Wed, 19 Apr 2017 23:38:56 +0000
Type Systems as Macros, by Stephen Chang, Alex Knauth, Ben Greenman:
We present TURNSTILE, a metalanguage for creating typed embedded languages. To implement the type system, programmers write type checking rules resembling traditional judgment syntax. To implement the semantics, they incorporate elaborations into these rules. TURNSTILE critically depends on the idea of linguistic reuse. It exploits a macro system in a novel way to simultaneously type check and rewrite a surface program into a target language. Reusing a macro system also yields modular implementations whose rules may be mixed and matched to create other languages. Combined with typical compiler and runtime reuse, TURNSTILE produces performant typed embedded languages with little effort.
This looks pretty awesome considering it's not limited to simple typed languages, but extends all the way to System F and F-omega! Even better, they can reuse previous type systems to define new ones, thereby reducing the effort to implement more expressive type systems. All code and further details available here, and here's a blog post where Ben Greenman further discusses the related "type tailoring", and of course, these are both directly related to Active Libraries.
Taken to its extreme, why not have an assembler with a powerful macro system of this sort as your host language, and every high-level language would be built on this. I'm not sure if this approach would extend that far, but it's an interesting idea. You'd need a cpp-like highly portable macro tool, and porting to a new platform consists of writing architecture-specific macros for some core language, like System F.
This work may also conceptually dovetail with another thread discussing fexprs and compilation.
Sat, 01 Apr 2017 18:53:58 +0000
Sat, 25 Mar 2017 15:45:23 +0000
kdb+ is a real-time time series database, known in the financial services universe as the fastest tick database on the market. It was first conceived by Arthur Whitney at Morgan Stanley as a prototype, and over the last 35+ years has grown to add many features. The database makes such aggressive usage of mmap() POSIX function for mapping file chunks into main memory, to the point where it has exposed issues with the implementation of mmap itself.
Recently, the company now behind kdb+ has also built Kx for DAAS (Data-as-a-Service), which is basically a cloud-based, massively clustered version of kdb+ that deals with the curious oddity that kdb+ is effectively entirely singly threaded. For those interested in reading more about kdb+'s unique cloud architecture (as compared to "big data" solutions like Hadoop), you can read the following whitepapers as suggestive guidelines for how the q community thinks about truly "big data" several orders of magnitude faster and larger than most Hadoop data sets:
While I don't suggest these papers are the blueprint for copying/mimicking the DAAS product, it does help the LtU reader imagine a "different world" of data processing than the often cited Map/Reduce paper and other more mainstream approaches. What is particularly striking is how tiny q.exe (the program that runs kdb+ and provides a CLI for q scripting) is. Language researchers are looking at provably correct C compilers, and it is not a huge leap to think about the world soon seeing provably correct real-time time series databases using kdb+ as an inspiration.
Another curiosity, relevant to us here at LtU, is that kdb+ has its own programming language, q. q is a variant of APL with a special library for statistics. Most "big data" solutions don't have native implementations for weighted average, which is a fairly important and frequently used function in quantitative finance, useful for computing volume weighted average price (VWAP) as well as tilt and weighted spread. q is itself implemented in another language, k. The whole language of each is just a couple lines of (terse) code.
Thu, 12 Jan 2017 01:09:41 +0000
I previously wrote about a brand of research by Guy Blelloch on the Cost semantics for functional languages, which let us make precise claim about the complexity of functional programs without leaving their usual and comfortable programming models (beta-reduction).
While the complexity behavior of weak reduction strategies, such as call-by-value and call-by-name, is by now relatively well-understood, the lambda-calculus has a much richer range of reduction strategies, in particular those that can reduce under lambda-abstractions, whose complexity behavior is sensibly more subtle and was, until recently, not very well understood. (This has become a practical concern since the rise in usage of proof assistants that must implement reduction under binders and are very concerned about the complexity of their reduction strategy, which consumes a lot of time during type/proof-checking.)
Beniamino Accatoli, who has been co-authoring a lot of work in that area, recently published on arXiv a new paper that has survey quality, and is a good introduction to this area of work and other pointers from the literature.
Beniamino Accatoli, 2017
The lambda-calculus is a peculiar computational model whose definition does not come with a notion of machine. Unsurprisingly, implementations of the lambda-calculus have been studied for decades. Abstract machines are implementations schema for fixed evaluation strategies that are a compromise between theory and practice: they are concrete enough to provide a notion of machine and abstract enough to avoid the many intricacies of actual implementations. There is an extensive literature about abstract machines for the lambda-calculus, and yet -- quite mysteriously -- the efficiency of these machines with respect to the strategy that they implement has almost never been studied.
This paper provides an unusual introduction to abstract machines, based on the complexity of their overhead with respect to the length of the implemented strategies. It is conceived to be a tutorial, focusing on the case study of implementing the weak head (call-by-name) strategy, and yet it is an original re-elaboration of known results. Moreover, some of the observation contained here never appeared in print before.
Fri, 16 Dec 2016 08:25:51 +0000
Dave Herman is the voice of the oppressed: syntax is important, contrary to what you have been told!
To illustrate he discusses what he calls Stroustrup's Rule:
- For new features, people insist on LOUD explicit syntax.
- For established features, people want terse notation.
Thu, 15 Dec 2016 21:20:54 +0000Monads and algebraic effects are general concepts that give a definition of what a "side-effect" can be: an instance of monad, or an instance of algebraic effect, is a specific realization of a side-effect. While most programming languages provide a fixed family of built-in side-effects, monads or algebraic effects give a structured way to introduce a new notion of effect as a library. A recent avenue of programming language research is how to locally define several realizations of the same effect interface/signature. There may be several valid notions of "state" or "non-determinism" or "probabilistic choice", and different parts of a program may not require the same realization of those -- a typical use-case would be mocking an interaction with the outside world, for example. Can we let users locally define a new interpretation of an effect, or write code that is generic over the specific interpretation? There are several existing approaches, such as monad transformer stacks, free monads interpreters, monad reification and, lately, effect handlers, as proposed in the programming language Eff. Frank, presented in the publication below, is a new language with user-defined effects that makes effect handling a natural part of basic functional programming, instead of a separate, advanced feature. It is a significant advance in language design, simplifying effectful programming. Functions, called operators, do not just start computing a result from the value of their arguments, they interact with the computation of those arguments by having the opportunity to handle any side-effects arising during their evaluation. It feels like a new programming style, a new calling convention that blends call-by-value and effect handling -- Sam Lindley suggested the name call-by-handling. Frank also proposes a new type-and-effect system that corresponds to this new programming style. Operators handle some of the effects raised by their arguments, and silently forward the rest to the computation context; their argument types indicate which effects they handle. In other words, the static information carried by an argument types is the delta/increment between the effects permitted by the ambient computation and the effects of evaluating this argument. Frank calls this an adjustment over the ambient ability. This delta/increment style results in lightweight types for operators that can be used in different contexts (a form of effect polymorphism) without explicit quantification over effect variables. This design takes part in a research conversation on how to make type-and-effect systems usable, which is the major roadblock for their wider adoption -- Koka and Links are also promising in that regard, and had to explore elaborate conventions to elide their polymorphic variables. Another important part of Frank's type-system design is the explicit separation between values that are and computations that do. Theoretical works have long made this distinction (for example with Call-By-Push-Value), but programmers are offered the dichotomy of either having only effectful expressions or expressing all computations as values (Haskell's indirect style). Frank puts that distinction in the hands of the user -- this is different from distinguishing pure from impure computations, as done in F* or WhyML. If you wish to play with the language, a prototype implementation is available. Do Be Do Be Do (arXiv) Sam Lindley, Conor McBride, Craig McLaughlin 2017 We explore the design and implementation of Frank, a strict functional programming language with a bidirectional effect type system designed from the ground up around a novel variant of Plotkin and Pretnar’s effect handler abstraction. Effect handlers provide an abstraction for modular effectful programming: a handler acts as an interpreter for a collection of commands whose interfaces are statically tracked by the type system. Howe[...]
Fri, 09 Dec 2016 21:37:50 +0000Contextual Isomorphisms Paul Blain Levy 2017 What is the right notion of "isomorphism" between types, in a simple type theory? The traditional answer is: a pair of terms that are inverse, up to a specified congruence. We firstly argue that, in the presence of effects, this answer is too liberal and needs to be restricted, using Führmann’s notion of thunkability in the case of value types (as in call-by-value), or using Munch-Maccagnoni’s notion of linearity in the case of computation types (as in call-by-name). Yet that leaves us with different notions of isomorphism for different kinds of type. This situation is resolved by means of a new notion of “contextual” isomorphism (or morphism), analogous at the level of types to contextual equivalence of terms. A contextual morphism is a way of replacing one type with the other wherever it may occur in a judgement, in a way that is preserved by the action of any term with holes. For types of pure λ-calculus, we show that a contextual morphism corresponds to a traditional isomorphism. For value types, a contextual morphism corresponds to a thunkable isomorphism, and for computation types, to a linear isomorphism. This paper is based on a very simple idea that everyone familiar with type-systems can enjoy. It then applies it in a technical setting in which it brings a useful contribution. I suspect that many readers will find that second part too technical, but they may still enjoy the idea itself. To facilitate this, I will rephrase the abstract above in a way that I hope makes it accessible to a larger audience. The problem that the paper solves is: how do we know what it means for two types to be equivalent? For many languages they are reasonable definitions of equivalence (such that: there exists a bijection between these two types in the language), but for more advanced languages these definitions break down. For example, in presence of hidden mutable state, one can build a pair of functions from the one-element type unit to the two-element type bool and back that are the identity when composed together -- the usual definition of bijection -- while these two types should probably not be considered "the same". Those two functions share some hidden state, so they "cheat". Other, more complex notions of type equivalence have been given in the literature, but how do we know whether they are the "right" notions, or whether they may disappoint us in the same way? To define what it means for two program fragments to be equivalent, we have a "gold standard", which is contextual equivalence: two program fragments are contextually equivalent if we can replace one for the other in any complete program without changing its behavior. This is simple to state, it is usually clear how to instantiate this definition for a new system, and it gives you a satisfying notion of equivalent. It may not be the most convenient one to work with, so people define others, more specific notions of equivalence (typically beta-eta-equivalence or logical relations); it is fine if they are more sophisticated, and their definiton harder to justify or understand, because they can always be compared to this simple definition to gain confidence. The simple idea in the paper above is to use this exact same trick to define what it means for two types to be equivalent. Naively, one could say that two types are equivalent if, in any well-typed program, one can replace some occurrences of the first type by occurrences of the second type, all other things being unchanged. This does not quite work, as changing the types that appear in a program without changing its terms would create ill-typed terms. So instead, the paper proposes that two types are equivalent when we are told how to transform any program using the first type into a program using the second type, in a way that is bijective (invertible) and [...]
Fri, 25 Nov 2016 15:25:27 +0000
By far not the best presentation of Kay's ideas but surely a must watch for fans. Otherwise, skip until the last third of the interview which might add to what most people here already know.
It is interesting that in this talk Kay rather explicitly talks about programming languages as abstraction layers. He also mentions some specifics that may not be as well known as others, yet played a role in his trajectory, such as META.
I fully sympathize with his irritation with the lack of attention to and appreciation of fundamental concepts and theoretical frameworks in CS. On the other hand, I find his allusions to biology unconvincing.
An oh, he is certainly right about Minsky's book (my first introduction to theoretical CS) and in his deep appreciation of John McCarthy.
Wed, 23 Nov 2016 16:54:15 +0000
I am very enthusiastic about the following paper: it brings new ideas and solves a problem that I did not expect to be solvable, namely usable type inference when both polymorphism and subtyping are implicit. (By "usable" here I mean that the inferred types are both compact and principal, while previous work generally had only one of those properties.)
Polymorphism, Subtyping, and Type Inference in MLsub
Stephen Dolan and Alan Mycroft
We present a type system combining subtyping and ML-style parametric polymorphism. Unlike previous work, our system supports type inference and has compact principal types. We demonstrate this system in the minimal language MLsub, which types a strict superset of core ML programs.
This is made possible by keeping a strict separation between the types used to describe inputs and those used to describe outputs, and extending the classical unification algorithm to handle subtyping constraints between these input and output types. Principal types are kept compact by type simplification, which exploits deep connections between subtyping and the algebra of regular languages. An implementation is available online.
The paper is full of interesting ideas. For example, one idea is that adding type variables to the base grammar of types -- instead of defining them by their substitution -- forces us to look at our type systems in ways that are more open to extension with new features. I would recommend looking at this paper even if you are interested in ML and type inference, but not subtyping, or in polymorphism and subtyping, but not type inference, or in subtyping and type inference, but not functional languages.
(If you are looking for interesting work on inference of polymorphism and subtyping in object-oriented languages, I would recommend Getting F-Bounded Polymorphism into Shape by Ben Greenman, Fabian Muehlboeck and Ross Tate, 2014.)
Wed, 23 Nov 2016 16:20:16 +0000In the first decade of the twenty-first century, the Feyerabend Project organized several workshops to discuss and develop new ways to think of programming languages and computing in general. A new event in this direction is a new workshop that will take place in Brussels, in April, co-located with the new
Fri, 04 Nov 2016 14:27:32 +0000
Proving Programs Correct Using Plain Old Java Types, by Radha Jagadeesan, Alan Jeffrey, Corin Pitcher, James Riely:
Tools for constructing proofs of correctness of programs have a long history of development in the research community, but have often faced difficulty in being widely deployed in software development tools. In this paper, we demonstrate that the off-the-shelf Java type system is already powerful enough to encode non-trivial proofs of correctness using propositional Hoare preconditions and postconditions.
We illustrate the power of this method by adapting Fähndrich and Leino’s work on monotone typestates and Myers and Qi’s closely related work on object initialization. Our approach is expressive enough to address phased initialization protocols and the creation of cyclic data structures, thus allowing for the elimination of null and the special status of constructors. To our knowledge, our system is the first that is able to statically validate standard one-pass traversal algorithms for cyclic graphs, such as those that underlie object deserialization. Our proof of correctness is mechanized using the Java type system, without any extensions to the Java language.
Not a new paper, but it provides a lightweight verification technique for some program properties that you can use right now, without waiting for integrated theorem provers or SMT solvers. Properties that require only monotone typestates can be verified, ie. those that operations can only move the typestate "forwards".
In order to achieve this, they require programmers to follow a few simple rules to avoid Java's pervasive nulls. These are roughly: don't assign null explicitly, be sure to initialize all fields when constructing objects.
Thu, 22 Sep 2016 18:29:03 +0000
Automating Ad hoc Data Representation Transformations by Vlad Ureche, Aggelos Biboudis, Yannis Smaragdakis, and Martin Odersky:
To maximize run-time performance, programmers often specialize their code by hand, replacing library collections and containers by custom objects in which data is restructured for efficient access. However, changing the data representation is a tedious and error-prone process that makes it hard to test, maintain and evolve the source code.
We present an automated and composable mechanism that allows programmers to safely change the data representation in delimited scopes containing anything from expressions to entire class definitions. To achieve this, programmers define a transformation and our mechanism automatically and transparently applies it during compilation, eliminating the need to manually change the source code.
Our technique leverages the type system in order to offer correctness guarantees on the transformation and its interaction with object-oriented language features, such as dynamic dispatch, inheritance and generics.
We have embedded this technique in a Scala compiler plugin and used it in four very different transformations, ranging from improving the data layout and encoding, to
retrofitting specialization and value class status, and all the way to collection deforestation. On our benchmarks, the technique obtained speedups between 1.8x and 24.5x.
This is a realization of an idea that has been briefly discussed here on LtU a few times, whereby a program is written using high-level representations, and the user has the option to provide a lowering to a more efficient representation after the fact.
This contrasts with the typical approach of providing efficient primitives, like primitive unboxed values, and leaving it to the programmer to compose them efficiently up front.
Wed, 07 Sep 2016 08:54:58 +0000
At this year's SPLASH there will be a follow-up to last year's NOOL 2015 workshop (discussed here on LtU). Objects may not be the trendiest thing in PL circles, but I think they have a bright future. (Example: session types are essentially concurrent objects in disguise, and they are quite trendy.)
Please consider submitting! (Disclaimer: I'm one of the organisers :)
Fri, 19 Aug 2016 09:23:21 +0000
Long HN thread ensues. Many of the comments discuss the benefits/costs of basing pipes on typed objects rather than text streams. As someone who should be inclined in favor of the typed object approach I have to say that I think the text-only folks have the upper hand at the moment. Primary reason is that text as a lingua franca between programs ensures interoperability (and insurance against future changes to underlying object models) and self-documenting code. Clearly the Achilles' heel is parsing/unparsing.
As happens often, one is reminded of the discussions of DSLs and pipelines in Jon Bentley's Programming Pearls...