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Abstract: Nowadays the state of the art Density Functional Theory (DFT) codes are based on local (LDA) or semilocal (GGA) energy functionals. Recently the theory of a truly nonlocal energy functional has been developed. It has been used mostly as a post-DFT calculation approach, i.e. by applying the functional to the charge density calculated using any standard DFT code, thus obtaining a new improved value for the total energy of the system. Nonlocal calculation is computationally quite expensive and scales as where N is the number of points in which the density is defined, and a massively parallel calculation is welcome for a wider applicability of the new approach. In this article we present a code which accomplishes this goal. Program summary: Program title: JuNoLo Catalogue identifier: AEFM_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEFM_v1_0.html Program obtainable from: CPC Program Library, Queen''s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 176 980 No. of bytes in distributed program, including test data, etc.: 2 126 072 Distribution format: tar.gz Programming language: Fortran 90 Computer: any architecture with a Fortran 90 compiler Operating system: Linux, AIX Has the code been vectorised or parallelized?: Yes, from 1 to 65536 processors may be used. RAM: depends strongly on the problem''s size. Classification: 7.3 External routines: • FFTW (http://www.tw.org/) • MPI (http://www.mcs.anl.gov/research/projects/mpich2/ or http://www.lam-mpi.org/) Nature of problem: Obtaining the value of the nonlocal vdW-DF energy based on the charge density distribution obtained from some Density Functional Theory code. Solution method: Numerical calculation of the double sum is implemented in a parallel F90 code. Calculation of this sum yields the required nonlocal vdW-DF energy. Unusual features: Binds to virtually any DFT program. Additional comments: Excellent parallelization features. Running time: Depends strongly on the size of the problem and the number of CPUs used. [Copyright &y& Elsevier]