Dynamic Radix Sorting Algorithm for Cache Efficiency
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Solution Overview
Problem
Existing radix, bucket, and interval sorting algorithms face limitations in dynamically adjusting the radix of digits or symbols during the sorting process, leading to inefficient use of auxiliary tables and poor adaptation to non-uniform distributions of key values, which affects cache memory load and sorting performance.
Innovation Solution
The proposed solution dynamically reduces the radix of digits or symbols by reconstructing counter or index tables using data from existing tables without re-accessing the original records, allowing for optimal digit or symbol capacity determination through vector or matrix characteristics analysis, and enabling reuse of tables in subsequent steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the radix of digits or symbols is fixed at a high value (e.g., 256 elements for 8-bit digits) to ensure comprehensive coverage of all possible key values, then the sorting algorithm can handle any distribution of key values, but the auxiliary counter tables or index tables become excessively large, increasing cache memory load and reducing sorting performance
Solution Approach 1:
The patent applies dynamics by making the radix value changeable during the sorting process. Instead of using a fixed radix throughout, the algorithm dynamically adjusts the radix based on the actual distribution of key values encountered. This allows the auxiliary tables to be resized from an initial large capacity (e.g., 256 elements) down to a smaller optimal size as the sorting progresses and the actual key value range becomes apparent, thereby reducing cache memory load while maintaining adaptability to handle any key value distribution.
2Productivity
If the radix is dynamically adjusted during sorting to optimize table size, then cache memory load is reduced and sorting performance improves, but the algorithm becomes more complex and requires additional logic to determine optimal radix values
Solution Approach 1:
The patent implements self-service by enabling the sorting algorithm to automatically determine the optimal radix value during execution without requiring external intervention or complex pre-computation. The algorithm monitors the actual distribution of key values as they are processed and autonomously adjusts the radix to match the observed data characteristics. This self-adjusting mechanism simplifies the overall system design while maintaining high sorting performance, as the algorithm adapts itself based on real-time feedback from the data being sorted.
3Productivity
If auxiliary tables are reconstructed with reduced radix during sorting, then the effective table size decreases and cache memory efficiency improves, but additional time is required for table reconstruction and transformation operations
Solution Approach 1:
The patent applies preliminary action by performing table reconstruction operations at strategically chosen points during the sorting process, rather than waiting until the optimal radix is fully determined. The algorithm proactively reduces the radix and reconstructs auxiliary tables as soon as sufficient information about the key value distribution becomes available, even if the final optimal radix hasn't been completely established. This preliminary reconstruction reduces cache memory load earlier in the process, improving overall efficiency despite the additional reconstruction time required.
Data Source
AI summary
The device, software, and method of information processing are a further development of the ideas underlying radix, bucket, and interval sorting, and corresponding digital search. In particular, the device executes new or improved methods for sorting based on representing a key as a number or a string, new methods for constructing or transforming tables of counters or index tables in sorting and corresponding searching processes, new methods for determining the optimal bit depth for sorting, new methods and improvements based on normalization and reinterpretation, new methods for estimating the amount of work and for choosing an optimal strategy in sorting or corresponding searching, and other advanced methods.