heat_mpi


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

The continuous problem

This code 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:

                     U(I,J+1)
                      |
                      |
        U(I-1,J)-----U(I,J)--------U(I+1,J)
      

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.

Licensing:

The computer code and data files made available on this web page are distributed under the GNU LGPL license.

Languages:

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

Related Data and codes:

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.

heat_mpi_test

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

LAPLACE_MPI, a C code which solves Laplace's equation on a rectangle, using MPI for parallel execution.

mpi_test, C codes which illustrate the use of the MPI application code interface for carrying out 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.

POISSON_MPI, a C code which computes a solution to the Poisson equation in a rectangle, using the Jacobi iteration to solve the linear system, and MPI to carry out the Jacobi iteration in parallel.

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 codeming 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.

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

Reference:

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

Source Code:


Last revised on 25 June 2020.