Compressed Stream Metadata for Parallel GPU Decompression
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Solution Overview
Problem
Conventional lossless compression algorithms are inherently serial in nature, making them less ideal for parallel processing on architectures like GPUs, and modifying these algorithms for parallel decompression often results in increased file size and compatibility issues with existing systems.
Innovation Solution
Generating metadata for compressed data streams to expose parallelism, allowing for parallel decompression without modifying the data itself, thus maintaining compatibility with existing systems and minimizing bandwidth and storage impacts.
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 due to strong data dependencies
Solution Approach 1:
The patent segments the compressed data stream into independent blocks, each with its own metadata containing offset and length information. This segmentation allows different blocks to be decompressed in parallel by multiple GPU threads simultaneously, eliminating the serial dependency of traditional algorithms while maintaining compression efficiency.
2Ease of operation
If compression algorithms are modified to enable parallel decompression, then parallel processing becomes possible, but file size increases by 10-15% and compatibility with existing systems is lost
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing metadata (offsets and lengths) during the compression phase. This metadata is embedded in the compressed stream, enabling parallel decompression without modifying the core compression algorithm or significantly increasing file size (only 1-2% overhead), while maintaining compatibility with existing DEFLATE-based systems.
3Productivity
If a new compression format is adopted for parallel decompression, then parallel processing is enabled, but reconfiguration costs increase and existing data must be reformatted
Solution Approach 1:
The patent implements a universal solution that works with existing DEFLATE-compressed streams by adding metadata that enables parallel processing. The same compressed stream can be decompressed sequentially by CPUs using traditional algorithms or in parallel by GPUs using the metadata, eliminating the need for reconfiguration or data reformatting and maintaining full compatibility with existing systems.
4Loss of time
If parallel decompression algorithms are implemented, then decompression time decreases, but bandwidth and storage requirements increase
Solution Approach 1:
The patent applies partial action by adding only the essential metadata (offset and length values) needed to enable parallel processing, rather than completely redesigning the compression format. This minimal overhead approach increases decompression speed significantly while keeping the additional bandwidth and storage requirements to only 1-2%, avoiding the substantial resource increases of other parallel 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.