clear, correct code—and if performance is critical, tackle more influential optimizations first.