Lambda the Ultimate

I have just learned that Dennis Ritchie (1941-2011) has passed away. His contributions changed the computing world. As everyone here knows, dmr developed C, and with Brian Kernighan co-authored K&R, a book that served many of us in school and in our professional lives and remains a classic text in the field, if only for its style and elegance. He was also one of the central figures behind UNIX. Major programming languages, notably C++ and Java, are descendants of Ritchie's work; many other programming languages in use today show traces of his influences.

Update

Bjarne Stroustrup puts the C revolution in perspective: They said it couldn’t be done, and he did it.

Steve Jobs (1955 - 2011) had a profound influence on the computing world. As others discuss his many contributions and accomplishments, I think it is appropriate that we discuss how these affected programming, and consequently programming languages. Bringing to life some of the ideas of the Mother of All Demos, Jobs had a hand in making event loops standard programming fare, and was there when Apple and NeXT pushed languages such as Objective-C and Dylan and various software frameworks, and decided to cease supporting others. Some of these were more successful than others, and I am sure members have views on their technical merits. This thread is for discussing Jobs -- from the perspective of programming languages and technologies.

Update:

Eric Schmidt on Jobs and OOP

Stephen Wolfram on Jobs and Mathematica

The iPhone mandate decision

Programming and Scaling, a one-hour lecture by Alan Kay at his finest (and that's saying something!)

Some of my favorite quotes:

• "The biggest problem we have as human beings is that we confuse our beliefs with reality."
• "We could imagine taking the internet as a model for doing software modules. Why don't people do it?" (~00:17)
• "One of the mistakes that we made years ago is that we made objects too small." (~00:26)
• "Knowledge in many cases trumps IQ. [Henry] Ford was powerful because Isaac Newton changed the way we think." (~00:28)
• "Knowledge is silver. Outlook is gold. IQ is a lead weight." (~00:30)
• "Whatever we [in computing] do is more like what the Egyptians did. Building pyramids, piling things on top of each other."
• "The ability to make science and engineering harmonize with each other - there's no greater music." (~00:47)

And there are some other nice ideas in there: "Model-T-Shirt Programming" - software the definition of which fits on a T-shirt. And imagining source code sizes in terms of books: 20,000 LOC = a 400-page book. A million LOC = a stack of books one meter high. (Windows Vista: a 140m stack of books.)

Note: this a Flash video, other formats are available.

Rob Pike's talk about the motivation for Go is rather fun, but doesn't really break new ground. Most of what he says have been said here many times, from the critic of the verbosity of C++ and Java to the skepticism about dynamic typing. Some small details are perhaps worth arguing with, but in general Pike is one of the good guys -- it's all motherhood and apple pie.

So why mention this at all (especially since it is not even breaking news)? Well, what caught my attention was the brief reconstruction of history the Pike presents. While he is perfectly honest about not being interested in history, and merely giving his personal impressions, the description is typical. What bugs me, particularly given the context of this talk, is that the history it totally sanitized. It's the "history of ideas" in the bad sense of the term -- nothing about interests (commercial and otherwise), incentives, marketing, social power, path dependence, any thing. Since we had a few discussions recently about historiography of the field, I thought I'd bring this up (the point is not to target Pike's talk in particular).

Now, when you think about Java, for example, it is very clear that the language didn't simply take over because of the reasons Pike marshals. Adoption is itself a process, and one that is worth thinking about. More to the point, I think, is that Java was (a) energetically marketed; and (b) was essentially a commercial venture, aimed at furthering the interests of a company (that is no longer with us...) Somehow I think all this is directly relevant to Go. But of course, it is hard to see Go gaining the success of Java.

All this is to say that history is not just "we had a language that did x well, but not y, so we came up with a new language, that did y but z only marginally, so now we are building Go (which compiles real fast, you know) etc. etc."

Or put differently, those who do not know history are doomed to repeat it (or some variation of this cliche that is more authentic). Or does this not hold when it comes to PLs?

Roberto Ierusalimschy, Luiz Henrique de Figueiredo, and Waldemar Celes, "Passing a Language through the Eye of a Needle: How the embeddability of Lua impacted its design", ACM Queue vol. 9, no. 5, May 2011.

A key feature of a scripting language is its ability to integrate with a system language. This integration takes two main forms: extending and embedding. In the first form, you extend the scripting language with libraries and functions written in the system language and write your main program in the scripting language. In the second form, you embed the scripting language in a host program (written in the system language) so that the host can run scripts and call functions defined in the scripts; the main program is the host program.
...
In this article we discuss how embeddability can impact the design of a language, and in particular how it impacted the design of Lua from day one. Lua is a scripting language with a particularly strong emphasis on embeddability. It has been embedded in a wide range of applications and is a leading language for scripting games.

An interesting discussion of some of the considerations that go into supporting embeddability. The design of a language clearly has an influence over the API it supports, but conversely the design of an API can have a lot of influence over the design of the language.

Memory Models: A Case for Rethinking Parallel Languages and Hardware by Sarita V. Adve and Hans-J. Boehm

This is a pre-print of the actual version.

The era of parallel computing for the masses is here, but writing correct parallel programs remains far more difficult than writing sequential programs. Aside from a few domains, most parallel programs are written using a shared-memory approach. The memory model, which specifies the meaning of shared variables, is at the heart of this programming model. Unfortunately, it has involved a tradeoff between programmability and performance, and has arguably been one of the most challenging and contentious areas in both hardware architecture and programming language specification. Recent broad community-scale efforts have finally led to a convergence in this debate, with popular languages such as Java and C++ and most hardware vendors publishing compatible memory model specifications. Although this convergence is a dramatic improvement, it has exposed fundamental shortcomings in current popular languages and systems that prevent achieving the vision of structured and safe parallel programming.

This paper discusses the path to the above convergence, the hard lessons learned, and their implications. A cornerstone of this convergence has been the view that the memory model should be a contract between the programmer and the system - if the programmer writes disciplined (data-race-free) programs, the system will provide high programmability (sequential consistency) and performance. We discuss why this view is the best we can do with current popular languages, and why it is inadequate moving forward. We then discuss research directions that eliminate much of the concern about the memory model, but require rethinking popular parallel languages and hardware. In particular, we argue that parallel languages should not only promote high-level disciplined models, but they should also enforce the discipline. Further, for scalable and efficient performance, hardware should be co-designed to take advantage of and support such disciplined models. The inadequacies of the state-of-the-art and the research agenda we outline have deep implications for the practice, research, and teaching of many computer science sub-disciplines, spanning theory, software, and hardware.

Oleg Kiselyov wrote a mail to haskell-cafe today titled, The IO Monad is 45 years old. I thought LtU readers might like this.

Pure and Declarative Syntax Definition: Paradise Lost and Regained by Lennart C. L. Kats, Eelco Visser, Guido Wachsmuth from Delft

Syntax definitions are pervasive in modern software systems, and serve as the basis for language processing tools like parsers and compilers. Mainstream parser generators pose restrictions on syntax definitions that follow from their implementation algorithm. They hamper evolution, maintainability, and compositionality of syntax definitions. The pureness and declarativity of syntax definitions is lost. We analyze how these problems arise for different aspects of syntax definitions, discuss their consequences for language engineers, and show how the pure and declarative nature of syntax definitions can be regained.

I haven't compared this version with the Onward 2010 version, but they look essentially the same. It seems timely to post this paper, considering the other recent story Yacc is dead. There is not a whole lot to argue against in this paper, since we all "know" the other approaches aren't as elegant and only resort to them for specific reasons such as efficiency. Yet, this is the first paper I know of that tries to state the argument to software engineers.

For example, the Dragon Book, in every single edition, effectively brushes these topics aside. In particular, the Dragon Book does not even mention scannerless parsing as a technique, and instead only explains the "advantages" of using a scanner. Unfortunately, the authors of this paper don't consider other design proposals, either, such as Van Wyk's context-aware scanners from GPCE 2007. It is examples like these that made me wish the paper was a bit more robust in its analysis; the examples seem focused on the author's previous work.

If you are not familiar with the author's previous work in this area, the paper covers it in the references. It includes Martin Bravenboer's work on modular Eclipse IDE support for AspectJ.

The role the ideas of Principia Mathematica played in type theory in programming languages is often alluded to in our discussions, making this contribution to a meeting celebrating the hundredth anniversary of Whitehead-and-Russell's opus provocative.

To get your juices going here is a quote from page 3:

...I will discuss later our efforts at Cornell to create one such type theory, Computational Type Theory (CTT), very closely related to two others, the Calculus of Inductive Constructions (CIC) implemented in the Coq prover and widely used, and Intuitionistic Type Theory (ITT) implemented in the Alf and Agda provers. All three of these efforts, but especially CTT and ITT, were strongly influenced by Principia and the work of Bishop presented in his book Foundations of Constructive Analysis.

Peter J. Denning and Jack B. Dennis, The Resurgence of Parallelism, Communications of the ACM, Vol. 53 No. 6, Pages 30-32, 10.1145/1743546.1743560

"Multi-core chips are a new paradigm!" "We are entering the age of parallelism!" These are today's faddish rallying cries for new lines of research and commercial development. ... The parallel architecture research of the 1960s and 1970s solved many problems that are being encountered today. Our objective in this column is to recall the most important of these results and urge their resurrection.

A brief but timely reminder that we should avoid reinventing the wheel. Denning and Dennis give a nice capsule summary of the history of parallel computing research, and highlight some of the key ideas that came out of earlier research on parallel computing. This isn't a technically deep article. But it gives a quick overview of the field, and tries to identify some of the things that actually are research challenges rather than problems for which the solutions have seemingly been forgotten.