heat_mpi, a C++ code which solves the 1D Time Dependent Heat Equation using MPI.

The continuous problem:

This program solves

        dUdT - k * d2UdX2 = F(X,T)
over the interval [A,B] with boundary conditions
        U(A,T) = UA(T),
        U(B,T) = UB(T),
over the time interval [T0,T1] with initial conditions
        U(X,T0) = U0(X)

The finite difference discretization

To apply the finite difference method, we define a grid of points X(1) through X(N), and a grid of times T(1) through T(M). In the simplest case, both grids are evenly spaced. We denote by U(I,J) the approximate solution at spatial point X(I) and time T(J).

A second order finite difference can be used to approximate the second derivative in space, using the solution at three points equally separated in space.

A forward Euler approximation to the first derivative in time is used, which relates the value of the solution to its value at a short interval in the future.

Thus, at the spatial point X(I) and time T(J), the discretized differential equation defines a relationship between U(I-1,J), U(I,J), U(I+1,J) and the "future" value U(I,J+1). This relationship can be drawn symbolically as a four node stencil:


Since we are given the value of the solution at the initial time, we can use the stencil, plus the boundary condition information, to advance the solution to the next time step. Repeating this operation gives us an approximation to the solution at every point in the space-time grid.

Using MPI to compute the solution:

To solve the 1D heat equation using MPI, we use a form of domain decomposition. Given P processors, we divided the interval [A,B] into P equal subintervals. Each processor can set up the stencil equations that define the solution almost independently. The exception is that every processor needs to receive a copy of the solution values determined for the nodes on its immediately left and right sides.

Thus, each processor uses MPI to send its leftmost solution value to its left neighbor, and its rightmost solution value to its rightmost neighbor. Of course, each processor must then also receive the corresponding information that its neighbors send to it. (However, the first and last processor only have one neighbor, and use boundary condition information to determine the behavior of the solution at the node which is not next to another processor's node.)

The naive way of setting up the information exchange works, but can be inefficient, since each processor sends a message and then waits for confirmation of receipt, which can't happen until some processor has moved to the "receive" stage, which only happens because the first or last processor doesn't have to receive information on a given step.

It is worth investigating how to improve the information exchange (an exercise for the reader!). The odd processors could SEND while the even processors RECEIVE for instance, guaranteeing that messages would not have to wait in a buffer.

The latest versions of MPI no longer support the special C++ MPI bindings, so the examples given here have reverted to using the C MPI bindings.


The computer code and data files described and made available on this web page are distributed under the MIT license


heat_mpi is available in a C version and a C++ version and a FORTRAN90 version.

Related Data and Programs:

COMMUNICATOR_MPI, a C++ code which creates new communicators involving a subset of initial set of MPI processes in the default communicator MPI_COMM_WORLD.


HELLO_MPI, a C++ code which prints out "Hello, world!" using the MPI parallel programming environment.

mpi_test, C++ codes which illustrate the use of the Message Passing Interface (MPI) for parallel computations in a distributed memory environment.

MULTITASK_MPI, a C++ code which demonstrates how to multitask, that is, to execute several unrelated and distinct tasks simultaneously, using MPI for parallel execution.

PRIME_MPI, a C++ code which counts the number of primes between 1 and N, using MPI for parallel execution.

QUAD_MPI, a C++ code which approximates an integral using a quadrature rule, and carries out the computation in parallel using MPI.

RANDOM_MPI, a C++ code which demonstrates one way to generate the same sequence of random numbers for both sequential execution and parallel execution under MPI.

RING_MPI, a C++ code which uses the MPI parallel programming environment, and measures the time necessary to copy a set of data around a ring of processes.

SATISFY_MPI, a C++ code which demonstrates, for a particular circuit, an exhaustive search for solutions of the circuit satisfiability problem, using MPI to carry out the calculation in parallel.

SEARCH_MPI, a C++ code which searches integers between A and B for a value J such that F(J) = C, using MPI for parallel execution.

WAVE_MPI, a C++ code which uses finite differences and MPI to estimate a solution to the wave equation.


  1. William Gropp, Ewing Lusk, Anthony Skjellum,
    Using MPI: Portable Parallel Programming with the Message-Passing Interface,
    Second Edition,
    MIT Press, 1999,
    ISBN: 0262571323,
    LC: QA76.642.G76.

Source Code:

Last revised on 11 March 2020.