Running Pipeline Stages in Parallel

A frequent need in Bioinformatics pipelines is to execute several tasks at the same time. There are two main cases where you want to do this:

  1. you have one set of data (eg. a sample) that needs to undergo several independent operations that can be done at the same time
  2. your data is made up of many separate samples which can be processed independently through part or all of your pipeline

In both cases you can save a lot of time by doing the operations in parallel instead of sequentially. Bpipe supports this kind of parallel execution with a simple syntax that helps you declare which parts of your pipeline can execute at the same time and what inputs should go to them.

Executing Multiple Stages Simultaneously on the Same Data

Suppose you had a very simple "hello world" pipeline as illustrated below: {
  hello + world

Now suppose you wanted to add a second "mars" stage that would execute simultaneously with the "world" pipeline stage. All you need to do is place all the stages that execute together in square brackets and separate them with commas: {
  hello + [ world,mars ]

Note: if you are familiar with Groovy syntax, you will notice that the square bracket notation is how you define a list in Groovy. Thus all we are saying is that if you give Bpipe a list of stages to process, it executes them in parallel.

You can execute multiple stages in parallel too: {
  hello + [ blue + world, red + mars ]

Here "blue + world" and "red + mars" form sub-pipelines that execute in parallel with each other. You can have more stages at the end that are sequential: {
  hello + [ blue + world, red + mars ] + nice_to_see_you

Note that in this case the last stage nice_to_see_you won't execute until all of the preceding stages executing in parallel have finished. It will receive all the outputs combined from both the "blue + world" and "red + mars" stages as input.

You can also nest parallel tasks if you wish: {
  hello + [ blue + world, red + [mars,venus] ] + nice_to_see_you

In this case the mars and venus stages will execute simultaneously, but only after the red stage has finished executing.

Parallelizing Based on Chromosome

In bioinformatics it is often possible to run operations simultaneously across multiple chromosomes. Bpipe makes this easy to achieve using a special syntax as follows: {
  chr(1..5) * [ hello ]

This will run 5 parallel instances of the 'hello' pipeline stage, each receiving the same file(s) as input. Each stage will receive an implicit chr variable that can be used to refer to the chromosome that is to be processed by the stage. This can be used with many tools that accept the chromosome as an input to specify the region to process. For example, with samtools:

hello = {
    exec """samtools view test.bam $chr | some_other_tool """

Multiple ranges or single chromosomes can be specified: {
  chr(1..10, 'X','Y') * [ hello ]

This would run 12 parallel stages, passing 'chr1' through to 'chr10' and 'chrX' and 'chrY' as the the chr variable to all the different stages.

Executing Multiple Stages Simultaneously on Different Data

In the above examples each parallel stage received the same input files and operated on them together. Sometimes however what you really want is to have each input file or groups of your input files processed independently through the same stage (or stages). Bpipe calls this input splitting and gives you a concise and simple way to achieve it.

Suppose we have 10 input files and we want all 10 files named input_1.txt to input_10.txt to be processed at the same time. Here is how it looks: {
   "input_%.txt" * [ hello + world ] + nice_to_see_you

There are two things to notice here: 1. The pipeline starts with an input splitting pattern containing a % character that shows which part of the file name should be used to split the input files into groups 1. The pipeline uses a ** (or multiplication) operator in your pipeline definition instead of the usual +

Note that Bpipe still requires you to specify the files to match against on the command line when you run your pipeline; the matching is not done on files in the file system, but on files that are part of the pipeline. So if you saved it in a file called 'helloworld.pipe' then you might run this example using something like this:

bpipe run helloworld.pipe input*.txt

Input Splitting Patterns


Bpipe uses a very simple wildcard pattern syntax to let you indicate how your files should be split into groups for processing. In these patterns you simply replace the portion of file names that indicates what group the file belongs to with the percent character which acts as a wildcard (matches any number of characters). Files that share the same grouping portion will be passed together to the the parallel pipeline stages to process.

The pattern matching used for grouping files is a substring match. Therefore you only need to supply enough of the input file name to uniquely identify where the grouping character is. For example, the following pipeline is equivalent to the one above: {
   "_%." * [ hello + world ] + nice_to_see_you

This means Bpipe will look for the first (and shortest) token in the file name that is flanked by an underscore on the left and a period (. character) on the right. This may be useful if your files have portions of their names that differ but are not related to how you wish to group them.

Note: files that mismatch the grouping operator pattern will be filtered out of the inputs altogether. This feature can be useful by allowing you to have a directory full of files that you provide as input even if some of them are not real input files - Bpipe will filter out only the ones it needs based on the pattern you specified.


Bpipe supports one other special character in its input splitting patterns: the * wildcard. This also acts as a wildcard match but it does not split the input into groups. Instead, it affects ordering within the groups that are split. When Bpipe matches a * character in an input splitting pattern it first splits the files into their groups (based on the % match) and then sorts them based on the portions that match the * character. This helps you ensure that even after splitting, your files are still in a sensible order. For example, consider the following input files

You can split and sort the inputs using a pattern:


This pattern will then split and order the files like so:

Group 1 - input_1_1.txt, input_1_2.txt

Group 2 - input_2_1.txt, input_2_2.txt

Notice that the second group had its files reversed in order because Bpipe sorted them.

Explicitly Specifying Parallel Paths

If you don't get the flexibility you need from the above mechanisms, you can set the branch paths yourself explicitly by specifying a Groovy List or a Map that tells Bpipe what paths you want. When you specify a Map, the keys in the map are interpreted as branch names and the values in the Map are interpreted as files, or lists of files, that are supplied to the branch as input.

For example:

// Create a data structure (Map) that maps branches to files
def branches = [
    sample1: ["sample1_2.fastq.gz"],
    sample2: ["sample2_2.fastq.gz"],
    sample3: ["sample3_2.fastq.gz"]

align = {
   // keep the sample name as a branch variable
   branch.sample = 

run { branches * [ align ] }

In this example the align stage will run three times in parallel and the files specified for each branch will be explicitly provided to it. Of course, in normal usage this technique would not be best applied by specifying them statically, but rather for when you want to read the information from a file or database or other source and construct the branch => file mapping from that.

Allocating Threads to Commands

Sometimes you know in advance exactly how many threads you wish to use with a command. In that case, it makes sense to specify it using the procs attribute in a configuration, or to specify it directly in the pipeline stage with the Uses clause.

Other times, however, you want to be more flexible, and allow resources to be assigned more dynamically. This means that if more compute power is available, you can take advantage of it, and when less is available, your pipeline can scale down to run on what is available. Bpipe offers some capability for this through the special $threads variable. This variable can behave in two different ways:


align_bwa = {
  exec "bwa aln -t $threads ref.fa $input.fq"

Here Bpipe will assign a value to $threads that tries to best utilise the total available concurrency. For example, if there are 32 cores on the computer you are using and there are 4 of these stages that execute in parallel, each one should get allocated 8 cores, as long as they start at approximately the same time.

Limiting Dynamic Concurrency

Bpipe implements dynamic concurrency in a somewhat subtle manner. When a command asks for a value for $threads, Bpipe needs to decide what other tasks the current pool of available threads should be shared with. If it simply calculates this value immediately then the first command that tries to use $threads will get allocated all the remaining concurrency slots and others will have none available. To avoid this, when a command asks for $threads, Bpipe pauses the current branch and waits until all concurrently executing paths in the pipeline are either executing tasks or have also requested $threads. Then the threads are divided up among all the requestors, and allocated fairly.

In general, the above process results in a "fair" allocation of threads to competing tasks, but you should be aware that "greedy" behavior can still emerge for tasks that are scattered apart in time. For this reason, it can be useful to set an upper limit on how many threads Bpipe will give to any one task. You can do this by setting the max_per_command_threads variable in bpipe.config. This will limit the total number of threads that can be allocated.

Another approach to this is to specify thread allocation ranges in the configuration of your commands via the procs variable. For example, we can reserve between 2 and 8 threads for bwa in the previous example by specifying in bpipe.config:

commands {
    bwa {

This will allow Bpipe to set $threads to anything between 2 and 8.


  1. When you run stages in parallel, you should always use the Bpipe specified output file (defined for you as the $output variable) rather than hard coding the file names. This is needed because when you define output files yourself Bpipe detects the creation of the files and interprets them as outputs of whatever pipeline stage is currently executing. However with multiple stages executing this detection can assign the the output to the wrong pipeline stage or even the wrong parallel instance of the correct pipeline stage. If you wish to "hard code" the file name that is output from a stage (or part of a stage) you can still do so, but you should do it by wrapping the command that creates that output with a Produce statement, for example:

hello = {
  produce("hello.txt") {
    exec "cp $input $output"

Even this is not recommended because you may end up overwriting your output files from multiple parallel threads if you are not careful. In general, whenever you can, let Bpipe manage the names of your files and just give it hints to make them look the way you want.