Lambda the Ultimate

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.

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.

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.

A (brief) retrospective on transactional memory, by Joe Duffy, January 3rd, 2010. Although this is a blog post, don't expect to read it all on your lunch break...

The STM.NET incubator project was canceled May 11, 2010, after beginning public life July 27, 2009 at DevLabs. In this blog post, written 4 months prior to its cancellation, Joe Duffy discusses the practical engineering challenges around implementing Software Transactional Memory in .NET. Note: He starts off with a disclaimer that he was not engaged in the STM.NET project past its initial working group phase.

In short, Joe argues, "Throughout, it became abundantly clear that TM, much like generics, was a systemic and platform-wide technology shift. It didn’t require type theory, but the road ahead sure wasn’t going to be easy." The whole blog post deals with how many implementation challenges platform-wide support for STM would be in .NET, including what options were considered. He does not mention Maurice Herlihy's SXM library approach, but refers to Tim Harris's work several times.

There was plenty here that surprised me, especially when you compare Concurrent Haskell's STM implementation to STM.NET design decisions and interesting debates the team had. In Concurrent Haskell, issues Joe raises, like making Console.WriteLine transactional, are delegated to the type system by the very nature of the TVar monad, preventing programmers from writing such wishywashy code. To be honest, this is why I didn't understand what Joe meant by "it didn't require type theory" gambit, since some of the design concerns are mediated in Concurrent Haskell via type theory. On the other hand, based on the pragmatics Joe discusses, and the platform-wide integration with the CLR they were shooting for, reminds me of The Transactional Memory / Garbage Collection Analogy. Joe also wrote a briefer follow-up post, More thoughts on transactional memory, where he talks more about Barbara Liskov's Argus.

Many a people have looked at Programming Lanugages through the Sapir-Whorf lens so it's not uncommon to find people making PL claims using that hypothesis. Also not surprisingly, the topic keeps re-appearing here on LtU.

This week's NY Times magazine has an article titled Does Your Language Shape How You Think? by Guy Deutscher which starts as a retrospective on Whorf but then goes into what new research has shown.

Some 50 years ago, the renowned linguist Roman Jakobson pointed out a crucial fact about differences between languages in a pithy maxim: “Languages differ essentially in what they must convey and not in what they may convey.” This maxim offers us the key to unlocking the real force of the mother tongue: if different languages influence our minds in different ways, this is not because of what our language allows us to think but rather because of what it habitually obliges us to think about.

...

When your language routinely obliges you to specify certain types of information, it forces you to be attentive to certain details in the world and to certain aspects of experience that speakers of other languages may not be required to think about all the time. And since such habits of speech are cultivated from the earliest age, it is only natural that they can settle into habits of mind that go beyond language itself, affecting your experiences, perceptions, associations, feelings, memories and orientation in the world.

Microsoft employee Bart De Smet, who has a widely trafficked blog, has been writing a lot in the past few months about a new library being designed by his group at Microsoft. Here is a whole truckload of blogpost links, in chronological order, which appears to be how Bart intended folks to read it:

Dec 26, 2009: More LINQ with System.Interactive – The Ultimate Imperative
Dec 27, 2009: More LINQ with System.Interactive – Exceptional Exception Handling
Dec 28, 2009: More LINQ with System.Interactive – Sequences under construction
Dec 29, 2009: More LINQ with System.Interactive – Exploiting the code = data relationship
Dec 30, 2009: More LINQ with System.Interactive – More combinators for your Swiss Army Knife
Jan 01, 2010: The Essence of LINQ – MinLINQ
Jan 07, 2010: More LINQ with System.Interactive – Functional fun and taming side-effects

I don't usually read blogs, but I thought this was a pretty cogent series of posts. Also, judging by how interested LtU and the surrounding blogosphere community was from Erik Meijer's presentation on the Rx framework at the JVM Language Summit 2009, I figured people would like this as well.

Leo Meyerovich recently started a thread on LtU asking about Historical or sociological studies of programming language evolution?. I've been meaning to post a paper on this topic to LtU for awhile now, but simply cherrypicking for the opportune time to fit it into forum discussion. With Leo's question at hand, I give you an interesting paper that models language evolution, by artificial intelligence researcher Luc Steels. Steels has spent over 10 years researching this area, and his recent paper, The Recruitment Theory of Language Origins, summarizes one of his models for dealing with language evolution:

The recruitment theory of language origins argues that language users recruit and try out different strategies for solving the task of communication and retain those that maximise communicative success and cognitive economy. Each strategy requires specific cognitive neural mechanisms, which in themselves serve a wide range of purposes and therefore may have evolved or could be learned independently of language. The application of a strategy has an impact on the properties of the emergent language and this fixates the use of the strategy in the population. Although neurological evidence can be used to show that certain cognitive neural mechanisms are common to linguistic and non-linguistic tasks, this only shows that recruitment has happened, not why. To show the latter, we need models demonstrating that the recruitment of a particular strategy and hence the mechanisms to carry out this strategy lead to a better communication system. This paper gives concrete examples how such models can be built and shows the kinds of results that can be expected from them.

While trying to find marshall's claim that Alberto Mendelzon says the universal relation is an idea re-invented once every 3 years (and later finding a quote by Jeffrey Ullman that the universal relation is re-invented 3 times a year), I stumbled across a very provocative rant by a researcher/practitioner: Why Normalization Failed to Become the Ultimate Guide for Database Designers? by Martin Fotache. It shares an interesting wealth of experience and knowledge about logical design. The author is obviously well-read and unlike usual debates I've seen about this topic, presents the argument thoroughly and comprehensively.

The abstract is:

With an impressive theoretical foundation, normalization was supposed to bring rigor and relevance into such a slippery domain as database design is. Almost every database textbook treats normalization in a certain extent, usually suggesting that the topic is so clear and consolidated that it does not deserve deeper discussions. But the reality is completely different. After more than three decades, normalization not only has lost much of its interest in the research papers, but also is still looking for practitioners to apply it effectively. Despite the vast amount of database literature, comprehensive books illustrating the application of normalization to effective real-world applications are still waited. This paper reflects the point of view of an Information Systems academic who incidentally has been for almost twenty years a practitioner in developing database applications. It outlines the main weaknesses of normalization and offers some explanations about the failure of a generous framework in becoming the so much needed universal guide for database designers. Practitioners might be interested in finding out (or confirming) some of the normalization misformulations, misinterpretations, inconsistencies and fallacies. Theorists could find useful the presentation of some issues where the normalization theory was proved to be inadequate, not relevant, or source of confusion.

The body of the paper presents an explanation for why practitioners have rejected normalization. The author also shares his opinion on potentially underexplored ideas as well, drawing from an obviously well-researched depth of knowledge. In recent years, some researchers, such as Microsoft's Pat Helland, have even said Normalization is for sissies (only to further this with later formal publications such as advocating we should be Building on Quicksand). Yet, the PLT community is pushing for the exact opposite. Language theory is firmly rooted in formal grammars and proven correct 'tricks' for manipulating and using those formal grammars; it does no good to define a language if it does not have mathematical properties ensuring relaibility and repeatability of results. This represents and defines real tension between systems theory and PLT.

I realize this paper focuses on methodologies for creating model primitives, comparing mathematical frameworks to frameworks guided by intuition and then mapped to mathematical notions (relations in the relational model), and some may not see it as PLT. Others, such as Date, closely relate understanding of primitives to PLT: Date claims the SQL language is to blame and have gone to the lengths of creating a teaching language, Tutorial D, to teach relational theory. In my experience, nothing seems to effect lines of code in an enterprise system more than schema design, both in the data layer and logic layer, and often an inverse relationship exists between the two; hence the use of object-relational mapping layers to consolidate inevitable problems where there will be The Many Forms of a Single Fact (Kent, 1988). Mapping stabilizes the problem domain by labeling correspondances between all the possible unique structures. I refer to this among friends and coworkers as the N+1 Schema Problem, as there is generally 1 schema thought to be canonical, either extensionally or intensionally, and N other versions of that schema.

Question: Should interactive programming languages aid practitioners in reasoning about their bad data models, (hand waving) perhaps by modeling each unique structure and explaining how they relate? I could see several reasons why that would be a bad idea, but as the above paper suggests, math is not always the best indicator of what practitioners will adopt. It many ways this seems to be the spirit of the idea behind such work as Stephen Kell's interest in approaching modularity by supporting evolutionary compatibility between APIs (source texts) and ABIs (binaries), as covered in his Onward! paper, The Mythical Matched Modules: Overcoming the Tyranny of Inflexible Software Construction. Similar ideas have been in middleware systems for years and are known as wrapper architecures (e.g., Don’t Scrap It, Wrap It!), but haven't seen much PLT interest that I'm aware of; "middleware" might as well be a synonym for Kell's "integration domains" concept.

The gaming studio Electronic Arts maintains their own version of the Standard Template Library. Despite the fact this is old news, I checked the LtU Archives and the new site, and there is no mention of EASTL anywhere. There are quite a few good blog posts about EASTL on the Internet, as well as the the following paper, EASTL -- Electronic Arts Standard Template Library by Paul Pedriana:

Gaming platforms and game designs place requirements on game software which differ from requirements of other platforms. Most significantly, game software requires large amounts of memory but has a limited amount to work with. Gaming software is also faced with other limitations such as weaker processor caches, weaker CPUs, and non-default memory alignment requirements. A result of this is that game software needs to be careful with its use of memory and the CPU. The C++ standard library's containers, iterators, and algorithms are potentially useful for a variety of game programming needs. However, weaknesses and omissions of the standard library prevent it from being ideal for high performance game software. Foremost among these weaknesses is the allocator model. An extended and partially redesigned replacement (EASTL) for the C++ standard library was implemented at Electronic Arts in order to resolve these weaknesses in a portable and consistent way. This paper describes game software development issues, perceived weaknesses of the current C++ standard, and the design of EASTL as a partial solution for these weaknesses.

This paper is a good introduction to a unique set of requirements video game development studios face, and compliments Manuel Simoni's recent story about The AI Systems of Left 4 Dead. This paper could be a useful inroad to those seeking to apply newer object-functional programming languages and ideas to game development.

Back to the Future: Lisp as a Base for a Statistical Computing System by Ross Ihaka and Duncan Temple Lang, and the accompanying slides.

This paper was previously discussed on comp.lang.lisp, but apparently not covered on LtU before.

The application of cutting-edge statistical methodology is limited by the capabilities of the systems in which it is implemented. In particular, the limitations of R mean that applications developed there do not scale to the larger problems of interest in practice. We identify some of the limitations of the computational model of the R language that reduces its effectiveness for dealing with large data efficiently in the modern era.

We propose developing an R-like language on top of a Lisp-based engine for statistical computing that provides a paradigm for modern challenges and which leverages the work of a wider community. At its simplest, this provides a convenient, high-level language with support for compiling code to machine instructions for very significant improvements in computational performance. But we also propose to provide a framework which supports more computationally intensive approaches for dealing with large datasets and position ourselves for dealing with future directions in high-performance computing.

We discuss some of the trade-offs and describe our efforts to realizing this approach. More abstractly, we feel that it is important that our community explore more ambitious, experimental and risky research to explore computational innovation for modern data analyses.

Foot note:
Ross Ihaka co-developed the R statistical programming language with Robert Gentleman. For those unaware, R is effectively an open source implementation of S-PLUS, which in turn was based on S. R is sort of the lingua franca of statistics, and you can usually find R code provided in the back of several Springer Verlag monographs.

Duncan Temple Lang is a core developer of R and has worked on the core engine for TIBCO's S-PLUS.

Thanks to LtU user bashyal for providing the links.