Advanced Topics in Types and Programming Languages by Benjamin C. Pierce

By Benjamin C. Pierce

The research of variety structures for programming languages now touches many components of computing device technological know-how, from language layout and implementation to software program engineering, community safeguard, databases, and research of concurrent and disbursed platforms. This ebook deals obtainable introductions to key principles within the box, with contributions via specialists on every one topic.

The subject matters lined comprise designated variety analyses, which expand uncomplicated sort structures to offer them a greater grip at the run time habit of structures; variety platforms for low-level languages; functions of sorts to reasoning approximately machine courses; style concept as a framework for the layout of refined module structures; and complex innovations in ML-style sort inference.

Advanced themes in varieties and Programming Languages builds on Benjamin Pierce's Types and Programming Languages (MIT Press, 2002); lots of the chapters might be obtainable to readers accustomed to simple notations and strategies of operational semantics and sort systems—the fabric lined within the first 1/2 the sooner book.

Advanced issues in kinds and Programming Languages can be utilized within the lecture room and as a source for execs. such a lot chapters contain routines, ranging in trouble from speedy comprehension tests to tough extensions, many with recommendations.

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Extra resources for Advanced Topics in Types and Programming Languages

Example text

One of the main applications of these new type systems was to control effects and enable in-place update of arrays in pure functional languages. Lafont (1988) was the one of the first to study programming languages with linear types, developing a linear abstract machine. He was soon followed by many other researchers, including Baker (1992) who informally showed how to compile Lisp into a linear assembly language in which all allocation, deallocation and pointer manipulation is completely explicit, yet safe.

For example, here is how we map an unrestricted function across a linear list. Remember, multi-argument functions are abbreviations for functions that accept linear pairs as arguments. fun nil(_:unit) : T2 llist = roll (lin inl ()) fun cons(hd:T2 , tl:T2 llist) : T2 llist = roll (lin inr (lin )) fun map(f:T1 →T2 , xs:T1 llist) : T2 llist = case unroll xs ( inl _ ⇒ nil() | inr xs ⇒ split xs as hd,tl in cons(f hd,map lin )) In this implementation of map, we can observe that on each iteration of the loop, it is possible to reuse the space deallocated by split or case operations for the allocation operations that follow in the body of the function (inside the calls to nil and cons).

14 Exercise [ , ]: Prove progress and preservation using TAPL, Chapters 9 and 13, as an approximate guide. ✷ (S;t) then (S;t) → (S ;t ) or t is a value. 3 Extensions and Variations Most features found in modern programming languages can be defined to interoperate successfully with linear type systems, although some are trickier than others. In this section, we will consider a variety of practical extensions to our simple linear lambda calculus. Sums and Recursive Types Complex data structures, such as the recursive data types found in ML-like languages, pose little problem for linear languages.

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