Large-scale Sustainable Search on Unconventional Computing Hardware

TL;DR

This paper explores the potential of unconventional optical hardware to efficiently compute PageRank eigenvectors for large web graphs, offering a sustainable alternative to traditional computing methods.

Contribution

It demonstrates the feasibility of using optical parametric oscillators and related hardware to accelerate eigenvector computation for web ranking, reducing power consumption.

Findings

Optical machines can reliably reconstruct principal eigenvectors of large web graphs.

Unconventional hardware may significantly reduce power consumption compared to classical computers.

Alternative ranking methods based on spin Hamiltonian minimization are proposed.

Abstract

Since the advent of the Internet, quantifying the relative importance of web pages is at the core of search engine methods. According to one algorithm, PageRank, the worldwide web structure is represented by the Google matrix, whose principal eigenvector components assign a numerical value to web pages for their ranking. Finding such a dominant eigenvector on an ever-growing number of web pages becomes a computationally intensive task incompatible with Moore's Law. We demonstrate that special-purpose optical machines such as networks of optical parametric oscillators, lasers, and gain-dissipative condensates, may aid in accelerating the reliable reconstruction of principal eigenvectors of real-life web graphs. We discuss the feasibility of simulating the PageRank algorithm on large Google matrices using such unconventional hardware. We offer alternative rankings based on the minimisation of spin Hamiltonians. Our estimates show that special-purpose optical machines may provide dramatic improvements in power consumption over classical computing architectures.