Perfect Hash Compression Using Instruction Cache Instead of Lookup Tables
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Solution Overview
Problem
Traditional data compression methods for regular data streams suffer from inefficiencies due to cache misses and computational overhead, failing to fully exploit CPU cache resources and maintain optimal performance.
Innovation Solution
A system utilizing machine learning to generate instruction cache-resident perfect hash functions that transform compression operations into executable instructions, continuously monitoring and regenerating hash functions to adapt to data stream changes, optimizing CPU cache utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lookup tables are stored in data cache for compression operations, then compression functionality is achieved, but cache misses increase and performance degrades
Solution Approach 1:
The patent transitions the storage location of compression lookup data from the data cache dimension to the instruction cache dimension. By storing perfect hash functions as executable code in the instruction cache rather than as lookup tables in the data cache, the system exploits a different cache dimension that does not compete with application data, thereby eliminating cache misses and improving compression throughput.
Solution Approach 2:
The patent replaces the traditional mechanical lookup table approach with a computational approach. Instead of storing static lookup tables in data cache and performing memory access operations, the system generates executable perfect hash function code that computes compression mappings on-the-fly, substituting memory access mechanics with computational mechanics that execute directly from instruction cache.
2Ease of manufacture
If encoding tables are stored in data cache, then compression operations can be performed, but application data is evicted causing cache pollution
Solution Approach 1:
The patent moves compression encoding functionality from the data cache dimension to the instruction cache dimension. By storing perfect hash functions as executable instructions in the instruction cache, the system enables compression operations without occupying data cache space, thereby preventing cache pollution and ensuring application data remains in the data cache.
Solution Approach 2:
The patent segments the cache system into distinct functional dimensions: the instruction cache is dedicated to storing compression function code, while the data cache is dedicated to storing application data. This segmentation eliminates the conflict and competition for cache space between encoding tables and application data, resolving the cache pollution problem.
3Measurement precision
If adaptive coding techniques continuously update encoding tables, then pattern matching improves, but computational overhead increases
Solution Approach 1:
The patent performs preliminary action by pre-computing the perfect hash function characteristics during an offline training phase using machine learning. The system analyzes the data stream distribution beforehand and generates optimized hash function code that is then stored in the instruction cache. This preliminary computation eliminates the need for continuous updates during runtime, reducing computational overhead while maintaining high pattern matching accuracy.
Solution Approach 2:
The patent changes the parameter of encoding table representation from adaptive updating structures to static perfect hash function code. By transforming the encoding mechanism into a pre-computed functional form with fixed parameters stored in instruction cache, the system achieves high pattern matching accuracy without the continuous computational overhead of adaptive updates.
4Productivity
If machine learning generates optimized hash functions, then compression efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies machine learning in a preliminary offline phase to generate optimized perfect hash function characteristics. The complex machine learning model training and optimization occur beforehand, producing a compact set of function parameters and code. This preliminary action separates the complex optimization process from the runtime compression operation, improving compression efficiency while keeping the runtime system simple.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex machine learning model output. Instead of running the full machine learning system during compression, the patent generates executable perfect hash function code that captures the essential compression logic. This copying approach maintains high compression efficiency while reducing system complexity at runtime by replacing the complex model with its functional representation.
Data Source
AI summary
A system and method for compressing regular data streams using instruction cache-resident perfect hash functions. The system employs machine learning to analyze regular data streams and generate optimized perfect hash functions that remain resident in CPU instruction cache during compression operations. Unlike traditional compression methods that store lookup tables in data cache, this approach transforms compression operations into executable instructions, enabling efficient processing while leaving data cache available for application data. The system comprises continuous monitoring of compression performance and data stream characteristics, automatically regenerating hash functions when significant changes are detected. The method encompasses analyzing data streams, generating hash function characteristics through machine learning, transforming these characteristics into optimized instructions, and deploying them in instruction cache for runtime compression operations. This approach improves compression performance for regular data streams by leveraging CPU cache hierarchy more efficiently than traditional lookup table-based methods.