LZ77 Decompression Fast Path for Branch Misprediction Bottlenecks
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Solution Overview
Problem
LZ77-based data decompression algorithms face performance issues due to branch misprediction penalties from conditional branches, which hinder efficient decompression, especially in processors with limited branch prediction capabilities.
Innovation Solution
Implementing a computing device with a fast-path decompression routine that predicts the outcome of input symbols using branch prediction hardware, avoiding data-dependent branch instructions and utilizing small data tables to determine symbol locations, thereby reducing branch misprediction penalties and improving throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional LZ77 decompression algorithms use numerous conditional branches to categorize input symbols, then the algorithm can handle all symbol types correctly, but branch misprediction penalties reduce decompression performance
Solution Approach 1:
The patent divides the decompression process into multiple specialized routines (fast-path and slow-path) that handle different symbol types. The fast-path routine handles common symbol types with predictable branches, while the slow-path routine handles rare symbol types. This segmentation allows the majority of symbols to be processed with highly predictable branch patterns, improving branch prediction accuracy and decompression speed while maintaining correctness for all symbol types.
Solution Approach 2:
The patent performs preliminary classification of input symbols to determine which fast-path or slow-path routine should be executed. By pre-categorizing symbols and selecting the appropriate execution path before detailed processing, the system establishes highly predictable branch patterns early in the execution flow. This preliminary action allows branch prediction hardware to accurately predict the outcome of conditional branches, reducing misprediction penalties.
2Adaptability or versatility
If the decompression algorithm uses data-dependent branch instructions to handle variable symbol types, then the algorithm can adapt to different input data, but branch prediction hardware cannot correctly predict branch outcomes
Solution Approach 1:
The patent implements a dynamic decompression system that adapts its execution path based on the input symbol type. The system uses preliminary classification to dynamically select between fast-path and slow-path routines, and within each path, further dynamically selects specific handling based on symbol characteristics. This dynamic adaptation maintains versatility in handling variable symbol types while constraining the actual branch instructions to highly predictable patterns that branch prediction hardware can accurately forecast.
Data Source
AI summary
Technologies for data decompression include a computing device that reads a symbol tag byte from an input stream. The computing device determines whether the symbol can be decoded using a fast-path routine, and if not, executes a slow-path routine to decompress the symbol. The slow-path routine may include data-dependent branch instructions that may be unpredictable using branch prediction hardware. For the fast-path routine, the computing device determines a next symbol increment value, a literal increment value, a data length, and an offset based on the tag byte, without executing an unpredictable branch instruction. The computing device sets a source pointer to either literal data or reference data as a function of the tag byte, without executing an unpredictable branch instruction. The computing device may set the source pointer using a conditional move instruction. The computing device copies the data and processes remaining symbols. Other embodiments are described and claimed.