In the realm of path planning and surface finishing, Step-over algorithms play a critical role. However, as complexity increases, evaluating the computational load becomes essential for optimizing real-time performance. This article explores practical techniques to measure and compare the efficiency of different step-over strategies.
1. Understanding Computational Complexity
Before jumping into benchmarks, we must analyze the algorithmic efficiency using Big O notation. Most step-over calculations depend on surface resolution ($n$) and the tool diameter ($d$).
2. Time-Based Profiling (Execution Time)
The most direct way to compare load is by measuring execution time. In Python, we can use the timeit module to get high-precision results.
import timeit
def constant_step_over():
# Simulate constant step-over calculation
return [i * 0.5 for i in range(1000)]
def adaptive_step_over():
# Simulate adaptive step-over (more complex)
return [i * 0.5 if i % 2 == 0 else i * 0.2 for i in range(1000)]
# Measuring execution time
time_const = timeit.timeit(constant_step_over, number=1000)
time_adapt = timeit.timeit(adaptive_step_over, number=1000)
print(f"Constant Load: {time_const}")
print(f"Adaptive Load: {time_adapt}")
3. Memory Footprint Analysis
Computational load isn't just about speed; it's also about Memory Usage. Using tools like memory_profiler allows developers to see the peak RAM consumption during path generation.
4. Floating Point Operations (FLOPs)
For a hardware-independent comparison, counting Floating Point Operations (FLOPs) is the gold standard. This technique counts the number of mathematical calculations ($+$, $-$, $\times$, $\div$) required per unit of surface area.
