Sparse Data Structure Compression with Mode Search Approximation
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Solution Overview
Problem
Conventional delta compression methods fail to provide satisfactory results for sparse data structures like sparse matrices used in machine learning due to large differences between neighboring numerical values, defeating traditional compression schemes.
Innovation Solution
A data compression method utilizing mode search approximation, where the most repeated value (mode) is extracted and encoded with a significance map, complementing delta compression, and employing a ternary tree processing algorithm for rapid mode approximation with parallel machine processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional delta compression is applied to sparse data structures with large differences between neighboring values, then compression is attempted, but compression efficiency deteriorates significantly
Solution Approach 1:
The patent changes the compression parameter from delta encoding to mode-based encoding. Instead of storing differences between consecutive elements, the system identifies the mode (most frequent value) in data blocks and encodes deviations from this mode, which is particularly effective for sparse data structures where many elements share the same value.
Solution Approach 2:
The patent divides the data into blocks and processes each block independently to find the local mode. This segmentation allows the compression algorithm to adapt to local data characteristics and handle sparse structures more effectively by identifying repeated values within each block.
2Productivity
If mode search approximation is implemented to extract and encode the most repeated value, then compression efficiency for sparse data structures improves, but computational complexity increases
Solution Approach 1:
The patent performs mode search only on a subset of data blocks rather than the entire dataset. By selectively applying mode-based compression to blocks that benefit most from this approach, the system achieves good compression ratios while limiting computational overhead.
Solution Approach 2:
The patent pre-processes data to identify candidate mode values before performing the full compression encoding. This preliminary identification step simplifies the subsequent encoding process and reduces the overall computational complexity by avoiding repeated full scans of the data.
Data Source
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AI summary
Embodiments are generally directed to compression for compression for sparse data structures utilizing mode search approximation. An embodiment of an apparatus includes one or more processors including a graphics processor to process data; and a memory for storage of data, including compressed data. The one or more processors are to provide for compression of a data structure, including identification of a mode in the data structure, the data structure including a plurality of values and the mode being a most repeated value in a data structure, wherein identification of the mode includes application of a mode approximation operation, and encoding of an output vector to include the identified mode, a significance map to indicate locations at which the mode is present in the data structure, and remaining uncompressed data from the data structure.