In-Place Sample Sorting With Bucket Redistribution and Small-Cache Fit
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Solution Overview
Problem
Existing in-place sorting techniques face challenges with large datasets, such as the x280 architecture, due to the limitations of memory bandwidth and cache hierarchy, and existing out-of-place sorting techniques are inefficient and require significant memory bandwidth, such as the S 3< -sort, which are inefficient and require significant memory bandwidth.
Innovation Solution
A method implemented using one or more processors and memory storing methods and processes for implementing one or more processors and memory storing instructions that, in response to execution by the one or more processors, cause the one or more processors to: compare elements of a set of unsorted elements stored in a first memory segment to a plurality of splitter values; based on the comparing, determine respective counts of elements of the set that will fit into each of a plurality of buckets bounded by the splitter values; move, to a second memory segment that is smaller than the first memory segment, a predetermined number of elements from each of a plurality of ranges of the first memory segment that correspond to the plurality of buckets; redistribute remaining elements from at least some of the plurality of ranges of the first memory segment across the plurality of ranges based on comparing the remaining elements to the splitter values; and distribute the elements from the second memory segment across the plurality of ranges based on comparing the elements to the splitter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If in-place sorting techniques are used, then memory usage is reduced, but memory bandwidth burden increases significantly
Solution Approach 1:
The patent divides the sorting process into distinct phases (sampling, counting, distribution) and segments the memory operations. Elements are moved in batches between memory segments rather than requiring continuous full-bandwidth access, reducing peak memory bandwidth burden while maintaining in-place sorting benefits.
Solution Approach 2:
The patent performs preliminary sampling and counting operations to determine element distribution before actual sorting. This preliminary action allows the system to pre-calculate bucket assignments and reduce subsequent memory access requirements, as elements can be moved directly to their final positions without repeated comparisons.
2Productivity
If out-of-place sorting techniques like S 3 are used, then memory bandwidth burden is reduced, but memory storage requirements increase significantly
Solution Approach 1:
The patent discards the oracle data structure used in traditional out-of-place sorting techniques. Instead of storing bucket assignments in separate memory locations, the system recovers space by performing distribution operations directly in the original memory segment, eliminating the need for O(M) extra storage while maintaining efficient memory bandwidth usage.
Solution Approach 2:
The patent extracts and removes the oracle component from the sorting algorithm. By eliminating the separate bucket assignment storage requirement, the system achieves out-of-place sorting efficiency without the associated memory storage overhead, using only O(1) temporary storage instead of O(M).
3Productivity
If block buffer size is reduced for small L2 cache architectures, then cache efficiency improves, but sorting complexity increases
Solution Approach 1:
The patent implements dynamic block processing where the block buffer size and processing granularity are adapted to match the specific L2 cache capacity of the target architecture. The sampling phase dynamically determines optimal block sizes, and the distribution phase processes elements in cache-friendly batches, allowing the algorithm to maintain high cache efficiency across different hardware configurations without excessive complexity.
Data Source
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AI summary
Implementations described herein relate to improved in-place sample sorting of data. In various implementations, unsorted elements of a set stored in a first memory segment may be compared to splitter values to determine respective counts of elements of the set that will fit into each of a plurality of buckets bounded by the splitter values. Some number of elements from each of multiple ranges of the first memory segment that correspond to the multiple buckets may be moved to a smaller second memory segment. Remaining elements may be redistributed from at least some of the plurality of ranges of the first memory segment across the plurality of ranges based on comparing the remaining elements to the splitter values. The elements from the second memory segment may then be distributed across the plurality of ranges based on comparing the elements to the splitter values.