Are Integer Calculations Still Faster Than Floating-Point Calculations on Modern Hardware?

Floating Point vs Integer Calculations on Modern Hardware

Are Integer Calculations Truly Faster?

A long-held belief among programmers is that integer calculations are inherently faster than floating-point calculations. However, this assumption may no longer hold for modern multi-core processors.

Evaluating the Performance Gap

To assess the relative speed of integer and floating-point calculations on various CPU architectures, let's delve into some data:

64-bit Intel Xeon X5550 @ 2.67GHz, gcc 4.1.2 -O3:

short add/sub: 1.005460 [0]
short mul/div: 3.926543 [0]
long add/sub: 0.000000 [0]
long mul/div: 7.378581 [0]
long long add/sub: 0.000000 [0]
long long mul/div: 7.378593 [0]
float add/sub: 0.993583 [0]
float mul/div: 1.821565 [0]
double add/sub: 0.993884 [0]
double mul/div: 1.988664 [0]

32-bit Dual Core AMD Opteron(tm) Processor 265 @ 1.81GHz, gcc 3.4.6 -O3:

short add/sub: 0.553863 [0]
short mul/div: 12.509163 [0]
long add/sub: 0.556912 [0]
long mul/div: 12.748019 [0]
long long add/sub: 5.298999 [0]
long long mul/div: 20.461186 [0]
float add/sub: 2.688253 [0]
float mul/div: 4.683886 [0]
double add/sub: 2.700834 [0]
double mul/div: 4.646755 [0]

These results show that, on these architectures, the performance gap between integer and floating-point operations is relatively small. In some cases, floating-point calculations can even be faster than integer operations, particularly for longer data types like 'long long'.

Performance Considerations

Several factors influence the performance of floating-point and integer calculations:

• CPU Architecture: Different CPUs have varying floating-point units (FPUs) and integer arithmetic logic units (ALUs). Some CPUs have dedicated FPUs, while others combine FPUs and ALUs.
• Superscalar Execution: Modern CPUs use superscalar architecture, which allows them to execute multiple instructions in parallel. This can significantly improve the performance of floating-point calculations, which can handle more independent operations than integer operations.
• Floating-Point Precision: Floating-point calculations use different levels of precision (e.g., single-precision or double-precision), which can affect performance. Double-precision calculations are generally slower but can handle a wider range of values.

Testing Performance

To accurately test the performance of floating-point and integer calculations on a specific target hardware, use the following steps:

1. Write simple test programs that perform the desired calculations.
2. Compile the programs with the same optimization settings.
3. Run the programs multiple times to reduce the impact of variances in system performance.
4. Compare the execution times to determine the relative speed of the operations.

Conclusion

While integer calculations were once significantly faster than floating-point calculations, that gap has closed significantly on modern hardware. Superscalar architecture, dedicated FPUs, and efficient floating-point libraries have made floating-point operations comparable in speed to integer operations. Therefore, it's essential to evaluate the specific hardware and workload before assuming that integer calculations are inherently faster.

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