Scaling Lattice Sieves across Multiple Machines

Lattice sieves are algorithms for finding short vectors in lattices. We present an implementation of two such sieves – known as “BGJ1” and “BDGL” in the literature – that scales across multiple servers (with varying success). This class of algorithms requires exponential memory which had put into question their ability to scale across sieving nodes. We discuss our architecture and optimisations and report experimental evidence of the efficiency of our approach.