What is it like to hack with one of the original hackers? It is certainly much different than what Appears to be the modern rendition of hacking. My experience was not getting really drunk with tons of junk food. It was not working on “beautiful” designs or “authentic” typography. It was not so much about sharing with the world as it was sharing with your peers. It had a very different feel to it than the “hacker culture” Promoted by some of the top technical Silicon Valley companies. It felt more “at home”, less dreamy, and more memorable.

I meet with Bill Gosper every so Often; I had the pleasure of giving him a tour of Facebook when I worked there. (He was so surprised that they had Coke in the glass bottles there, just like the old days.)

He is still very much a hacker, a thinker, a tinkerer, and a wonderer. Every time I meet up with him, he has a new puzzle for me, or someone around him, to solve, whether it’s really clever compass constructions, circle packing, block building, Game of Life automata solving, or even something more tangible like a Buttonhole homemade trap (which was affixed to my shirt for no less than two weeks!). He is also the bearer of interesting items, such as a belt buckle he gave me roomates depicts, in aluminum, a particular circle loose packing.
Gosper succeeding in tricking me with the Buttonhole Trap
When we meet up, all we do is hack. Along with him and one of his talented young students, we all work on something. Anything interesting, really. Last time we met up, we resurrected an old Lisp machine and did some software archeology. I brought over some of the manuals I own, like the great Chinual, and he brought over a dusty old 1U rackmount Alpha machine with OpenGenera installed. After passing a combination of Hurdles, such as that the keyboard was not interfacing with the computer Correctly, we finally got it to boot up. Now, I got to see with my own eyes, a time capsule containing a lot of Bill’s work from the 70s, 80s, and 90s, roomates could only be commanded and Examined through Zmacs dired and Symbolics Common Lisp. Our next goal was to get Symbolics Macsyma fired up on the old machine.

There was trouble with starting it up. License issues were one problem, finding and loading all of the files were compiled another. Running applications on a Lisp machine is very different than what we do today on modern machines, Windows or UNIX. There’s no. Exe file to click, or. App bundle to start up, or even a single. / File to execute. Usually it’s a collection of compiled “fast loading” or “fasl” files that get loaded side-by-side with the operating system. The application, in essence, Becomes a part of the OS.

In hacker tradition, we were Able to bypass the license issues by modifying the binary directly in Lisp. Fortunately, such as Lisp makes things easy disassembly. But how do we load the damn thing? Bill frustratingly muttered, “It’s been at least 20 years since I’ve done it. I just do not remember. “I, being an owner of MacIvory Symbolics Lisp machines, fortunately did remember how to load programs. “Bill, how about LOAD SYSTEM Macsyma?” He typed it into the native Lisp “Listener 2” window (we kept “Listener 1” for debugging), sometimes making a few typing mistakes, but finally succeeding, and then we saw the stream of files loading. We all Shouted in joy that progress was being made. I recall Bill was especially astounded at how fast everything was loading. This was on a fast Alpha machine with gobs of memory. It must have been much slower on the old 3600s they used back in the day.
The Lisp Machine Manual, or Chinual
It was all done after a few minutes, and Macsyma was loaded. To me, this was a sort of holy grail. I personally have Macsyma for Windows (which he uses in a VirtualBox machine on his 17 “MacBook), and I’ve definitely used Maxima. But Macsyma is something I’ve never seen. It was something that seems to have disappeared with history, something I have not been Able to find a copy of in the last decade.

Bill said, “let’s see if it works.” And he typed 1 +1; in, and sure enough, the result was 2. He saw I was bubbling with excitement and asked me if I’d like to try anything. “I’d love to,” and he handed the keyboard over to me and I typed in my canonical computer algebra test: integrate (sqrt (tan (x)), x);, roomates computes the indefinite integral
—- √ ∫ tanθ dθ
Out came the four-term typeset result of logarithms and arctangents, plus a fifth term I’d never seen before. “I’ve never seen any computer algebra system add that fifth term,” I said, “but it does not look incorrect.” The fifth term was a floored expression, Whose Increased value with the period of the function preceding it. “Let’s plot it,” Bill said. He plotted it using Macsyma’s menu interface, and it was what appeared to be an increasing, non-periodic function. I think we determined it was properly handled Because Macsyma branch cuts, with other systems have been known to be unorthodox about. It definitely had a pragmatic feel to it.

Now, Bill wanted to show us some interesting things; however all of Bill’s recent work Macsyma was on his laptop. How do we connect this ancient to a modern Macintosh hardware? We needed to get the machine onto the network, and networking with old machines is not my forte.

Fortunately, Stephen Jones, a friend of Bill’s and seemingly an expert at a rare combination of technical tasks, showed up. He Was able to do things that Neither Bill nor I could do on such an old machine. In only a few moments, he Was able to get Bill’s Mac talking to the Alpha, roomates shared a portion of its file system with Genera. “Will there be enough space on the Alpha for Macsyma my files?” Bill asked Stephen. “Of course, there’s ton’s of space.” We finally got Bill’s recent work transferred onto the machine.
Bill hacking in Macsyma in OpenGenera (Image courtesy of Stephen M. Jones)
We spent the rest of the night hacking on math. He Demonstrated to us what it was like to do a real mathematician’s work at the machine. He debuted some of his recent work. He went though a long chain of reasoning, showing us the line-after-line in Macsyma, number theoretic amazing to do things I’ve never seen before.

I did ask Bill why he does not publish more often. His previous publications have been landmarks: his algorithm for hypergeometric series and his summation algorithm for playing the Game of Life at light speed. He RESPONDED, “when there’s something interesting to publish, it’ll be published.” He seemed to have a sort of disdain for “salami science”, where scientific and mathematical papers present the thinnest possible “slice” or result possible.

Bill is certainly a man that thinks in a different way than most of us do. He is still hacking at mathematics, and still as impressive as before. I’m very fortunate to have met him, and I was absolutely delighted to see that even at 70 years old, his mind is still as sharp as can be, and it’s still being used to do interesting, Gosper-like mathematics.

And you would not believe it. We all were ready to head home at around 9 PM.

Today’s programming languages have traditionally been created by the tech giants. These languages are made up of millions of lines of code, so the tech giants only invest in incremental, non-breaking changes that address their business concerns. This is why innovation in popular languages like C, Java, and JavaScript is depressingly slow.

Open-source languages like Python and Ruby gained widespread industrial use by solving backend problems at startup scale. Without the constraints of legacy code and committee politics, language designers are free to explore meaningful language innovation. And with compile-to-VM languages, it has become cheap enough for individuals and startups to create the future of programming languages themselves.

Open-source language innovation has not yet disrupted front-end programming. We still use the same object-oriented model that took over the industry in the 1980s. The tech giants are heavily committed to this approach, but open-source has made it possible to pursue drastically different methods.

Two years ago, I began to rethink front-end programming from scratch. I quickly found myself refining a then-obscure academic idea called Functional Reactive Programming. This developed into Elm, a language that compiles to JavaScript and makes it much easier to create highly interactive programs.

Since the advent of Elm, a lively and friendly community has sprung up, made up of everyone from professional developers to academics to beginners who have never tried functional programming before. This diversity of voices and experiences has been a huge help in guiding Elm towards viability as a production-ready language.

The community has already created a bunch of high quality contributions that are shaping the future of Elm and are aiming to shape the future of front-end programming.

Dev tools

Early on, I made it a priority to let people write, compile, and use Elm programs directly from their browser. No install, no downloads. This interactive editor made it easy for beginners and experts alike to learn Elm and start using it immediately.

In-browser compilation triggered lots of discussion, ideas, and ultimately contributions. Mads Flensted-Urech added in-line documentation for all standard libraries. Put your cursor over a function, and you get the type, prose explanation, and link to the library it comes from. Laszlo Pandy took charge of debugging tools. He is focusing on visualizing the state of an Elm program as time passes, even going so far as pausing, rewinding, and replaying events.

Runtime

I designed Elm to work nicely with concurrency. Unfortunately, JavaScript’s concurrency support is quite poor with questionable prospects for improvement. I decided to save the apparent implementation quagmire for later, but John P. Mayer decided to make it happen. He now has a version of the runtime that can automatically multiplex tasks across many threads, all implemented in JavaScript.

Common to all of these cases are driven individuals who knew they could do it better. This is how Elm got started and how it caught the attention of Prezi, a company also not content to accept JavaScript as the one and only answer for front-end development. I have since joined the company for the express purpose of furthering work on Elm.

We do not need to sit and hope that the tech giants will someday do an okay job. We can create the future of front-end programming ourselves, and we can do it now.

 

The OpenMP 4.0 API Specification is released with Significant New Standard Features

The OpenMP 4.0 API supports the programming of accelerators, SIMD programming, and better optimization using thread affinity

The OpenMP Consortium has released OpenMP API 4.0, a major upgrade of the OpenMP API standard language specifications. Besides several major enhancements, this release provides a new mechanism to describe regions of code where data and/or computation should be moved to another computing device.

Bronis R. de Supinski, Chair of the OpenMP Language Committee, stated that “OpenMP 4.0 API is a major advance that adds two new forms of parallelism in the form of device constructs and SIMD constructs. It also includes several significant extensions for the loop-based and task-based forms of parallelism already supported in the OpenMP 3.1 API.

The 4.0 specification is now available on the 

Standard for parallel programming extends its reach

With this release, the OpenMP API specifications, the de-facto standard for parallel programming on shared memory systems, continues to extend its reach beyond pure HPC to include DSPs, real time systems, and accelerators. The OpenMP API aims to provide high-level parallel language support for a wide range of applications, from automotive and aeronautics to biotech, automation, robotics and financial analysis.

New features in the OpenMP 4.0 API include:

· Support for accelerators. The OpenMP 4.0 API specification effort included significant participation by all the major vendors in order to support a wide variety of compute devices. OpenMP API provides mechanisms to describe regions of code where data and/or computation should be moved to another computing device. Several prototypes for the accelerator proposal have already been implemented.

· SIMD constructs to vectorize both serial as well as parallelized loops. With the advent of SIMD units in all major processor chips, portable support for accessing them is essential. OpenMP 4.0 API provides mechanisms to describe when multiple iterations of the loop can be executed concurrently using SIMD instructions and to describe how to create versions of functions that can be invoked across SIMD lanes.

· Error handling. OpenMP 4.0 API defines error handling capabilities to improve the resiliency and stability of OpenMP applications in the presence of system-level, runtime-level, and user-defined errors. Features to abort parallel OpenMP execution cleanly have been defined, based on conditional cancellation and user-defined cancellation points.

· Thread affinity. OpenMP 4.0 API provides mechanisms to define where to execute OpenMP threads. Platform-specific data and algorithm-specific properties are separated, offering a deterministic behavior and simplicity in use. The advantages for the user are better locality, less false sharing and more memory bandwidth.

· Tasking extensions. OpenMP 4.0 API provides several extensions to its task-based parallelism support. Tasks can be grouped to support deep task synchronization and task groups can be aborted to reflect completion of cooperative tasking activities such as search. Task-to-task synchronization is now supported through the specification of task dependency.

· Support for Fortran 2003. The Fortran 2003 standard adds many modern computer language features. Having these features in the specification allows users to parallelize Fortran 2003 compliant programs. This includes interoperability of Fortran and C, which is one of the most popular features in Fortran 2003.

· User-defined reductions. Previously, OpenMP API only supported reductions with base language operators and intrinsic procedures. With OpenMP 4.0 API, user-defined reductions are now also supported.

· Sequentially consistent atomics. A clause has been added to allow a programmer to enforce sequential consistency when a specific storage location is accessed atomically.

This represents collaborative work by many of the brightest in industry, research, and academia, building on the consensus of 26 members. We strive to deliver high-level parallelism that is portable across 3 widely-implemented common General Purpose languages, productive for HPC and consumers, and delivers highly competitive performance. I want to congratulate all the members for coming together to create such a momentous advancement in parallel programming, under such tight constraints and industry challenges.
With this release, the OpenMP API will move immediately forward to the next release to bring even more usable parallelism to everyone.
 – Michael Wong, CEO OpenMP ARB.

The Carnegie Mellon University Software Engineering Institute (SEI) has announced the slate of software engineering thought-leaders who will serve as keynote speakers for the Team Software Process (TSP) Symposium 2013. Held in Dallas, Texas, on September 16-19, the TSP Symposium 2013 keynote line-up includes Bill Curtis, senior vice president and chief scientist with Cast Software; Enrique Ibarra, senior vice president of technology of the Mexican Stock Exchange (BMV); and Robert Behler, chief operating officer of the SEI.

The symposium theme, When Software Really Matters, explores the idea that when product quality is critical, high-quality practices are the best way to achieve it.

“When a software system absolutely must work correctly, quality must be built in from the start. A disciplined approach to quality also offers the benefit of lower lifecycle costs. The TSP promotes the application of practices that lead to superior, high-quality products,” said James McHale, TSP Symposium 2013 technical chair. “Our keynote speakers and representatives from industry and government organizations from around the world will share how using TSP helps organizations build quality in from the start when there’s no room for error.”

  • Curtis will assert that the stakes for software-caused operational problems are now larger than ever, approaching a half-billion dollars per incident. Every other aspect of the business is managed by numbers, including IT operations. Software lags behind, however, because the culture of craftsmanship still prevails. Curtis’s talk will challenge that culture: Quality measurement will be challenged for under-measuring non-functional, structural quality, the cause of many operational disasters. Productivity measurement will be challenged for not penalizing baselines when rework is shifted into future releases as technical debt. Software measurement will be challenged to better express outcomes in terms that justify investments for improving quality. The word “quality” will be challenged as the wrong way to frame the argument. Curtis will propose a measurement stack or measurement pyramid to help translate software numbers to business numbers. At the foundation of this pyramid are the Personal Software Process (PSP) and TSP.
  • Ibarra will detail the Mexican Stock Exchange’s (BMV) broad plan of technological renovation that included migration to a new state-of-the-art data center and creating new operational systems with better functionalities and quality attributes. Since 2005, the BMV, which is responsible for operating the cash and derivatives market of the country and is the only exchange in Mexico, has faced the constant challenge of accommodating an exponential growth of demand for its transactional services as well as pressure from the market to offer services with better response times and functionalities. One of the most challenging software projects included in this technological renovation plan was the redesign and construction of the operational system known as the trading engine, which has strict and ambitious requirements for speed (latency), scalability, and continuous availability. The new system, which was to be designed and built internally, and the project were called MoNeT. The BMV had two goals for MoNeT: making sure a carefully considered and reviewed system architecture was in place prior to building the system and adopting a software development process that maximizes the quality of the new system and ensures that it complies with its intended quality attributes. Ibarra will describe the most relevant aspects of the MoNeT project, its performance in production, and the business impact it had on the BMV.
  • Behler, one of only 139 individuals qualified as pilots of the Lockheed SR-71 Blackbird aircraft, will describe his experience flying the fastest, most physically demanding aircraft in the world to gather vital data during the Cold War and the teamwork approach it took to develop the aircraft. The SR-71 was developed in the 1960s with myriad sophisticated sensors used to acquire highly specific intelligence data. The aircraft remains an icon of American aerospace engineering to this day and is considered to be the most effective reconnaissance aircraft in history.

In addition to the keynote speakers, substantial technical program, and organized networking events, the TSP Symposium 2013 also offers practitioners an in-depth learning opportunity with full-day tutorials on introductory and advanced TSP concepts.

“I am very excited about this year’s lineup of keynote speakers and technical presenters. The symposium should be stimulating with presentations on a broad array of topics related to quality-focused software development. It is also an excellent way for participants to network and exchange diverse ideas about how they have used the PSP/TSP approach to achieve their software quality goals,” said Mark Kasunic, Symposium co-chair.

CoffeeScript, as many people know, is a transpile-to-JavaScript language.1 For the most part, it does not introduce major changes in semantics. For example, this:

-> 'Hello, world'

Transpiles directly to:

function () { return 'Hello, world'; }

This is convenient syntactic sugar, and by removing what some folks call the “syntactic vinegar” of extraneous symbols, it encourages the use of constructs that would otherwise make the code noisy and obscure the important meaning. The vast majority of features introduced by CoffeeScript are of this nature: They introduce local changes that transpile directly to JavaScript.2

CoffeeScript also introduces features that don’t exist in JavaScript, such as destructuring assignment and comprehensions. In each case, the features compile directly to JavaScript without introducing changes elsewhere in the program. And since they don’t look like existing JavaScript features, little confusion is created.

equals doesn’t equal equals

One CoffeeScript feature does introduce confusion, and the more you know JavaScript the more confusion it introduces. This is the behaviour of the assignment operator, the lowly (and prevalent!) equals sign:

foo = 'bar'

Although it looks almost identical to assignment in JavaScript:

foo = 'bar';

It has different semantics. That’s confusing. Oh wait, it’s worse than that: Sometimes it has different semantics. Sometimes it doesn’t.

So what’s the deal with that?

Well, let’s review the wonderful world of JavaScript. We’ll pretend we’re in a browser application, and we write:

foo = 'bar';

What does this mean? Well, it depends: If this is in the top level of a file, and not inside of a function, then foo is a global variable. In JavaScript, global means global across all files, so you are now writing code that is coupled with every other file in your application or any vendored code you are loading.

But what if it’s inside a function?

function fiddleSticks (bar) {
  foo = bar;
  // ...
}

For another example, many people enclose file code in an Immediately Invoked Function Expression (“IIFE”) like this:

;(function () {
  foo = 'bar'
  // more code...
})();

What do foo = 'bar'; or foo = bar; mean in these cases? Well, it depends as we say. It depends on whether foo is declared somewhere else in the same scope. For example:

function fiddleSticks (bar) {
  var foo;
  foo = bar;
  // ...
}

Or:

function fiddleSticks (bar) {
  foo = bar;
  // ...
  var foo = batzIndaBelfrie;
  // ...
} 

Or even:

function fiddleSticks (bar) {
  foo = bar;
  // ...
  function foo () {
    // ...
  }
  // ...
}

Because of something called hoisting,3 these all mean the same this: foo is local to function fiddleSticks, and therefore it is NOT global and ISN’T magically coupled to every other file loaded whether written by yourself or someone else.

nested scope

JavaScript permits scope nesting. If you write this:

function foo () {
  var bar = 1;
  var bar = 2;
  return bar;
}

Then bar will be 2. Declaring bar twice makes no difference, since both declarations are in the same scope. However, if you nest functions, you can nest scopes:

function foo () {
  var bar = 1;
  function foofoo () {
    var bar = 2;
  }
  return bar;
}

Now function foo will return 1 because the second declaration of bar is inside a nested function, and therefore inside a nested scope, and therefore it’s a completely different variable that happens to share the same name. This is called shadowing: The variablebar inside foofoo shadows the variable bar inside foo.

javascript failure modes

Now over time people have discovered that global variables are generally a very bad idea, and accidental global variables doubly so. Here’s an example of why:

function row (numberOfCells) {
  var str = '';
  for (i = 0; i < numberOfCells; ++i) {
    str = str + '<td></td>';
  }
  return '<tr>' + str + '</tr>';
}

function table (numberOfRows, numberOfColumns) {
  var str = '';
  for (i = 0; i < numberOfRows; ++i) {
    str = str + row(numberOfColumns);
  }
  return '<table>' + str + '</table>';
}

Let’s try it:

table(3, 3)
  //=> "<table><tr><td></td><td></td><td></td></tr></table>"

We only get one row, because the variable i in the function row is global, and so is the variable i in the function table, so they’re the exact same global variable. Therefore, after counting out three columns, i is 3 and the for loop in table finishes. Oops!

And this is especially bad because the two functions could be anywhere in the code. If you accidentally use a global variable and call a function elsewhere that accidentally uses the same global variable, pfft, you have a bug. This is nasty because there’s this weird action-at-a-distance where a bug in one file reaches out and breaks some code in another file.

Now, this isn’t a bug in JavaScript the language, just a feature that permits the creation of very nasty bugs. So I call it a failure mode, not a language bug.

coffeescript to the rescue

CoffeeScript addresses this failure mode in two ways. First, all variables are local to functions. If you wish to do something in the global environment, you must do it explicitly. So in JavaScript:

UserModel = Backbone.Model.extend({ ... });
var user = new UserModel(...);

While in CoffeeScript:

window.UserModel = window.Backbone.Model.extend({ ... })
user = new window.UserModel(...)

Likewise, CoffeeScript bakes the IIFE enclosing every file in by default. So instead of:

;(function () {
  // ...
})();

You can just write your code.4

The net result is that it is almost impossible to replicate the JavaScript failure mode of creating or clobbering a global variable by accident. That is a benefit.

what would coffeescript do?

This sounds great, but CoffeeScript can be surprising to JavaScript programmers. Let’s revisit our table function. First, we’ll fix it:

function row (numberOfCells) {
  var i,
      str = '';
  for (i = 0; i < numberOfCells; ++i) {
    str = str + '<td></td>';
  }
  return '<tr>' + str + '</tr>';
}

function table (numberOfRows, numberOfColumns) {
  var i,
      str = '';
  for (i = 0; i < numberOfRows; ++i) {
    str = str + row(numberOfColumns);
  }
  return '<table>' + str + '</table>';
}

table(3, 3)
  //=> "<table><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr></table>"

Good! Now suppose we notice that no function calls row other than table. Although there is a slightly more “performant” way to do this, we decide that the clearest and simplest way to indicate this relationship is to nest row inside table Pascal-style:

function table (numberOfRows, numberOfColumns) {
  var i,
      str = '';
  for (i = 0; i < numberOfRows; ++i) {
    str = str + row(numberOfColumns);
  }
  return '<table>' + str + '</table>';

  function row (numberOfCells) {
    var i,
        str = '';
    for (i = 0; i < numberOfCells; ++i) {
      str = str + '<td></td>';
    }
    return '<tr>' + str + '</tr>';
  }
}

It still works like a charm, because the i in row shadows the i in table, so there’s no conflict. Okay. Now how does it work in CoffeeScript?

Here’s one possible translation to CoffeeScript:

table = (numberOfRows, numberOfColumns) ->
  row = (numberOfCells) ->
    str = ""
    i = 0
    while i < numberOfCells
      str = str + "<td></td>"
      ++i
    "<tr>" + str + "</tr>"
  str = ""
  i = 0
  while i < numberOfRows
    str = str + row(numberOfColumns)
    ++i
  return "<table>" + str + "</table>"

table(3,3)
  #=> "<table><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr></table>"

It works just fine. Here’s another:

table = (numberOfRows, numberOfColumns) ->
  str = ""
  i = 0
  row = (numberOfCells) ->
    str = ""
    i = 0
    while i < numberOfCells
      str = str + "<td></td>"
      ++i
    "<tr>" + str + "</tr>"
  str = ""
  i = 0
  while i < numberOfRows
    str = str + row(numberOfColumns)
    ++i
  return "<table>" + str + "</table>"

table(3,3)
  #=> "<table><tr><td></td><td></td><td></td></tr></table>"

Broken! And a third:

str = ""
i = 0
table = (numberOfRows, numberOfColumns) ->
  row = (numberOfCells) ->
    str = ""
    i = 0
    while i < numberOfCells
      str = str + "<td></td>"
      ++i
    "<tr>" + str + "</tr>"
  str = ""
  i = 0
  while i < numberOfRows
    str = str + row(numberOfColumns)
    ++i
  return "<table>" + str + "</table>"

table(3,3)
  #=> "<table><tr><td></td><td></td><td></td></tr></table>"

Also broken! Although the three examples look similar, the first gives us what we expect but the second and third do not. What gives?

Well, CoffeeScript doesn’t allow us to “declare” that variables are local with var. They’re always local. But local to what? In CoffeeScript, they’re local to the function that either declares the variable as a parameter or that contains the first assignment to the variable.5 So in our first example, reading from the top, the first use of str and i is inside the row function, so CoffeeScript makes them local to row.

A little later on, the code makes an assignment to i and str within the table function. This scope happens to enclose row’s scope, but it is different so it can’t share the strand i variables. CoffeeScript thus makes the i and str in table variables local totable. As a result, the i and str in row end up shadowing the i and str in table.

The second example is different. The first i encountered by CoffeeScript is in table, so CoffeeScript makes it local to table as we’d expect. The second i is local to row. But since row in enclosed by table, it’s possible to make that i refer to the i already defined, and thus CoffeeScript does not shadow the variable. The i inside row is the same variable as the i inside table.

In the third example, i (and str) are declared outside of both table and row, and thus again they all end up being the same variable with no shadowing.

Now, CoffeeScript could scan an entire function before deciding what variables belong where, but it doesn’t. That simplifies things, because you don’t have to worry about a variable being declared later that affects your code. Everything you need to understand is in the same file and above your code.

In many cases, it also allows you to manipulate whether a variable is shadowed or not by carefully controlling the order of assignments. That’s good, right?

all those against the bill, say “nay nay!”

Detractors of this behaviour say this is not good. When JavaScript is written using var, the meaning of a function is not changed by what is written elsewhere in the file before the code in question. Although you can use this feature to control shadowing by deliberately ordering your code to get the desired result, a simple refactoring can break what you’ve already written.

For example, if you write:

table = (numberOfRows, numberOfColumns) ->
  row = (numberOfCells) ->
    str = ""
    i = 0
    while i < numberOfCells
      str = str + "<td></td>"
      ++i
    "<tr>" + str + "</tr>"
  str = ""
  i = 0
  while i < numberOfRows
    str = str + row(numberOfColumns)
    ++i
  return "<table>" + str + "</table>"

table(3,3)
  #=> "<table><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr></table>"

All will be well, until you are debugging late one night, and you add:

console.log('Hello!') for i in [1..5]

table = (numberOfRows, numberOfColumns) ->
  row = (numberOfCells) ->
    str = ""
    i = 0
    while i < numberOfCells
      str = str + "<td></td>"
      ++i
    "<tr>" + str + "</tr>"
  str = ""
  i = 0
  while i < numberOfRows
    str = str + row(numberOfColumns)
    ++i
  return "<table>" + str + "</table>"

table(3,3)
  #=> "table><tr><td></td><td></td><td></td></tr></table>"

This breaks your code because the i you used at the top “captures” the other variables so they are now all the same thing. To someone used to JavaScript, this is a Very Bad Thing™. When you write this in JavaScript:

function row (numberOfCells) {
  var i,
      str = '';
  for (i = 0; i < numberOfCells; ++i) {
    str = str + '<td></td>';
  }
  return '<tr>' + str + '</tr>';
}

It will always mean the same thing no matter where it is in a file, and no matter what comes before it or after it. There is no spooky “action-at-a-distance” where code somewhere else changes what this code means. Whereas in CoffeeScript, you don’t know whether the iin row is local to row or not without scanning the code that comes before it in the same or enclosing scopes.

coffeescript’s failure mode

In this case, CoffeeScript has a failure mode: The meaning of a function seems to be changed by altering its position within a file or (in what amounts to the same thing) by altering code that appears before it in a file in the same or enclosing scopes. In contrast, JavaScript’s var declaration never exhibits this failure mode. JavaScript has a different action-at-a-distance failure mode, where neglecting var causes action at a much further distance: The meaning of code can be affected by code written in an entirely different file.

Mind you, the result of calling our row function is not affected by declaring an i in an enclosing scope. Our function always did what it was expected to do and always will. Although you and I know why the change breaks the table function is that row now uses an enclosed variable, imagine that we were writing unit tests. All of our tests for row would continue to pass, it’s the tests for table that break. So in an evidence-based programming sense, when we maintain the habit of always initializing variables we expect to use locally, changing code outside of those functions only changes the evidence that the enclosing code produces.

So one way to look at this is that row is fine, but moving i around changes the meaning of the code where you move i. And why wouldn’t you expect making changes to tableto change its meaning?

so which way to the asylum?

If you ask around, you can find people who dislike JavaScript’s behaviour, and others who dislike CoffeeScript’s behaviour. Accidentally getting global variables when you neglectvar is brutal, and action-at-a-distance affecting the meaning of a function (even if it is always within the same file) flies against everything we have learned about the importance of writing small chunks of code that completely encapsulate their behaviour.

Of course, programmers tend to internalize the languages they learn to use. If you write a lot of JavaScript, you habitually use var and may have tools that slap your wrist when you don’t. You’re bewildered by all this talk of action-at-a-distance. It will seems to you to be one of those rookie mistake problems that quickly goes away and is not a practical concern.

Likewise, if you write twenty thousand lines of CoffeeScript, you may never be bitten by its first-use-is-a-declaration behaviour. You may be in the habit of using variable names likeiRow and iColumn out of habit. You may find that your files never get so large and your functions so deeply nested that a “capture” problem takes longer than three seconds to diagnose and fix.

It’s a bit of a cop-out, but I suggest that this issue resembles the debate over strong, manifest typing vs. dynamic typing. In theory, one is vastly preferable to the other. But in practice, large stable codebases are written with both kinds of languages, and programmers seem to adjust to overcome the failure modes of their tools unconsciously while harvesting the benefits that each language provides.


  1. Yes, “transpile” is a real word, or at least, a real piece of jargon. It’s a contraction of “transcompiler,” which is a compiler that translates one language to another language at a similar level of abstraction. There’s room for debate over what constitutes a “similar level of abstraction.” https://en.wikipedia.org/wiki/Source-to-source_compiler

  2. There are other possibilities: You could write a Tail-Call Optimized language that transpiles to JavaScript, however its changes wouldn’t always be local: Some function calls would be rewritten substantially to use trampolining. Or adding continuations to a language might cause everything to be rewritten in continuation-passing style.

  3. Scanning all of the code first is called “hoisting,” in part because some declarations nested in blocks are “hoisted” up to the level of the function, and all declarations are “hoisted” to the top of the function. This is a source of confusion for some programmers, but it isn’t germane to this essay.

  4. If you don’t want the file enclosed in an IIFE, you can compile your CoffeeScript with the--bare command-line switch.

I’ve been listening to the audiobook of Heart of Darkness this week, read by Kenneth Branagh. It’s fantastic. It also reminds me of some jobs I’ve had in the past.

There’s a great passage in which Marlow requires rivets to repair a ship, but finds that none are available. This, in spite of the fact that the camp he left further upriver is drowning in them. That felt familiar. There’s also a famous passage involving a French warship that’s blindly firing its cannons into the jungles of Africa in hopes of hitting a native camp situated within. I’ve had that job as well. Hopefully I can help you avoid getting yourself into those situations.

There are several really good lists of common traits seen in well-functioning engineering organizations. Most recently, there’s Pamela Fox’s list of What to look for in a software engineering culture. More famous, but somewhat dated at this point, is Joel Spolsky’s Joel Test. I want to talk about signs of teams that you should avoid.

This list is partially inspired by Ralph Peters’ Spotting the Losers: Seven Signs of Non-Competitive States. Of course, such a list is useless if you can’t apply it at the crucial point, when you’re interviewing. I’ve tried to include questions to ask and clues to look for that reveal dysfunction that is deeply baked into an engineering culture.

Preference for process over tools. As engineering teams grow, there are many approaches to coordinating people’s work. Most of them are some combination of process and tools. Git is a tool that enables multiple people to work on the same code base efficiently (most of the time). A team may also design a process around Git — avoiding the use of remote branches, only pushing code that’s ready to deploy to the master branch, or requiring people to use local branches for all of their development. Healthy teams generally try to address their scaling problems with tools, not additional process. Processes are hard to turn into habits, hard to teach to new team members, and often evolve too slowly to keep pace with changing circumstances. Ask your interviewers what their release cycle is like. Ask them how many standing meetings they attend. Look at the company’s job listings, are they hiring a scrum master?

Excessive deference to the leader or worse, founder. Does the group rely on one person to make all of the decisions? Are people afraid to change code the founder wrote? Has the company seen a lot of turnover among the engineering leader’s direct reports? Ask your interviewers how often the company’s coding conventions change. Ask them how much code in the code base has never been rewritten. Ask them what the process is for proposing a change to the technology stack. I have a friend who worked at a growing company where nobody was allowed to introduce coding conventions or libraries that the founding VP of Engineering didn’t understand, even though he hardly wrote any code any more.

Unwillingness to confront technical debt. Do you want to walk into a situation where the team struggles to make progress because they’re coding around all of the hacks they haven’t had time to address? Worse, does the team see you as the person who’s going to clean up all of the messes they’ve been leaving behind? You need to find out whether the team cares about building a sustainable code base. Ask the team how they manage their backlog of bugs. Ask them to tell you about something they’d love to automate if they had time. Is it something that any sensible person would have automated years ago? That’s a bad sign.

Not invented this week syndrome. We talk a lot about “not invented here” syndrome and how it affects the competitiveness of companies. I also worry about companies that lurch from one new technology to the next. Teams should make deliberate decisions about their stack, with an eye on the long term. More importantly, any such decisions should be made in a collaborative fashion, with both developer productivity and operability in mind. Finding out about this is easy. Everybody loves to talk about the latest thing they’re working with.

Disinterest in sustaining a Just Culture. What’s Just Culture? This post by my colleague John Allspaw on blameless post mortems describes it pretty well. Maybe you want to work at a company where people get fired on the spot for screwing up, or yelled at when things go wrong, but I don’t. How do you find out whether a company is like that? Ask about recent outages and gauge whether the person you ask is willing to talk about them openly. Do the people you talk to seem ashamed of their mistakes?

Monoculture. Diversity counts. Gender diversity is really important, but it’s not the only kind of diversity that matters. There’s ethnic diversity, there’s age diversity, and there’s simply the matter of people acting differently, or dressing differently. How homogenous is the group you’ve met? Do they all remind you of you? That’s almost certainly a serious danger sign. You may think it sounds like fun to work with a group of people who you’d happily have as roommates, but monocultures do a great job of masking other types of dysfunction.

Lack of a service-oriented mindset. The biggest professional mistakes I ever made were the result of failing to see that my job was ultimately to serve other people. I was obsessed with building what I thought was great software, and failed to see that what I should have been doing was paying attention to what other people needed from me in order to succeed in their jobs. You can almost never fail when you look for opportunities to be of service and avail yourself of them. Be on the lookout for companies where people get ahead by looking out for themselves. Don’t take those jobs.

There are a lot of ways that a team’s culture can be screwed up, but those are my top seven.

Boost Dependency Analyzer

I have something special to announce today. A tool I’ve build over the last 2 weeks, which allows to analyze the dependencies in boost. With boost 1.53 this spring, I had the idea to build this, but not the time, as I was busy writing a series over the Papers for Bristol. Back then I realized, how easy it could be to build such a tool, as the dependencies could be read & listed by boosts bcp tool. I already had a prototype for the graphpart from 2010. But lets have a look at the tool:

The tool is very easy to handle, it is based on the out of bcp, which is a tool coming with boost. Actually bcp can help you with ripping libraries out of boost, so that you don’t have to add all of boost to your repository when you would like to use smartpointers. But bcp also has a listing mode, where it only shows the dependencies thats whats my tool build up upon. Lets have a short look at the results, the dependencies of boost 1.54:

A few words on how to read this graph. The libraries in the middle of the “starshape” are the ones with the most dependencies, each line between the nodes is a dependency. A dependency can be one or multiple files. The graphlayout is not weighted.

How to

A short introduction on what you need to get this tool to run. First boost, as this tool is build to analyze boost. I’ve tested with some versions (1.49 – 1.54) of boost. You also need a version of bcp, which is quite easy to build (b2 tools/bcp). Then you simply need to start the tool, if BOOST_ROOT is set, the tool will try to read it, other wise you will be asked to choose the location of boost when clicking on Read dependencies. Next thing is selecting the location of bcp. That is the setup, and the tool will now run for some time. On my machine its 90 seconds to 2 minutes the analysis takes, it might be lot longer on yours, depending on how much cores you got. The tool will spawn for each boost library (~112) a bcp process, and analyze this output in a thread pool. After this is done, the data is loaded into the tool, and then saved to a SQLITE database, which will be used if you start the tool a second time and select this version of boost. Loading from the database is far faster.

A screenshot to illustrate this:

tl_files/blog/bda/bda.png

To the left are all the boost libraries, the number of dependencies is shown in the braces. To the right is a Tabwidget showing all the dependencies, the graph is layouted with boost graph. When you click on show all you’ll get the full view of all dependencies in boost. The layouting is done in the background, so this will take some time to calculate, and is animated when its done. The results of the layouting are good, but not perfect, so that you might have to move some nodes. Exporting supports images, which are transparent PNGs, not all services/tools are happy with that (f.e. facebook, twitter nor G+ could handle the perfectly fine images), this can be fixed by postprocessing the images and adding a white background.

Inner workings

I’ve already written a little about the tools inside, its build with Qt5.1 and boost. Where boost is mostly used for the graph layouting. As I choose to work with Qt5, it has a few more dependencies, for windows this sums up to a 18 mb download, which you’ll find at the end. The tool depends on 3 libraries from my company Code Node: ProcessingSink, a small wrapper around QProcess, that allows to just start a bunch of processes, and lets you connect to the finished and error slot. This was necessary, as I could only spawn 62 parallel processes under windows, so this library does take care of spawning the parallel processes now. Which are currently 50 at a time. GraphLayout is the code that wraps the innerworkings of boost::graph, its a bit dirty, but lets me easily process the graphlayouting. The 3rd library is NodeGraph, which is the Graph UI, based on Qts GraphicsView Framework.
I plan to release the tool and its libraries under GPL later on github, for now I don’t have the time to polish everything.

Problems

One of the earliest questions I had when thinking about building such a tool, was where to get a list of the boost libraries? This sounds easy. But I need to have this readable by machine, not human, so HTML is a great format, but I refused to write a parser for this list yet. I talked to some people about this at C++Now, and most agreed, that the second option would be best: maintainers.txt. Thats what the tool reads currently to find the boost libraries. Unfortunately at least lexical_cast is missing in this list. So, the tool isn’t perfect yet, while lexical_cast is already patched, I’m not sure if anything else is missing. A candidate could be signals, as its not maintained anymore. Currently the tool analyzes for 1.54 112 libraries.

boost dependencies

Working for 2 weeks on this tool has given me some inside knowledge about the dependencies in boost. First, the way it is shown in the tool, is the view of bcp. Some dependencies will not affect the user, as they are internal. f.e. a lot of libraries have a dependency to boost::test, simply because they provide their tests with it. The bcp tool really gets you ALL the dependencies. Also most (or was it all?) libraries depend on boost::config. I plan to add filtering later, so that the user has the ability to filter some of the libraries in the GraphView.

The tool

Here is how to get the tool for now: there is a download for the binaries for windows and linux. I’ll try to get you a deb package as soon as I have time, but for now its only the binaries for linux, you’ll have to make sure to have Qt5.1 etc. on linux too, as I do not provide them. For Windows, its 2 archives you’ll need to download: the programm itself, and needed dlls for Qt5.1 if you don’t have the SDK installed ( in this case you also could copy them from the bin directory)

Note on linux: this is a one day old beta version. Will update this later.

In this post we’ll look at five ways in roomates efficient coding we can use to help our garbage collector CPU spend less time allocating and freeing memory, and reduce GC overhead. Often Long GCs can lead to our code being stopped while memory is reclaimed (AKA “stop the world”). Duke_GCPost

Some background

The GC is built to handle large amounts of allocations of short-lived objects (think of something like rendering a web page, where most of the objects allocated Become obsolete once the page is served).

The GC does this using what’s called a “young generation” – a heap segment where new objects are allocated. Each object has an “age” (placed in the object’s header bits) defines how many roomates collections it has “survived” without being reclaimed. Once a certain age is reached, the object is copied into another section in the heap called a “survivor” or “old” generation.

The process, while efficient, still comes at a cost. Being Able to reduce the number of temporary allocations can really help us increase of throughput, especially in high-scale applications.

Below are five ways everyday we can write code that is more memory efficient, without having to spend a lot of time on it, or reducing code readability.

1. Avoid implicit Strings

Strings are an integral part of almost every structure of data we manage. Being much heavier than other primitive values, they have a much stronger impact on memory usage.

One of the most important things to note is that Strings are immutable. They can not be modified after allocation. Operators such as “+” for concatenation actually allocate a new String containing the contents of the strings being joined. What’s worse, is there’s an implicit StringBuilder object that is allocated to actually do the work of combining them.

For example –

1
a = a + b; / / a and b are Strings
The compiler generates code comparable behind the scenes:

1
StringBuilder temp = new StringBuilder (a).
2
temp.append (b);
3
a = temp.toString () / / a new string is allocated here.
4
/ / The previous “a” is now garbage.
But it gets worse.

Let’s look at this example –

1
String result = foo () + arg;
2
result + = boo ();
3
System.out.println (“result =” + result);
In this example we have 3 StringBuilders allocated in the background – one for each plus operation, and two additional Strings – one to hold the result of the second assignment and another to hold the string passed into the print method. That’s 5 additional objects in what would otherwise Appear to be a pretty trivial statement.

Think about what happens in real-world scenarios such as generating code a web page, working with XML or reading text from a file. Within a nested loop structures, you could be looking at Hundreds or Thousands of objects that are implicitly allocated. While the VM has Mechanisms to deal with this, it comes at a cost – one paid by your users.

The solution: One way of reducing this is being proactive with StringBuilder allocations. The example below Achieves the same result as the code above while allocating only one StringBuilder and one string to hold the final result, instead of the original five objects.

1
StringBuilder value = new StringBuilder (“result =”);
2
value.append (foo ()). append (arg). append (boo ());
3
System.out.println (value);
By being mindful of the way Strings are implicitly allocated and StringBuilders you can materially reduce the amount of short-term allocations in high-scale code locations.

2. List Plan capacities

Dynamic collections such as ArrayLists are among the most basic dynamic structures to hold the data length. ArrayLists and other collections such as HashMaps and implemented a Treemaps are using the underlying Object [] arrays. Like Strings (Themselves wrappers over char [] arrays), arrays are also immutable. Becomes The obvious question then – how can we add / put items in their collections if the underlying array’s size is immutable? The answer is obvious as well – by allocating more arrays.

Let’s look at this example –

1
List <Item> <Item> items = new ArrayList ();
2

3
for (int i = 0; i <len; i + +)
4
{
5
Item item = readNextItem ();
6
items.add (item);
7
}
The value of len Determines the ultimate length of items once the loop finishes. This value, however, is unknown to the constructor of the ArrayList roomates allocates a new Object array with a default size. Whenever the internal capacity of the array is exceeded, it’s replaced with a new array of sufficient length, making the previous array of garbage.

If you’re executing the loop Welcome to Thunderbird times you may be forcing a new array to be allocated and a previous one to be collected multiple times. For code running in a high-scale environment, these allocations and deallocations are all deducted from your machine’s CPU cycles.
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 SIGGRAPH – Anaheim, CA – The Khronos™ Group today announced the immediate release of the OpenGL® 4.4 specification,bringing the very latest graphics functionality to the most advanced and widely adopted cross-platform 2D and 3D graphics API (application programming interface). OpenGL 4.4 unlocks capabilities of today’s leading-edge graphics hardware while maintaining full backwards compatibility, enabling applications to incrementally use new features while portably accessing state-of-the-art graphics processing units (GPUs) across diverse operating systems and platforms. Also, OpenGL 4.4 defines new functionality to streamline the porting of applications and titles from other platforms and APIs. The full specification and reference materials are available for immediate download at http://www.opengl.org/registry.

In addition to the OpenGL 4.4 specification, the OpenGL ARB (Architecture Review Board) Working Group at Khronos has created the first set of formal OpenGL conformance tests since OpenGL 2.0. Khronos will offer certification of drivers from version 3.3, and full certification is mandatory for OpenGL 4.4 and onwards. This will help reduce differences between multiple vendors’ OpenGL drivers, resulting in enhanced portability for developers.

New functionality in the OpenGL 4.4 specification includes:

Buffer Placement Control (GL_ARB_buffer_storage)
Significantly enhances memory flexibility and efficiency through explicit control over the position of buffers in the graphics and system memory, together with cache behavior control – including the ability of the CPU to map a buffer for direct use by a GPU.

Efficient Asynchronous Queries
(GL_ARB_query_buffer_object)
Buffer objects can be the direct target of a query to avoid the CPU waiting for the result and stalling the graphics pipeline. This provides significantly boosted performance for applications that intend to subsequently use the results of queries on the GPU, such as dynamic quality reduction strategies based on performance metrics.

Shader Variable Layout (GL_ARB_enhanced_layouts)
Detailed control over placement of shader interface variables, including the ability to pack vectors efficiently with scalar types. Includes full control over variable layout inside uniform blocks and enables shaders to specify transform feedback variables and buffer layout.

Efficient Multiple Object Binding (GL_ARB_multi_bind)
New commands which enable an application to bind or unbind sets of objects with one API call instead of separate commands for each bind operation, amortizing the function call, name space lookup, and potential locking overhead. The core rendering loop of many graphics applications frequently bind different sets of textures, samplers, images, vertex buffers, and uniform buffers and so this can significantly reduce CPU overhead and improve performance.

Streamlined Porting of Direct3D applications

A number of core functions contribute to easier porting of applications and games written in Direct3D including GL_ARB_buffer_storage for buffer placement control, GL_ARB_vertex_type_10f_11f_11f_rev which creates a vertex data type that packs three components in a 32 bit value that provides a performance improvement for lower precision vertices and is a format used by Direct3D, and GL_ARB_texture_mirror_clamp_to_edge that provides a texture clamping mode also used by Direct3D.Extensions released alongside the OpenGL 4.4 specification include:

Bindless Texture Extension (GL_ARB_bindless_texture)
Shaders can now access an effectively unlimited number of texture and image resources directly by virtual addresses. This bindless texture approach avoids the application overhead due to explicitly binding a small window of accessible textures. Ray tracing and global illumination algorithms are faster and simpler with unfettered access to a virtual world’s entire texture set.

Sparse Texture Extension (GL_ARB_sparse_texture)
Enables handling of huge textures that are much larger than the GPUs physical memory by allowing an application to select which regions of the texture are resident for ‘mega-texture’ algorithms and very large data-set visualizations.

OpenGL BOF at SIGGRAPH, Anaheim, CA July 24th 2013
There is an OpenGL BOF “Birds of a Feather” Meeting on Wednesday July 24th at 7-8PM at the Hilton Anaheim, California Ballroom A & B, where attendees are invited to meet OpenGL implementers and developers and learn more about the new OpenGL 4.4 specification.

As one of the developers of the Glasgow Haskell Compiler (GHC) for almost 15 years, I have seen Haskell grow from a niche research language into a rich and thriving ecosystem. I spent a lot of that time working on GHC’s support for parallelism and concurrency. One of the first things I did to GHC in 1997 was to rewrite its runtime system, and a key decision we made at that time was to build concurrency right into the core of the system rather than making it an optional extra or an add-on library. I like to think this decision was founded upon shrewd foresight, but in reality it had as much to do with the fact that we found a way to reduce the overhead of concurrency to near zero (previously it had been on the order of 2%; we’ve always been performance-obsessed). Nevertheless, having concurrency be non-optional meant that it was always a first-class part of the implementation, and I’m sure that this decision was instrumental in bringing about GHC’s solid and lightning-fast concurrency support.

Haskell has a long tradition of being associated with parallelism. To name just a few of the projects, there was the pH variant of Haskell derived from the Id language, which was designed for parallelism, the GUM system for running parallel Haskell programs on multiple machines in a cluster, and the GRiP system: a complete computer architecture designed for running parallel functional programs. All of these happened well before the current multicore revolution, and the problem was that this was the time when Moore’s law was still giving us ever-faster computers. Parallelism was difficult to achieve, and didn’t seem worth the effort when ordinary computers were getting exponentially faster.

Around 2004, we decided to build a parallel implementation of the GHC runtime system for running on shared memory multiprocessors, something that had not been done before. This was just before the multicore revolution. Multiprocessor machines were fairly common, but multicores were still around the corner. Again, I’d like to think the decision to tackle parallelism at this point was enlightened foresight, but it had more to do with the fact that building a shared-memory parallel implementation was an interesting research problem and sounded like fun. Haskell’s purity was essential—it meant that we could avoid some of the overheads of locking in the runtime system and garbage collector, which in turn meant that we could reduce the overhead of using parallelism to a low-single-digit percentage. Nevertheless, it took more research, a rewrite of the scheduler, and a new parallel garbage collector before the implementation was really usable and able to speed up a wide range of programs. The paper I presented at the International Conference on Functional Programming (ICFP) in 2009 marked the turning point from an interesting prototype into a usable tool.

All of this research and implementation was great fun, but good-quality resources for teaching programmers how to use parallelism and concurrency in Haskell were conspicuously absent. Over the last couple of years, I was fortunate to have had the opportunity to teach two summer school courses on parallel and concurrent programming in Haskell: one at the Central European Functional Programming (CEFP) 2011 summer school in Budapest, and the other the CEA/EDF/INRIA 2012 Summer School at Cadarache in the south of France. In preparing the materials for these courses, I had an excuse to write some in-depth tutorial matter for the first time, and to start collecting good illustrative examples. After the 2012 summer school I had about 100 pages of tutorial, and thanks to prodding from one or two people (see the Acknowledgments), I decided to turn it into a book. At the time, I thought I was about 50% done, but in fact it was probably closer to 25%. There’s a lot to say! I hope you enjoy the results.

Audience

You will need a working knowledge of Haskell, which is not covered in this book. For that, a good place to start is an introductory book such as Real World Haskell (O’Reilly), Programming in Haskell (Cambridge University Press), Learn You a Haskell for Great Good! (No Starch Press), or Haskell: The Craft of Functional Programming (Addison-Wesley).

How to Read This Book

The main goal of the book is to get you programming competently with Parallel and Concurrent Haskell. However, as you probably know by now, learning about programming is not something you can do by reading a book alone. This is why the book is deliberately practical: There are lots of examples that you can run, play with, and extend. Some of the chapters have suggestions for exercises you can try out to get familiar with the topics covered in that chapter, and I strongly recommend that you either try a few of these, or code up some of your own ideas.

As we explore the topics in the book, I won’t shy away from pointing out pitfalls and parts of the system that aren’t perfect. Haskell has been evolving for over 20 years but is moving faster today than at any point in the past. So we’ll encounter inconsistencies and parts that are less polished than others. Some of the topics covered by the book are very recent developments: Chapters 4, 5, 6, and pass:[14 cover frameworks that were developed in the last few years.

The book consists of two mostly independent parts: Part I and Part II. You should feel free to start with either part, or to flip between them (i.e., read them concurrently!). There is only one dependency between the two parts: Chapter 13 will make more sense if you have read Part I first, and in particular before reading “The ParIO monad”, you should have read Chapter 4.

While the two parts are mostly independent from each other, the chapters should be read sequentially within each part. This isn’t a reference book; it contains running examples and themes that are developed across multiple chapters.