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Research in reflection seismology is dependent, as in all
computational sciences, on the computer tools available.
Seismology is an ``experimental'' science, that is,
the ``theory'' (new algorithms) must be checked against the results
of the ``experiments'' (real data results).
Therefore, the speed of the available
computers determines the limits of the computational cost of the new
methods that is practical to investigate.
Massively parallel computers hold the promise of expanding these
limits by increasing the computational
power available to researchers by some order of magnitude.
But, to actually keep this promise, massively parallel
computers must show themselves to be capable
of solving the numerical problems that are of interest to
Geophysicists, and to do so without requiring a large
effort in programming from the users.
Among the seismic algorithms that are most widely used,
and most computationally intensive, are algorithms related
to the solution of the wave equation.
Wave-equation algorithms are fundamental to seismic data processing,
as well as of data modeling.
The wave equation is usually solved or by one of the
following basic methods, or by a combination of them:
Fourier methods, finite-difference schemes, and Kirchhoff integrals.
In this paper, I describe an efficient implementation
of each of these three basic algorithms.
The algorithms that I present are very simple
but they are useful for illustrating the ideas underlying
the efficient implementation of wave-equation algorithms on
a parallel computer.
They achieve high efficiency because they
respect the two rules of parallel algorithms:
low communications overhead and load balancing.

I implemented the example algorithms on the Connection Machine (Hillis, 1985)
that SEP recently acquired.
The algorithms were coded in Fortran 90 (Brainerd et al., 1990);
I show some code fragments from my programs.

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Stanford Exploration Project

12/18/1997