dear all, There have been a few posts lately about doing distributed computing via Galaxy - i.e. job splitters etc - below a contribution of some ideas we have developed and applied in our work, where we have arranged for some Galaxy tools to execute in parallel on our cluster.
We have developed a job-splitter script "tardis.py" (available from https://bitbucket.org/agr-bifo/tardis), which takes marked-up standard unix commands that run an application or tool. The mark-up is prefixed to the input and output command-line options. Tardis strips off the mark-up, and re-writes the commands to refer to split inputs and outputs, which are then executed in parallel e.g. on a distributed compute resource. Tardis knows the output files to expect and how to join them back together. (This was referred to in our GCC2013 talk http://wiki.galaxyproject.org/Events/GCC2013/Abstracts#Events.2FGCC2013.2FAbstracts.2FTalks.A_layered_genotyping-by-sequencing_pipeline_using_Galaxy ) Any reasonable unix based data processing or analysis command may be marked up and run using tardis, though of course tardis needs to know how to split and join the data. Our approach also assumes a "symmetrical" HPC cluster configuration, in the sense that each node sees the same view of the file system (and has the required underlying application installed). We use tardis to support both Galaxy and command-line based compute. Background / design pattern / motivating analogy: Galaxy provides a high level "end to end" view of a workflow; the HPC cluster resource that one uses then involves spraying chunks of data out into parallel processes, usually in the form of some kind of distributed compute cluster - but an end-user looking at a Galaxy history, should ideally not be able to tell whether the workflow was run as a single process on the server, or via many parallel processes on the cluster (apart from the fact that when run in parallel on the cluster, its alot faster!). We noticed that the TCP / IP layered networking protocol stack provides a useful metaphor and design pattern - with the "end to end" topology of a Galaxy workflow corresponding to the transport layer of TCP/ IP; and the distribution of computation across a cluster corresponding to the next TCP/IP layer down - the packet-routing layer. This picture suggested a strongly layered approach to provisioning Galaxy with parallelised compute on split data, and hence to an approach in which the footprint in the Galaxy code-base, of parallel / distributed compute support, should ideally (from the layered-design point of view) be minimal and superficial. Thus in our approach so far, the only footprint is in the tool config files, where we arrange the templating to (optionally) prefix the required tardis mark-up to the input and output command options, and the tardis script name to the command as a whole. tardis then takes care of rewriting and launching all of the jobs, and finally joining the results back together and putting them where galaxy expects them to be (and also housekeeping such as collating and passing up stderr and stdout , and appropriate process exit codes). (For each galaxy job, tardis creates a working folder in a designated scratch area, where input files are uncompressed and split; job files and their output are stored; logging is done etc. Split data is cleaned up at the end unless there was an error in some part of the job, in which case everything is retained for debugging and in some cases restart) (We modify Galaxy tool-configs so that the user can optionally choose to run the tool on our HPC cluster - there are three HPC related input fields, appended to the input section of a tool. Here the user selects whether they want to use our cluster and if so, they specify the chunk size, and can also at that point specify a sampling rate, since we often find it useful to be able to run preliminary analyses on a random sample of (for example) single or paired-end NGS sequence data, to obtain a fairly quick snapshot of the data, before the expense of a complete run. We found it convenient to include support for input sampling in tardis). The pdf document at https://bitbucket.org/agr-bifo/tardis includes a number of examples of marking up a command, and also a simple example of a galaxy tool-config that has been modified to include support for optionally running the job on our HPC cluster via the tardis pre-processor. Known limitations: * we have not yet attempted to integrate our approach with the existing Galaxy job-splitting distributed compute support, partly because of our "layered" design goal (admittedly also partly because of ignorance about its details ! ) * our current implementation is quite naive in the distributed compute API it uses - it supports launching condor job files (and also native sub-processes) - our plan is to replace that with using the drmaa API * we would like to integrate it better with the galaxy type system, probably via a galaxy-tardis wrapper We would be keen to contribute our approach to Galaxy if people are interested. Cheers Alan McCulloch Bioinformatics Software Engineer AgResearch NZ
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