GPU-Assisted Lossless Decompression for High-Speed Data Streaming
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Solution Overview
Problem
Current data decompression methods are inefficient, especially at high compression rates, as they require sequential processing and cannot take full advantage of parallel processing capabilities, leading to bottlenecks in real-time data access and transmission.
Innovation Solution
Employing a graphics processing unit (GPU) to decompress data in parallel by executing multiple threads over chunks of compressed data packets, utilizing a series of kernels to de-correlate, map, and compute checksums, thereby achieving high-speed decompression rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sequential processing methods are used for data decompression, then device complexity is reduced, but decompression speed becomes insufficient for high compression rates
Solution Approach 1:
The decompression process is divided into multiple independent kernels (e.g., Rice decoding kernel, de-correlation kernel, checksum verification kernel) that can be executed in parallel on different data packets. Each kernel handles a specific stage of decompression, allowing simultaneous processing of multiple data packets through the same computational pipeline.
Solution Approach 2:
The patent transitions from single-thread sequential processing to multi-thread parallel processing by launching multiple instances of decompression kernels simultaneously. This dimensional shift from one-dimensional sequential execution to multi-dimensional parallel execution enables the system to achieve decompression rates exceeding 300 MB/sec.
2Productivity
If higher compression rates are used to increase information density, then transmission efficiency improves, but decompression time increases beyond acceptable limits
Solution Approach 1:
The system pre-allocates multiple processing threads and kernel instances before decompression begins. Data packets are buffered and prepared for parallel processing, with memory resources pre-configured to handle high-compression-rate data streams. This preliminary preparation eliminates setup overhead during actual decompression operations.
Solution Approach 2:
The parallel kernel architecture enables continuous decompression processing where multiple data packets are processed simultaneously without idle periods. The system maintains continuous useful action by keeping all processing threads actively engaged, avoiding the stop-start nature of sequential processing.
3Ease of operation
If real-time decompression is implemented to enable continuous data streaming, then data accessibility improves, but processing speed becomes the limiting factor
Solution Approach 1:
The decompression pipeline is segmented into distinct computational stages (Rice decoding, de-correlation, checksum verification) that can be independently optimized and executed in parallel. This segmentation allows data to flow continuously through different processing stages, enabling real-time accessibility without bottlenecking at any single stage.
Data Source
AI summary
Systems and methods for decompressing compressed data that has been compressed by way of a lossless compression algorithm are described herein. In a general embodiment, a graphics processing unit (GPU) is programmed to receive compressed data packets and decompress such packets in parallel. The compressed data packets are compressed representations of an image, and the lossless compression algorithm is a Rice compression algorithm.