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A Multigrid Application

The multigrid method [5] is a fast algorithm for solution of linear and nonlinear problems. It uses a hierarchy or stack of grids of different granularity (typically with a geometric progression of grid-spacings, increasing by a factor of two up from finest to coarsest grid). Applied to a basic relaxation method, for example, multigrid hugely accelerates elimination of the residual by restricting a smoothed version of the error term to a coarser grid, computing a correction term on the coarse grid, then interpolating this term back to the original fine grid. Because computation of the correction term on the fine grid can itself be handled as a relaxation problem, the strategy can be applied recursively all the way up the stack of grids.

In our example, we apply the multigrid scheme to solution of the two-dimensional Poisson equation. For the basic, unaccelerated, solution scheme we use red-black relaxation. An HPJava method for red-black relaxation is given in Figure 1. This looks something like an HPF program with different syntax. One obvious difference is that the base language is Java instead of Fortran. The HPJava type signature double [[-,-]] means a two dimensional distributed array of double numbers[*]. So the arguments passed to the method relax() will be distributed arrays

Figure 1: Red black relaxation on array u.
\begin{figure}\small\begin{verbatim}static void relax(int itmax, int np,
doub...
... u [i + 1, j] +
u [i, j - 1] + u [i, j + 1]);
}
}
}\end{verbatim}\end{figure}

The inquiry rng() on the distributed array f returns the Range objects x, y. These describe the distribution format of the array index (for the two dimensions).

The HPJava overall construct operates like a forall construct, with one important difference. In the HPJava construct one must specify how the iteration space of the parallel loop is distributed over processors. This is done by specifying a Range object in the header of the construct.

The variables i, j in the figure are called distributed index symbols. Distributed indexes are scoped by the overall constructs that use them. They are not integer variables, and there is no syntax to declare a distributed index except through an overall construct (or an at construct--see later). The usual Java scoping rules for local variables apply: one can't for example use i as the index of an overall if there is already a local variable i in scope--the compiler doesn't allow it.

An unusual feature of the HPJava programming model is that the subscripts in a distributed array element reference usually must be distributed index symbols. And these symbols must be distributed with the essentially same format as the arrays they subscript. As a special case, shifted index expressions like i+1 are allowed as subscripts, but only if the distributed array was created with ghost regions. Information on ghost regions, along with other information about distribution format, is captured in the Range object associated with the array dimension or index.

These requirements ensure that a subscripting operation in an overall construct only accesses locally held elements. They place very stringent limitations on what kind of expression can appear as a subscript of a distributed array. We justify this by noting that this restricted kind of data parallel loop is a frequently recurring pattern in SPMD programs in general, and it is convenient to have it captured in syntax. A glance at the full source of the applications described in this paper should make this claim more plausible[*].

The method Adlib.writeHalo() is a communication method (from the library called Adlib). It performs the edge-exchange to fill in the ghost regions. As emphasized earlier, the compiler is not responsible for inserting communications--this is the programmer's responsibility. We assume this should be acceptable to programmers currently accustomed to using MPI and similar libraries for communication.

Figure 2: Illustration of restrict operation
\includegraphics[width=3.3in]{reoperation.eps}

Because of the special role of distributed index symbols in array subscripts, it is best not to think of the expressions i, j, i+1, etc, as having a numeric value: instead they are treated as a special kind of thing in the language. We use the notation i` to extract the numeric global index associated with i, say[*]. In particular, use of this expression in the modulo 2 expression in the inner overall construct in Figure 1 implements the red-black pattern of accesses.

This completes the description of most ``non-obvious'' features of HPJava syntax. Remaining examples in the paper either recycle these basic ideas, or just introduce new library routines; or they import relatively uncontroversial syntax, like a syntax for array sections.

Figure 2 visualizes the ``restrict'' operation that is used to transfer the error term from a fine grid to a coarse grid. The HPJava code is given in Figure 3. The restrict operation here computes the residual term at sites of the fine grid with even coordinate values, then sends these values to the coarse grid. In multigrid the restricted residual from the fine grid becomes the RHS of a new equation on the coarse grid. The implementation uses a temporary array tf which should be aligned with the fine grid (only a subset of elements of this array are actually used). The last line introduces two new features: distributed array sections, and the library function Adlib.remap(). Sections work in HPJava in much the same way as in Fortran--one small syntactic difference is that they use double brackets. The bounds in the fc section ensure that edge values, corresponding to boundary conditions, are not modified. The stride in the tf section ensures only values with even subscripts are selected. The Adlib.remap() operation is needed because in general there is no simple relation between the distribution format of the fine and coarse grid--this function introduces the communications necessary to perform an assignment between any two distributed arrays with unrelated distribution format. As another example, the interpolation code of Figure 4 performs the complementary transformation from the coarse grid to the fine grid.

Figure 3: HPJava code for restrict operation.
\begin{figure}\small\begin{verbatim}static void restr(int npc, int npf,
doubl...
...: nc - 1]],
tf [[2 : nf - 2 : 2, 2 : nf - 2 : 2]]);
}\end{verbatim}\end{figure}

Figure 4: HPJava code for interpolate operation.
\begin{figure}\small\begin{verbatim}static void interp(int npf, double[[-,-]] ...
... uf [i, j] += 0.5 * (tf [i, j - 1] + tf [i, j + 1]);
}\end{verbatim}\end{figure}

The basic pattern here depends only on the geometry of the problem. More complex (perhaps non-linear) equations with similar geometry could be tackled by similar code. Problems with more dimensions can also be programmed in a similar way.


next up previous
Next: A CFD Application Up: Applications of HPJava Previous: Features of the HPJava
Bryan Carpenter 2004-04-24