System and method for compressing regular data streams using instruction cache-resident perfect hash functions

Abstract

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.

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