Allpairs User's Manual

Last Updated October 2010

Allpairs is Copyright (C) 2009 The University of Notre Dame. This software is distributed under the GNU General Public License. See the file COPYING for details.


All-Pairs( array A[i], array B[j], function F(x,y) )
returns matrix M where
M[i,j] = F( A[i], B[j] )

The All-Pairs abstraction computes the Cartesian product of two sets, generating a matrix where each cell M[i,j] contains the output of the function F on objects A[i] and B[j]. You provide two sets of data files (as in the above figure, one set is setA = {A0, A1, A2, A3} and the other set is setB = {B0, B1, B2, B3}) and a function F that computes on them (later in the text we refer to this fuction F as either compare program or compare function. You may optionally provide additional parameters to control the actual computation, such as computing only part of the matrix, using a specified number of CPU cores. The abstraction then runs each of the functions in parallel, automatically handling load balancing, data movement, fault tolerance and so on for you.

All-Pairs on a Single Machine

Let's suppose you have a whole lot of files that you want to compare all to each other, named a, b, c, and so on. Suppose that you also have a program named compareit that when invoked as compareit a b will compare files a and b and produce some output summarizing the difference between the two, like this: a b are 45 percent similar

To use the allpairs framework, create a file called set.list that lists each of your files, one per line: a b c ... Then, invoke allpairs_multicore like this: allpairs_multicore set.list set.list compareit The framework will carry out all possible comparisons of the objects, and print the results one by one: a a are 100 percent similar a b are 45 percent similar a c are 37 percent similar ...

For large sets of objects, allpairs_multicore will use as many cores as you have available, and will carefully manage virtual memory to exploit locality and avoid thrashing. Because of this, you should be prepared for the results to come back in any order.

All-Pairs on a Distributed System

So far, we have introduced how to use All-Pairs abstraction on a single machine. But sometimes the All-Pairs problem is too big to allow a single machine to finish it in a reasonable amount of time, even if the single machine is multicore. So, we have built a Work Queue version of the All-Pairs abstraction which allows the users to easily apply the All-Pairs abstraction on clusters, grids or clouds.

To use the All-Pairs Work Queue version, you will need to start a All-Pairs master program called allpairs_master and a number of workers. The workers will perform the tasks distributed by the master and return the results to the master. The individual tasks that the master program distributes are sub-matrix computation tasks and all the tasks would be performed by the allpairs_multicore program on the workers. For end users, the only extra step involved here is starting the workers. Starting the All-Pairs master program is almost identical to starting the All-Pairs multicore program.

For example, to run the same example as above on a distributed system: allpairs_master set.list set.list compareit

This will start the master process, which will wait for workers to connect. Let's suppose the master is running on a machine named If you have access to login to other machines, you could simply start worker processes by hand on each one, like this: % work_queue_worker 9123 If you have access to a batch system like Condor, you can submit multiple workers at once: % condor_submit_workers 9123 10 Submitting job(s).......... Logging submit event(s).......... 10 job(s) submitted to cluster 298.

A similar script is available for Sun Grid Engine: % sge_submit_workers 9123 10

In the above two examples, the first argument is the port number that the master process will be or is listening on and the second the argument is the number of workers to start. Note that 9123 is the default port number that the master process uses. If you use the '-p' option in the allpairs_master to change the listening port, you will need to modify the port argument in the starting worker command accordingly.

Once the workers are running, the allpairs_master can dispatch tasks to each one very quickly. If a worker should fail, Work Queue will retry the work elsewhere, so it is safe to submit many workers to an unreliable system.

When the All-Pairs master process completes, your workers will still be available, so you can either run another master with the same workers, remove them from the batch system, or wait for them to expire. If you do nothing for 15 minutes, they will automatically exit by default. You can change this worker expiration time by setting the '-t' option.

Note that condor_submit_workers and sge_submit_workers are simple shell scripts, so you can edit them directly if you would like to change batch options or other details.

Using an Internal Function

If you have a very fast comparison program (less than a tenth of a second), the allpairs framework may be spending more time starting your program than actually accomplishing real work. If you can express your comparison as a simple function in C, you can embed that into the allpairs framework to achieve significant speedups.

To accomplish this, download the CCTools source code, and build it. Then, look for the file allpairs/src/allpairs_compare.c. At the top, you will see a function named allpairs_compare_CUSTOM, which accepts two memory objects as arguments. Implement your comparison function, and then rebuild the code. Test you code by running allpairs_multicore on a small set of data, but specify CUSTOM as the name of the comparison program. If your tests succeeed on a small set of data, then proceed to using allpairs_master.

We have implemented several internal comparison functions as examples, including:

  • BITWISE - Counts the number of bytes different in each object.
  • SWALIGN - Performs a Smith-Waterman alignment on two genomic sequences.
  • IRIS - Performs a similarity comparison between two iris templates.
  • Tuning Performance

    By default, both allpairs_master and allpairs_multicore will adjust to the proprties of your workload to run it efficiently. allpairs_master will run a few sample executions of your comparison program to measure how long it takes, and then break up the work units into tasks that take abuot one minute each. Likewise, allpairs_multicore will measure the number of cores and amount of memory available on your system, and then arrange the computation to maximize performance.

    If you like, you can use the options to further tune how the problem is decomposed:

  • -t can be used to inform allpairs_master how long (in seconds) it takes to perform each comparison. If given, allpairs_master will not sample the execution, and will start the computation immediately.
  • -x and -y can be used to set the size of the sub-problem dispatched from allpairs_master to allpairs_multicore
  • -c controls the number of cores used by allpairs_multicore, which is all available cores by default.
  • -b controls the block size of elements maintained in memory by allpairs_multicore, which is 3/4 of memory by default.
  • For More Information

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