PFCS: Prime Factorization Cache System for Deterministic Data Relationship Discovery
By: Duy Le
Potential Business Impact:
Makes computers run 6 times faster.
Cache systems fundamentally limit modern computing performance due to their inability to precisely capture data relationships. While achieving 85-92% hit rates, traditional systems rely on statistical heuristics that cannot guarantee relationship discovery, leading to suboptimal prefetching and resource waste. We present PFCS (Prime Factorization Cache System), which leverages the mathematical uniqueness of prime factorization to achieve deterministic relationship discovery with zero false positives. PFCS assigns unique primes to data elements and represents relationships as composite numbers, enabling the recovery of perfect relationships through factorization. A comprehensive evaluation across database, ML, and HPC workloads demonstrates an average performance improvement of x 6.2, 98.9% hit rates, and a 38% power reduction compared to state-of-the-art systems. The mathematical foundation provides formal guarantees impossible with approximation-based approaches, establishing a new paradigm for cache system design
Similar Papers
Fast Factorized Learning: Powered by In-Memory Database Systems
Databases
Speeds up computer learning by pre-calculating data.
Quantum Prime Factorization: A Novel Approach Based on Fermat Method
Cryptography and Security
Breaks secret codes much faster using quantum computers.
On Solving Structured SAT on Ising Machines: A Semiprime Factorization Study
Emerging Technologies
Solves harder problems by combining new chips and old computers.