PM Programming Language

With the recent release of PM 0.4 and the positive reception to my PM presentation at CIUK2023 , it seems like a good time to bring back the PM blog after a long hiatus. Another good reason for its resurrection is that I feel that I now have built the basic semantics of the language into something like a coherent whole, giving me something concrete to write about. There have been a few major changes to the language since my last blog entry. The main syntactic change has been the shift from keyword-delimited control statements to curly-brackets. This is not a statement on my part as to the merits of the two approaches, I am generally agnostic in this debate which can border on the religious. It was simply that with the way that the language was developing, the keyword approach was getting cumbersome – frequently used constructs were taking up far to much space and impeding readability. PM now uses curly brackets to terminate statements and semicolons to separate (and optionally terminat...

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PM was in part inspired by ZPL , a parallel array language. One of the key goals of the ZPL project was to provide a “What You See Is What You Get” (WYSIWYG) approach to parallel communication – in particular as this related to communication overhead. This was achieved using a range of operators that explicitly invoke parallel communication on distributed arrays. For example, the @ operator moves the elements of an array by a fixed displacement while the # operator provides for more general element permutation. A programmer would know that the former operation is much less expensive than the latter. To shift a distributed array by a fixed displacement, computational nodes only need to exchange information for edge cells that have moved into the region governed by a different node. However, general permutation requires all nodes to request and then receive data from arbitrary other nodes and for this exchange to be globally synchronized. The ZPL # operator thus needs avoiding unless ...

When a program is running over multiple processors or processing cores then it is important to be able to map where data are flowing. One way in which data flows can be translated into programming language semantics is though argument passing to subroutines. In the absence of global variables, data transfer to and from a subroutine straightforwardly can be directly related to communication to and from another processor. In PM, the emphasis is on data parallelism through the for statement. for index in array do index=process(index) endfor      So what are the rules governing data flow into and out of this statement? These are primarily based around variable locality. x:= 1 for index in array do y:=func(index) index=process(x,y) endfor Here x is relatively global to the for statement and y is local to it (a variables scope only extends to the end of the statement block in which it is defined).  There are two ways t...

One of the more difficult aspects of coding stencils or other neighbourhood-based operations is working out what should happen at the edge of the grid. Parallel array languages such as ZPL have expended significant effort to provide mechanism to make coding edge effects easier. PM handles edge effects by making use of optional data types - containers that can hold either a value ( e.g. : an integer) or null . Optional types have many uses, including the representation of unbounded data structures in a pointerless language. A number of operators are defined for optional types, most usefully |.  x|y returns the value contained in x if that value is not null . Otherwise y is returned.  Binary communicating operators (x@[ neighbourhood ] or x@{ neighbourhood }) always return optionally-typed values, with off-edge cells set to null . Combined with the | operator, this makes dealing with edge-effects straightforward. The following code fragment demonstrates how PM hand...