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Note Using a for-loop over a distributed range (drange) is intended for explicit indexing of the distributed dimension of codistributed arrays (such as inside an spmd statement or a parallel job). For most applications involving parallel for-loops you should first try using parfor loops. See Parallel for-Loops (parfor).
If you already have a coarse-grained application to perform, but you do not want to bother with the overhead of defining jobs and tasks, you can take advantage of the ease-of-use that pmode provides. Where an existing program might take hours or days to process all its independent data sets, you can shorten that time by distributing these independent computations over your cluster.
For example, suppose you have the following serial code:
results = zeros(1, numDataSets); for i = 1:numDataSets load(['\\central\myData\dataSet' int2str(i) '.mat']) results(i) = processDataSet(i); end plot(1:numDataSets, results); save \\central\myResults\today.mat results
The following changes make this code operate in parallel, either interactively in spmd or pmode, or in a parallel job:
results = zeros(1, numDataSets, codistributor()); for i = drange(1:numDataSets) load(['\\central\myData\dataSet' int2str(i) '.mat']) results(i) = processDataSet(i); end res = gather(results, 1); if labindex == 1 plot(1:numDataSets, res); print -dtiff -r300 fig.tiff; save \\central\myResults\today.mat res end
Note that the length of the for iteration and the length of the codistributed array results need to match in order to index into results within a for drange loop. This way, no communication is required between the workers. If results was simply a replicated array, as it would have been when running the original code in parallel, each worker would have assigned into its part of results, leaving the remaining parts of results 0. At the end, results would have been a variant, and without explicitly calling labSend and labReceive or gcat, there would be no way to get the total results back to one (or all) workers.
When using the load function, you need to be careful that the data files are accessible to all workers if necessary. The best practice is to use explicit paths to files on a shared file system.
Correspondingly, when using the save function, you should be careful to only have one worker save to a particular file (on a shared file system) at a time. Thus, wrapping the code in if labindex == 1 is recommended.
Because results is distributed across the workers, this example uses gather to collect the data onto worker 1.
A worker cannot plot a visible figure, so the print function creates a viewable file of the plot.
When a for-loop over a distributed range is executed in a parallel job, each worker performs its portion of the loop, so that the workers are all working simultaneously. Because of this, no communication is allowed between the workers while executing a for-drange loop. In particular, a worker has access only to its partition of a codistributed array. Any calculations in such a loop that require a worker to access portions of a codistributed array from another worker will generate an error.
To illustrate this characteristic, you can try the following example, in which one for loop works, but the other does not.
At the pmode prompt, create two codistributed arrays, one an identity matrix, the other set to zeros, distributed across four workers.
D = eye(8, 8, codistributor()) E = zeros(8, 8, codistributor())
By default, these arrays are distributed by columns; that is, each of the four workers contains two columns of each array. If you use these arrays in a for-drange loop, any calculations must be self-contained within each worker. In other words, you can only perform calculations that are limited within each worker to the two columns of the arrays that the workers contain.
For example, suppose you want to set each column of array E to some multiple of the corresponding column of array D:
for j = drange(1:size(D,2)); E(:,j) = j*D(:,j); end
This statement sets the j-th column of E to j times the j-th column of D. In effect, while D is an identity matrix with 1s down the main diagonal, E has the sequence 1, 2, 3, etc., down its main diagonal.
This works because each worker has access to the entire column of D and the entire column of E necessary to perform the calculation, as each worker works independently and simultaneously on two of the eight columns.
Suppose, however, that you attempt to set the values of the columns of E according to different columns of D:
for j = drange(1:size(D,2)); E(:,j) = j*D(:,j+1); end
This method fails, because when j is 2, you are trying to set the second column of E using the third column of D. These columns are stored in different workers, so an error occurs, indicating that communication between the workers is not allowed.
To use for-drange on a codistributed array, the following conditions must exist:
The codistributed array uses a 1-dimensional distribution scheme (not 2dbc).
The distribution complies with the default partition scheme.
The variable over which the for-drange loop is indexing provides the array subscript for the distribution dimension.
All other subscripts can be chosen freely (and can be taken from for-loops over the full range of each dimension).
To loop over all elements in the array, you can use for-drange on the dimension of distribution, and regular for-loops on all other dimensions. The following example executes in an spmd statement running on a parallel pool of 4 workers:
spmd PP = codistributed.zeros(6,8,12); RR = rand(6,8,12,codistributor()) % Default distribution: % by third dimension, evenly across 4 workers. for ii = 1:6 for jj = 1:8 for kk = drange(1:12) PP(ii,jj,kk) = RR(ii,jj,kk) + labindex; end end end end
To view the contents of the array, type: