Byte-Select Cache Compression Under Metadata Constraints
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Solution Overview
Problem
Cache compression algorithms are typically designed in an ad hoc manner, leading to inefficiencies in metadata usage, particularly in algorithms like the 'ideal' byte select algorithm, which requires excessive metadata due to high encoding possibilities, making it impractical for compressing data with limited dictionary sizes.
Innovation Solution
The development of a method to generate custom byte select algorithms through repeated transforms, including edge removal and edge coalescence transforms, to reduce metadata requirements, ensuring the 'cost' of compression is below a threshold and metadata constraints are met, allowing for efficient compression and decompression of data in cache memories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the ideal byte select algorithm is used to maximize compression possibilities, then the compression ratio is improved, but the metadata size increases excessively making it impractical
Solution Approach 1:
The patent extracts only the necessary compression possibilities from the ideal algorithm by removing edges that would require excessive metadata. This selective extraction maintains adequate compression capability while reducing metadata size to practical levels.
Solution Approach 2:
The patent changes the parameter constraints by imposing metadata size limits and adjusting the allowed encoding possibilities accordingly. This transforms the ideal algorithm into a practical one by modifying its parameters to balance compression ratio and metadata requirements.
2Quantity of substance
If the dictionary size is limited to reduce memory usage, then the metadata requirements decrease, but the compression capability is reduced
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the system to adjust between different compression strategies based on the dictionary size available. The byte select algorithm can adapt its encoding possibilities to work effectively within the constraints of limited dictionary sizes.
Solution Approach 2:
The patent modifies the algorithm parameters to accommodate limited dictionary sizes by adjusting the metadata constraints and encoding possibilities accordingly, maintaining an optimal balance between memory usage and compression capability.
3Productivity
If more encoding possibilities are allowed in the metadata, then the compression efficiency is improved, but the complexity of the algorithm increases
Solution Approach 1:
The patent extracts and retains only the most beneficial encoding possibilities while removing less useful ones. This selective approach maintains compression efficiency by keeping the essential encoding capabilities while eliminating complexity from unnecessary possibilities.
Solution Approach 2:
The patent uses simpler, more practical encoding possibilities that are easier to implement and manage, sacrificing some of the more complex encoding options from the ideal algorithm in exchange for reduced algorithmic complexity and improved practicality.
Data Source
AI summary
Techniques are disclosed for designing cache compression algorithms that control how data in caches are compressed. The techniques generate a custom “byte select algorithm” by applying repeated transforms applied to an initial compression algorithm until a set of suitability criteria is met. The suitability criteria include that the “cost” is below a threshold and that a metadata constraint is met. The “cost” is the number of blocks that can be compressed by an algorithm as compared with the “ideal” algorithm. The metadata constraint is the number of bits required for metadata.