Wednesday, March 31, 2010

Programming in the Large -- Multicore Goodness

The lowly shell (bash, zsh, csh, the whole bunch) is usually a dreadful programming environment. Perfectly awful. With some care, you can easily architect applications so that you don't really need the shell for very much.

However, there is a precious nugget of goodness within the shell's programming language. The Linux shell's have a cool Programming in the Large (PITL) language. This combines executable programs using a number of operators. These operators are an excellent set of design patterns that can help us create complex multi-processing pipelines.

The best part about the shell's PITL language is that a simple shell pipeline will use every core in our processor, maximizing throughput and minimizing the amount of programming we have to do.

PITL Objects

This PITL language has a simple set of operators. If your programs are well-behaved, the language is, in a formal mathematical sense, closed. You can apply PITL operators to combinations of programs to get new composite programs.

To be well-behaved a program must read from standard in and write to standard out. The inputs and outputs must be in some regular syntax. Regular, here, means parseable by regular expressions or regular grammars.

As a special case, we need to create a special program that can read from someplace other than standard in, but write it's content to standard out. A program like cat.

Note that any map-reduce step will be well-behaved. To seed the map-reduce pipeline we use cat as the "head-0f-the-pipeline".

PITL Operators

We'll look at the composition operators using three short-hand commands: p1, p2 and p3. Each of these is "well-behaved": they read from stdin and write to stdout.

Typically, running a program from the shell involves a much longer and more involved command-line, but we'll use these three aliases to strip away the details and look at the design patterns. You can imagine them as being or even python

Sequence, ;. A sequence of steps is shown in the shell on multiple lines, or with the ; operator. In effect a sequence declares a program as the precondition for the following program. We can summarize this as "p1 ; p2".

Parallel, &. A parallel operation is shown by using the & operator. The two programs are declared as independent operations. We can summarize this as "p1 & p2". As an extension to this, a trailing "&" allows the programs to run in parallel with the shell itself; this gives you a next prompt right away.

This allows the OS to schedule your two processes on two or more cores. However, there's no real relationship between the processes.

Pipeline, |. A pipeline operation is shown using the | operator. We can summarize this as "p1 | p2". In addition to the logical connection of one program's input being the other program's output, both programs can run in parallel, also.

This allows the OS to schedule your two processes on two or more cores. Indeed, the more stages in the pipeline, the more cores you'll need to do the processing. Best, of course, the I/O is through a shared buffer and doesn't involve any physical transfer of bytes among the processes.

This is a very powerful way to use multiple cores with minimal programming.

If one part of a pipeline is a sort, however, the parallel processing is limited. The sort must read all input before providing any output. A process like "p1 | sort | p3" is effectively serial: "p1 > temp1; sort temp1 >temp2; p3 temp2".

Grouping. Programs are grouped by ()'s of various kinds ({} and ``). Also the conditional and repetitive statements effectively group series of programs. We use syntax like "( p1 & p2 ); p3" to show the situation where p1 and p2 must both complete before p3 can begin processing.

Using All the Cores

Most importantly, something like "( p1 ; p2 ) | p3" directs the output of two programs into a third for further processing. And the two program sequence runs concurrently with that third program. This will use at least two cores.

What we'd also like is "( p1 & p2 ) | p3", but this doesn't work as well as we might hope. The output from p1 and p2 are not a stream of atomic writes carefully interleaved. They are non-atomic buffer copies that are impossible to disentangle. Sadly, this can't easily be implemented.

Other Features

The shell offers a few other composition operations, but as we start using these, we find that the shell isn't a very effective programming environment. While the shell pipeline notation is outstandingly cool, other parts of the notation are weak.

Conditional. The if, case and select shell statements define conditional processing and groupings for programs. Trying to evaluate expressions is where this gets dicey and needlessly complex.

Repetitive. The for, while and until shell statements define repetitive processing for a program. Again, expression evaluation is crummy. The for statement is usable without needless complication.

Four of these PITL operators (sequence, parallel, pipeline, grouping) give us a hint as to how we can proceed to design large-scale applications that will use every core we own.

Implementation Hints

You can -- trivially -- use all your cores simply by using the shell appropriately. Use the shell's pipeline features and nothing else, and you'll use every core you own.

For everything outside the pipelining features, use Python or something more civilized.

And, you have a nice hybrid solution: iterpipes. You can construct pleasant, simple, "use-all-the-cores" pipelines directly in Python.


  1. One thing that helps me run my everyday tasks in parallel using N cores is the xargs -P N command. Diomidis Spinellis wrote an indroductionary blog post on this subject Parallelizing Jobs with xargs a while ago. xargs -P does a better job in balancing the workload than a pipeline composition (|). It would be nice to mention xargs -P in your series of posts on multicore programming.

    Sometimes parallel mapping of a list of data units is not trivial due to the complex nature of the list itself. For example, in GNU make a list of jobs is computed lazily in the runtime. That is why it has a special-purpose -j option for running a list of jobs in parallel.

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  3. Check out

    Computer Scientists Created the Parallel Programming Crisis

    ... problem with the Turing Computing Model: timing is not an inherent part of the model ...

  4. For a Windows perspective on this issue, refer to "Windows Parallelism, Fast File Searching, and Speculative Processing" By Johnson M. Hart