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Why does the time to generate test data increase significantly after sorting the original data?
Why does the time to generate test data increase significantly after sorting the original data?
Apr 01, 2025 pm 06:51 PM
Analysis of the impact of data sorting on the performance of test data generation
When generating test data, sorting the original data results in a significant increase in generation time, which is not a simple algorithmic complexity problem ( O(n) ), but is closely related to memory access mode and CPU caching mechanism.
In the code in the article, the key part lies in the set derivation formula {j for j in test_strings if j.startswith(test_data_str)} . Although its time complexity is theoretically O(n), the actual execution efficiency is greatly affected by memory access.
The root of the problem: cache miss
Unsorted test_strings are stored in memory roughly consecutively. When looping through, the CPU can effectively utilize the cache mechanism. Because the data is continuous, subsequent elements are likely already in cache, thus reducing the number of memory accesses and significantly improving speed.
However, after sorting test_strings , its memory addresses are no longer continuous. During traversal, the CPU frequently experiences cache misses, and it is necessary to continuously read data from the main memory, resulting in a sharp drop in access speed, which extends the time for testing data generation.
Experimental verification and supplementary instructions
The experimental results in this article have proved this well: whether using sorted , random.shuffle or random.sample to disrupt the order, it will lead to performance degradation. This is all attributed to changes in memory access patterns, rather than differences in efficiency of the sorting algorithm itself.
The verification method of test_strings = list(reversed(test_strings)) proposed in the article is also effective. Reversing the list will also destroy the continuity of memory addresses, resulting in cache misses.
Further analysis: Pagination scheduling
In addition to cache misses, large-scale data may also involve pagination scheduling. If test_strings occupies multiple memory pages, after sorting, the access order becomes messy, which may frequently trigger page exchange, further aggravate the performance bottleneck.
Optimization suggestions
If you need to sort the data, it is recommended to complete the sorting before generating the test data, rather than inside the loop. This ensures that test_strings maintains continuity in memory, thereby maximizing the use of CPU cache and improving efficiency. Alternatively, consider using data structures and algorithms that are more suitable for memory access patterns. For example, if test_strings requires frequent searches of strings starting with a specific prefix, consider using data structures such as dictionaries or Trie trees to optimize search efficiency.
In short, this problem is not an algorithmic complexity issue, but a result of the combined action of memory access mode and CPU caching mechanism. Understanding this mechanism is essential for writing efficient code.
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