Copyright © 2014 David William Thomas. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Software to support the Monte Carlo method generates large vectors of pseudo-random numbers and uses these as operands in complex mathematical expressions. When such software is run on standard PC-based hardware, the volume of data involved often exceeds the physical RAM available. To address this problem, vectors must be paged out to disk and paged back in when required. This paging is often the performance bottleneck limiting the execution speed of the software. Because the mathematical expressions are specified in advance of execution, predictive solutions are possible – for instance, by treating the problem similarly to register allocation. The problem of allocating scalar variables to processor registers is a widely studied aspect of compiler implementation. A register allocation algorithm decides which variable is held in which register, when the value in a register can be overwritten, and when a value is stored in, or later retrieved from, main memory. In this paper, register allocation techniques are used to plan the paging of vectors in Monte Carlo software. Two register allocation algorithms are applied to invented vector programs written in a prototype low-level vector language and the results are compared.