Title. Algorithm Performance Optimization Analysis

 Title. Algorithm Performance Optimization Analysis

 

1. Performance Analysis Data Sheet

 

2. Data Analysis and Insights

• Linear Maintenance of Performance Gap: For existing algorithms, the performance degradation curve rises steeply whenever the data scale increases tenfold. In contrast, Sky Butterfly stably maintains a performance improvement multiple between 4.9 and 6.8 times. This suggests that the algorithm's computational complexity is being controlled much more efficiently than the existing sieve method.

 

• Scope of Absolute Time Reduction: The most notable aspect is the monetary value of the time saved. While the scale increased 1,000-fold from 100,000 to 100,000,000, the time saved increased by approximately 2,300-fold. This implies that as the data scale grows, the 'Resource Headroom' provided by Haneulnabi to the system increases exponentially.

 

• Hardware-Friendly Design: While existing algorithms tend to experience a sharp drop in efficiency due to memory access failures (Cache Misses) in operations beyond 100,000,000, Haneulnabi's filtering technique maintains system stability even in high-load computation environments through a CPU cache-friendly design.

 

3. Conclusion.

This data demonstrates that the Sky Butterfly algorithm is a filtering model that optimizes computational density by moving away from conventional indiscriminate sieve marking. This level of time efficiency in massive-scale AI computations presents a 'tipping point' capable of dramatically reducing annual data center operating expenses (OPEX) and carbon emissions.