PM Programming Language

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...

Conference presentation on PM at European Geosciences Union General Assembly, Vienna, April 2016. Slides - PDF You may note some small changes in the language from previous posts - I will update on these in a new post soon.

In common with other aspects of PM design, the type system is designed to facilitate the use of flexible programming constructs, such as polymorphism and dynamic dispatch, while emphasising the generation of fast, static, code.  In common with many other modern languages, PM also attempts to combine the safety of static typing with the expressivity of dynamically typed languages such as Python . The type of any PM expression may usually be determined by a static examination of the code. Variables take their types from an initialising expression using the ‘:=’ declaration syntax popularised by Go . a:= 100 ! Declare ‘a’ as an integer (int) variable Variables do not change type during the course of their lifetime – polymorphic programming requires the use of special values, described later. Composite values such as structures are generated using specialised expressions rather than through the invocation of type-specific creator functions: b:= struct var_descriptor...