hi Adam, yes, if I understand you correctly. A typical use-case for us is trimming and reference-aligning large numbers of short reads, usually paired. Tardis reads the (usually compressed) fastq input file (files if paired), and every "chunksize" sequence records, launches a "reconditioned" command as a job on the cluster. The reconditioned command is the same as the original, except that it refers to the chunk-input file(s) , and the chunk output file(s), rather than to the original input and output files. (Paired fastq files are split in "semantic lock step" - seq names are required to match) After all chunks are launched tardis polls for the expected chunk-outputs (and the log, stdout and stderr files associated with each completed chunk) . Once all results are in, the "join" step is done - chunk outputs are joined to yield the single output file(s) expected by Galaxy (or the user if running from the command line). stderr and stdout from each chunk is collated and emitted as the stdout and stderr of tardis. Process exit codes from the chunk jobs are collated, and tardis exits with an appropriate consensus exit code (i.e. 0 if all were 0 , > 0 if not) Because we are typically dealing with many very large fastq files, we want to avoid if possible too much uncompressed data, and redundant copies of data, as this can very quickly consume our disk resources. A few things included that we have found useful in practice : * tardis reads and splits compressed fastq on the fly, so that the original can remain compressed in place * fastq to fasta conversion is done on the fly and may be specified in the command mark-up - again this means for example original compressed fastq can remain compressed in-place, and we do not need to format-convert the whole file first. This is good for use-cases such as inexpensively blasting a random sample of short reads against a database of potential contaminants. * tardis can read a list file containing the names of (usually compressed fastq or fasta) files, and will treat this as one single input stream (each file is opened in turn and its contents processed as required). Again this is useful in that a number of compressed fastq files can be input to processes such as trimming, blast contamination check or reference alignment, while remaining compressed in place. (Such a list file then works quite fine in Galaxy, provided one is not above cheating by telling Galaxy that a list file containing the names of compressed fastqsanger files, is actually itself a fastqsanger file. This is also useful in avoiding an overly cluttered Galaxy history) * outputs are all compressed by default - e.g. mark-up of "_condition_fastq_output_myfile.fastq" will result in a file "myfile.fastq.gz" being delivered. Uncompressed output is specified by using markup such as "_condition_uncompressedfastq_output_myfile.fastq". (Trying to encourage good housekeeping) * support for random sampling of inputs - the idea again is that this can often help avoid disk and processing contention, by allowing users to obtain inexpensive initial summaries and insights into the data. In some cases (e.g. bad data), this avoids the cost and delay of a complete analysis. I hope that answers your question, and sorry if this post is a bit long. Updating this to use drmaa API is about next on the to-do list (- I 'd be grateful for any tips on python drmaa ? - e.g. best library or approach). Also grateful for any general suggestions or comments. Cheers Alan > -----Original Message----- > From: Adam Brenner [mailto:aebrenne@uci.edu] > Sent: Wednesday, 30 October 2013 5:37 a.m. > To: McCulloch, Alan > Cc: galaxy-dev(a)lists.bx.psu.edu; Harry Mangalam > Subject: Re: [galaxy-dev] tardis job splitter > > Alan, > > At first glance this look promising. I am a little leery of tools that claim to do > parallel processing. However I would like to test it out on our HPC cluster > here at UCI. > > Few questions: > > > > * 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 are strictly a SGE (Son of Grid Engine) cluster with a lot of work done by > Joseph Farran (check pointing, freeq system, etc). Using DRMAA APIs would > be great. If this tool can parallel fastq jobs along with BAM as described, it > would be a great improvement for a number of people here. > > ~Adam > > -- > Adam Brenner > Computer Science, Undergraduate Student > Donald Bren School of Information and Computer Sciences > > Research Computing Support > Office of Information Technology > http://www.oit.uci.edu/rcs/ > > University of California, Irvine > www.ics.uci.edu/~aebrenne/ > aebrenne(a)uci.edu > > > On Mon, Oct 28, 2013 at 7:39 PM, McCulloch, Alan > <alan.mcculloch(a)agresearch.co.nz&gt; wrote: > > 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.2FGCC201 > > 3.2FAbstracts.2FTalks.A_layered_genotyping-by-sequencing_pipeline_usin > > g_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 > > > > > > > > > > > > > > > __________________________________________________________ > _ > > Please keep all replies on the list by using "reply all" > > in your mail client. To manage your subscriptions to this and other > > Galaxy lists, please use the interface at: > > http://lists.bx.psu.edu/ > > > > To search Galaxy mailing lists use the unified search at: > > http://galaxyproject.org/search/mailinglists/

thanks for your time having a look at this, and your comments and advice ! yes I agree. I guess I am using this Galaxy-embedding hack (...i.e. modifying tool configs so that they can splice in tardis-related markup of the command string) because it’s a way I can try out galaxy-embedding of this approach , in an exploratory way (but also getting some good data processing throughput gains along the way of course !). I should have thought about and made that qualification. thanks - that’s a good point about the ordering of the layers. what I was thinking of exploring next, was the possibility of the galaxy engine using the tool input and output metadata as supplied by the tool wrapper XML, in a similar way that tardis uses command-line markup. All that the tardis command-line-markup is providing, is annotation of the command - i.e. which arguments represent input and output, and what are the respective data-types - so that the command and data can be factorised into split-command and split-data. But when the command is embedded in galaxy, that annotation is already supplied by the XML tool config. Therefore it should be possible to incorporate something like the tardis command and data factorisation approach, into the galaxy tool-config interpreter engine, with the required annotation provided by the tool-xml, rather than command-line markup. (....a switch in tool-config.xml for each tool would control whether parallelisation / job splitting was to be enabled for a given tool. If enabled, then 2 or 3 additional user input fields would be generated for the tool UI (e.g. parallelise or not ? chunksize ? and I include an option for randomly sampling the input there as well as its a handy place to put it); and then on submission the engine would factorise the job into splits, with a supervisor similar in function to tardis handling job submission to the cluster and output collation.) thanks for the feedback ! I'll have a think about that approach as well - i.e. tardifying tools and putting these in the tool shed. This is what I am doing internally, with the tardified tools in our local tool shed. Thanks again for looking at this and your encouraging comments. I probably won't make all that much progress with the "put it in the galaxy engine" idea for a while - but thats a potential road map I had thought of . In the near future I need to tidy up a few things, such as getting tardis to use drmaa. I'm also continuing to "tardify" a few tools for which thats useful for us , using the current galaxy-embedding hack, to get some processing done and also as further test cases. Usually each one flushes out another bug or two. Cheers Alan