How we are leading a 3-XORSAT challenge: from the energy landscape to the algorithm and its efficient implementation on GPUs

TL;DR

This paper introduces a GPU-optimized quasi-greedy algorithm for solving complex 3-XORSAT problems with rugged energy landscapes, achieving top rankings in a recent challenge and providing analytical insights into solution times.

Contribution

It presents a novel GPU-efficient implementation of a quasi-greedy algorithm and proposes a new performance comparison protocol for complex energy landscape problems.

Findings

Achieved first place in the 3-XORSAT challenge.

Provided analytical predictions for solution time growth.

Developed a more effective protocol for algorithm performance comparison.

Abstract

A recent 3-XORSAT challenge required to minimize a very complex and rough energy function, typical of glassy models with a random first order transition and a golf course like energy landscape. We present the ideas beyond the quasi-greedy algorithm and its very efficient implementation on GPUs that are allowing us to rank first in such a competition. We suggest a better protocol to compare algorithmic performances and we also provide analytical predictions about the exponential growth of the times to find the solution in terms of free-energy barriers.