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Readme.md

A Practical Guide to Parallel Computing in Macroeconomics

This repository contains the source code referenced in the paper A Practical Guide to Parallel Computing in Macroeconomics by Jesús Fernández-Villaverde and David Zarruk Valencia.

Abstract from the paper

Parallel computing opens the door to solving and estimating richer models in Economics. From dynamic optimization problems with high dimensionality to structural estimation with complex data, readily-available and economical parallel computing allows researchers to tackle problems in Economics that were beyond the realm of possibility just a decade ago. This paper describes the basics of parallel computing for economists, reviews widely-used implementation routines in Julia, Python, R, Matlab, C++ (OpenMP and MPI) and CUDA and compares performance gains using as a test bed a standard life-cycle problem such as those used in macro, labor, and other fields.

The file Makefile contains the compilation flags used in Linux and can be used to execute the codes in every language.

Files

  1. Cpp_main.cpp: C++ code for OpenMP
  2. CUDA_main.cu: CUDA code
  3. Julia_main_parallel.jl: Julia code
  4. Julia_main_pmap.jl: Julia code
  5. Julia_threads.jl: Julia code with @threads parallelization
  6. Matlab_main.m: Matlab code
  7. MPI_host_file: MPI host file
  8. MPI_main.cpp: C++ code for MPI
  9. Python_main.py: Python code
  10. Python_numba_main.py: Python code with numba parallelization
  11. Rcpp_main.cpp: C++ code for Rcpp package in R
  12. Rcpp_main.R: Rcpp code
  13. R_main.R: R code
  14. Makefile: Makefile to execute codes