  We investigate the feasibility of modelling turbulence via numeric functional
integration. By transforming the Burgers' equation into a functional integral
we are able to calculate equal-time spatial correlation of system variables
using standard methods of multidimensional integration. In contrast to direct
numerical simulation, our method allows for simple parallelization of the
problem as the value of the integral within any region can be calculated
separately from others. Thus the calculations required for obtaining one
correlation data set can be distributed to several supercomputers and/or the
cloud simultaneously.
  We present the mathematical background of our method and its numerical
implementation. We are interested in a steady state system with isotropic and
homogeneous turbulence, for which we use a lattice version of the functional
integral used in the perturbative analysis of stochastic transport equations.
The numeric implementation is composed of a fast serial program for evaluating
the integral over a given volume and a parallel Python wrapper that divides the
problem into subvolumes and distributes the work among available processes. The
code is available at https://github.com/iljah/hdintegrator for anyone to
download, use, study, modify and redistribute.
  We present velocity cross correlation for a 10x2 lattice in space and time
respectively, and analyse the computational resources required for the
integration. We also discuss potential improvements to the presented method.

Computational Physics