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‘Tis the season

… to give an end-of-year update of what is happening with the PM language. It is a long time now since I wrote a PM blog. However, behind the scenes there has been a lot of work done on the guts of the compiler with new inference and optimisation passes added and others taken apart, polished, and put back together again. New language features have also been implemented, including methods, improved object lifetime management and, most importantly, a radically simplified approach to coding stencils. The handling of stencils was one aspect of PM-0.4 with which I was least happy. Since these are a key requirement for nearly all numerical modelling, I have long been looking for a way to make their coding as straightforward as possible. However, this has been one of the greatest challenges in terms of implementation, since the amount of code restructuring need to make a stencil operate efficiently (separating out halo computation, overlapping computation and halo exchange, tiling and the in...
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Compile time, run time, coffee time

[ Please note that in the following discussion I will be using PM 0.5 notation, which is slightly different to PM 0.4 and also in some flux as PM 0.5 is still in development. ]   Many programming languages (and most newly developed ones) include some form of compile-time programming, ranging from simple preprocessors ( #define , #if in C) to fully blown macro systems capable of re-writing the abstract syntax tree during compilation (Rust, Julia, etc .). In line with its philosophy of keeping things as simple as possible, but not simpler, PM takes a middle road with compile-time programming supported primarily through the type system. There is nothing too radical here – this is not an area where the language aims to break new ground.  The PM approach centres around the use of compile-time types. Many languages use special types to denote literal values and PM follows this trend. Literal integers, reals, strings and Booleans each have their own types: literal(int) , litera...
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All change .. not quite

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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WYSIWYG

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 ...
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Data in, data out

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...
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