Compressed Stream Metadata for Parallel Decompression Compatibility
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Solution Overview
Problem
Conventional lossless compression algorithms are inherently serial, limiting their effectiveness on parallel processing units like GPUs, and modifying these algorithms for parallel decompression often results in increased file size and compatibility issues with existing systems.
Innovation Solution
Generate metadata for compressed data streams to expose parallelism, allowing decompression on both CPUs and GPUs without altering the compressed data format, using techniques like Huffman encoding and Lempel-Ziv algorithms, with minimal file size increase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression algorithms (LZ, Huffman) are used, then file size is reduced and bandwidth is saved, but decompression cannot be performed in parallel on GPUs
Solution Approach 1:
The compressed data stream is divided into multiple independent blocks, each with its own metadata header. This segmentation allows different blocks to be decompressed in parallel on GPU while maintaining compatibility with sequential decompression on CPU, as each block can be processed independently without affecting others.
Solution Approach 2:
A metadata structure is introduced as an intermediary between the compressed data and the decompression process. The metadata contains block delimiters and length information that enables parallel processors to identify and process independent blocks, while existing systems can ignore the metadata and process the compressed data as before.
2Productivity
If compression algorithms are modified for parallel decompression (e.g., LZ4, LZSSE), then decompression speed improves, but file size increases by 10-15%
Solution Approach 1:
Instead of completely redesigning the compression algorithm, the patent applies partial modification by adding a lightweight metadata layer that enables parallel processing. This excessive action of adding metadata is minimal compared to full algorithm redesign, resulting in only 1-2% file size increase rather than 10-15%.
3Productivity
If new compression formats are adopted for parallel processing, then decompression performance improves, but system reconfiguration and data reformating are required
Solution Approach 1:
The patent creates a universal format that serves multiple functions: it works with both sequential CPU decompression and parallel GPU decompression. The metadata structure is designed to be transparent to existing systems while enabling new functionality, eliminating the need for system reconfiguration or data reformating.
4Loss of time
If metadata is added to enable parallel decompression, then decompression time decreases, but file size increases
Solution Approach 1:
The patent changes the structural parameters of the data format by adding a metadata layer with specific delimiters and length fields. This parameter change enables parallel processing capability while keeping the metadata size minimal (1-2% increase), achieving a favorable trade-off between decompression time reduction and file size increase.
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.