Compressed Data Metadata for Parallel Decompression Compatibility
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Solution Overview
Problem
Traditional compression algorithms are inherently serial, making them less ideal for parallel processing, and modifying them for parallel decompression often results in increased file size and resource requirements, posing challenges for adoption in systems optimized for serial compression formats.
Innovation Solution
Generating metadata for compressed data streams to expose parallelism, allowing for parallel decompression without modifying the compressed data, which minimizes file size increase and maintains compatibility with existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional compression algorithms (LZ, Huffman encoding) are used, then file size is reduced and bandwidth is saved, but decompression must be performed serially which limits processing speed
Solution Approach 1:
The compressed data stream is divided into multiple independent blocks, each with its own Huffman tree and metadata. This segmentation allows parallel processing of multiple blocks simultaneously while maintaining the compression efficiency of traditional algorithms. Each block can be decompressed independently by different processing units, enabling parallel decompression without modifying the overall compression ratio.
2Productivity
If compression algorithms are modified for parallel decompression (e.g., LZ4, LZSSE), then decompression speed improves, but file size increases by 10-15% and compression benefits are lost
Solution Approach 1:
Huffman trees and metadata are pre-computed during the compression phase and stored with each block. This preliminary action eliminates the need for complex runtime computations during decompression, allowing fast parallel processing while maintaining the original compression ratios. The preprocessing work is done once during compression, not at the expense of increased file size.
3Productivity
If new parallel decompression formats are adopted, then processing performance improves, but system reconfiguration is required which increases adoption time and resource requirements
Solution Approach 1:
The patent creates a universal decompression format that works with both traditional sequential processors and modern parallel processors. The metadata structure is designed to be interpretable by both CPU-based sequential decompressors and GPU-based parallel decompressors. This multi-functionality allows the same compressed file format to benefit from parallel processing when available, while maintaining compatibility with existing systems that use traditional algorithms.
Data Source
AI summary
In various examples, metadata may be generated corresponding to compressed data streams that are compressed according to serial compression algorithms—such as arithmetic encoding, entropy encoding, etc.—in order to allow for parallel decompression of the compressed data. As a result, modification to the compressed data stream itself may not be required, and bandwidth and storage requirements of the system may be minimally impacted. In addition, by parallelizing the decompression, the system may benefit from faster decompression times while also reducing or entirely removing the adoption cycle for systems using the metadata for parallel decompression.