Emacs Lisp Lambda Expressions Are Not Self-Evaluating

This week I made a mistake that ultimately enlightened me about the nature of function objects in Emacs Lisp. There are three kinds of function objects, but they each behave very differently when evaluated as objects.

But before we get to that, let’s talk about one of Emacs’ embarrassing, old missteps: eval-after-load.

Taming an old dragon

One of the long-standing issues with Emacs is that loading Emacs Lisp files (.el and .elc) is a slow process, even when those files have been byte compiled. There are a number of dirty hacks in place to deal with this issue, and the biggest and nastiest of them all is the dumper, also known as unexec.

The Emacs you routinely use throughout the day is actually a previous instance of Emacs that’s been resurrected from the dead. Your undead Emacs was probably created months, if not years, earlier, back when it was originally compiled. The first stage of compiling Emacs is to compile a minimal C core called temacs. The second stage is loading a bunch of Emacs Lisp files, then dumping a memory image in an unportable, platform-dependent way. On Linux, this actually requires special hooks in glibc. The Emacs you know and love is this dumped image loaded back into memory, continuing from where it left off just after it was compiled. Regardless of your own feelings on the matter, you have to admit this is a very lispy thing to do.

There are two notable costs to Emacs’ dumper:

  1. The dumped image contains hard-coded memory addresses. This means Emacs can’t be a Position Independent Executable (PIE). It can’t take advantage of a security feature called Address Space Layout Randomization (ASLR), which would increase the difficulty of exploiting some classes of bugs. This might be important to you if Emacs processes untrusted data, such as when it’s used as a mail client, a web server or generally parses data downloaded across the network.

  2. It’s not possible to cross-compile Emacs since it can only be dumped by running temacs on its target platform. As an experiment I’ve attempted to dump the Windows version of Emacs on Linux using Wine, but was unsuccessful.

The good news is that there’s a portable dumper in the works that makes this a lot less nasty. If you’re adventurous, you can already disable dumping and run temacs directly by setting CANNOT_DUMP=yes at compile time. Be warned, though, that a non-dumped Emacs takes several seconds, or worse, to initialize before it even begins loading your own configuration. It’s also somewhat buggy since it seems nobody ever runs it this way productively.

The other major way Emacs users have worked around slow loading is aggressive use of lazy loading, generally via autoloads. The major package interactive entry points are defined ahead of time as stub functions. These stubs, when invoked, load the full package, which overrides the stub definition, then finally the stub re-invokes the new definition with the same arguments.

To further assist with lazy loading, an evaluated defvar form will not override an existing global variable binding. This means you can, to a certain extent, configure a package before it’s loaded. The package will not clobber any existing configuration when it loads. This also explains the bizarre interfaces for the various hook functions, like add-hook and run-hooks. These accept symbols — the names of the variables — rather than values of those variables as would normally be the case. The add-to-list function does the same thing. It’s all intended to cooperate with lazy loading, where the variable may not have been defined yet.


Sometimes this isn’t enough and you need some some configuration to take place after the package has been loaded, but without forcing it to load early. That is, you need to tell Emacs “evaluate this code after this particular package loads.” That’s where eval-after-load comes into play, except for its fatal flaw: it takes the word “eval” completely literally.

The first argument to eval-after-load is the name of a package. Fair enough. The second argument is a form that will be passed to eval after that package is loaded. Now hold on a minute. The general rule of thumb is that if you’re calling eval, you’re probably doing something seriously wrong, and this function is no exception. This is completely the wrong mechanism for the task.

The second argument should have been a function — either a (sharp quoted) symbol or a function object. And then instead of eval it would be something more sensible, like funcall. Perhaps this improved version would be named call-after-load or run-after-load.

The big problem with passing an s-expression is that it will be left uncompiled due to being quoted. I’ve talked before about the importance of evaluating your lambdas. eval-after-load not only encourages badly written Emacs Lisp, it demands it.

;;; BAD!
(eval-after-load 'simple-httpd
                 '(push '("c" . "text/plain") httpd-mime-types))

This was all corrected in Emacs 25. If the second argument to eval-after-load is a function — the result of applying functionp is non-nil — then it uses funcall. There’s also a new macro, with-eval-after-load, to package it all up nicely.

;;; Better (Emacs >= 25 only)
(eval-after-load 'simple-httpd
  (lambda ()
    (push '("c" . "text/plain") httpd-mime-types)))

;;; Best (Emacs >= 25 only)
(with-eval-after-load 'simple-httpd
  (push '("c" . "text/plain") httpd-mime-types))

Though in both of these examples the compiler will likely warn about httpd-mime-types not being defined. That’s a problem for another day.

A workaround

But what if you need to use Emacs 24, as was the situation that sparked this article? What can we do with the bad version of eval-after-load? We could situate a lambda such that it’s evaluated, but then smuggle the resulting function object into the form passed to eval-after-load, all using a backquote.

;;; Note: this is subtly broken
(eval-after-load 'simple-httpd
    ,(lambda ()
       (push '("c" . "text/plain") httpd-mime-types)))

When everything is compiled, the backquoted form evalutes to this:

(funcall #[0 <bytecode> [httpd-mime-types ("c" . "text/plain")] 2])

Where the second value (#[...]) is a byte-code object. However, as the comment notes, this is subtly broken. A cleaner and correct way to solve all this is with a named function. The damage caused by eval-after-load will have been (mostly) minimized.

(defun my-simple-httpd-hook ()
  (push '("c" . "text/plain") httpd-mime-types))

(eval-after-load 'simple-httpd
  '(funcall #'my-simple-httpd-hook))

But, let’s go back to the anonymous function solution. What was broken about it? It all has to do with evaluating function objects.

Evaluating function objects

So what happens when we evaluate an expression like the one above with eval? Here’s what it looks like again.

(funcall #[...])

First, eval notices it’s been given a non-empty list, so it’s probably a function call. The first argument is the name of the function to be called (funcall) and the remaining elements are its arguments. But each of these elements must be evaluated first, and the result of that evaluation becomes the arguments.

Any value that isn’t a list or a symbol is self-evaluating. That is, it evaluates to its own value:

(eval 10)
;; => 10

If the value is a symbol, it’s treated as a variable. If the value is a list, it goes through the function call process I’m describing (or one of a number of other special cases, such as macro expansion, lambda expressions, and special forms).

So, conceptually eval recurses on the function object #[...]. A function object is not a list or a symbol, so it’s self-evaluating. No problem.

;; Byte-code objects are self-evaluating

(let ((x (byte-compile (lambda ()))))
  (eq x (eval x)))
;; => t

What if this code wasn’t compiled? Rather than a byte-code object, we’d have some other kind of function object for the interpreter. Let’s examine the dynamic scope (shudder) case. Here, a lambda appears to evaluate to itself, but appearances can be deceiving:

(eval (lambda ())
;; => (lambda ())

However, this is not self-evaluation. Lambda expressions are not self-evaluating. It’s merely coincidence that the result of evaluating a lambda expression looks like the original expression. This is just how the Emacs Lisp interpreter is currently implemented and, strictly speaking, it’s an implementation detail that just so happens to be mostly compatible with byte-code objects being self-evaluating. It would be a mistake to rely on this.

Instead, dynamic scope lambda expression evaluation is idempotent. Applying eval to the result will return an equal, but not identical (eq), expression. In contrast, a self-evaluating value is also idempotent under evaluation, but with eq results.

;; Not self-evaluating:

(let ((x '(lambda ())))
  (eq x (eval x)))
;; => nil

;; Evaluation is idempotent:

(let ((x '(lambda ())))
  (equal x (eval x)))
;; => t

(let ((x '(lambda ())))
  (equal x (eval (eval x))))
;; => t

So, with dynamic scope, the subtly broken backquote example will still work, but only by sheer luck. Under lexical scope, the situation isn’t so lucky:

;;; -*- lexical-scope: t; -*-

(lambda ())
;; => (closure (t) nil)

These interpreted lambda functions are neither self-evaluating nor idempotent. Passing t as the second argument to eval tells it to use lexical scope, as shown below:

;; Not self-evaluating:

(let ((x '(lambda ())))
  (eq x (eval x t)))
;; => nil

;; Not idempotent:

(let ((x '(lambda ())))
  (equal x (eval x t)))
;; => nil

(let ((x '(lambda ())))
  (equal x (eval (eval x t) t)))
;; error: (void-function closure)

I can imagine an implementation of Emacs Lisp where dynamic scope lambda expressions are in the same boat, where they’re not even idempotent. For example:

;;; -*- lexical-binding: nil; -*-

(lambda ())
;; => (totally-not-a-closure ())

Most Emacs Lisp would work just fine under this change, and only code that makes some kind of logical mistake — where there’s nested evaluation of lambda expressions — would break. This essentially already happened when lots of code was quietly switched over to lexical scope after Emacs 24. Lambda idempotency was lost and well-written code didn’t notice.

There’s a temptation here for Emacs to define a closure function or special form that would allow interpreter closure objects to be either self-evaluating or idempotent. This would be a mistake. It would only serve as a hack that covers up logical mistakes that lead to nested evaluation. Much better to catch those problems early.

Solving the problem with one character

So how do we fix the subtly broken example? With a strategically placed quote right before the comma.

(eval-after-load 'simple-httpd
    ',(lambda ()
        (push '("c" . "text/plain") httpd-mime-types)))

So the form passed to eval-after-load becomes:

;; Compiled:
(funcall (quote #[...]))

;; Dynamic scope:
(funcall (quote (lambda () ...)))

;; Lexical scope:
(funcall (quote (closure (t) () ...)))

The quote prevents eval from evaluating the function object, which would be either needless or harmful. There’s also an argument to be made that this is a perfect situation for a sharp-quote (#'), which exists to quote functions.

Two Chaotic Motion Demos

I’ve put together two online, interactive, demonstrations of chaotic motion. One is 2D and the other is 3D, but both are rendered using WebGL — which, for me, is the most interesting part. Both are governed by ordinary differential equations. Both are integrated using the Runge–Kutta method, specifically RK4.

Far more knowledgeable people have already written introductions for chaos theory, so here’s just a quick summary. A chaotic system is deterministic but highly sensitive to initial conditions. Tweaking a single bit of the starting state of either of my demos will quickly lead to two arbitrarily different results. Both demonstrations have features that aim to show this in action.

This ain’t my first chaotic system rodeo. About eight years ago I made water wheel Java applet, and that was based on some Matlab code I collaborated on some eleven years ago. I really hope you’re not equipped to run a crusty old Java applet in 2018, though. (Update: now upgraded to HTML5 Canvas.)

If you want to find either of these demos again in the future, you don’t need to find this article first. They’re both listed in my Showcase page, linked from the header of this site.

Double pendulum

First up is the classic double pendulum. This one’s more intuitive than my other demo since it’s modeling a physical system you could actually build and observe in the real world.

Source: https://github.com/skeeto/double-pendulum

I lifted the differential equations straight from the Wikipedia article (derivative() in my code). Same for the Runge–Kutta method (rk4() in my code). It’s all pretty straightforward. RK4 may not have been the best choice for this system since it seems to bleed off energy over time. If you let my demo run over night, by morning there will obviously be a lot less activity.

I’m not a fan of buttons and other fancy GUI widgets — neither designing them nor using them myself — prefering more cryptic, but easy-to-use keyboard-driven interfaces. (Hey, it works well for mpv and MPlayer.) I haven’t bothered with a mobile interface, so sorry if you’re reading on your phone. You’ll just have to enjoy watching a single pendulum.

Here are the controls:

To witness chaos theory in action:

  1. Start with a single pendulum (the default).
  2. Pause the simulation (SPACE).
  3. Make a dozen or so clones (press c for awhile).
  4. Unpause.

At first it will appear as single pendulum, but they’re actually all stacked up, each starting from slightly randomized initial conditions. Within a minute you’ll witness the pendulums diverge, and after a minute they’ll all be completely different. It’s pretty to watch them come apart at first.

It might appear that the m key doesn’t actually do anything. That’s because the HTML5 Canvas rendering — which is what I actually implemented first — is really close to the WebGL rendering. I’m really proud of this. There are just three noticeable differences. First, there’s a rounded line cap in the Canvas rendering where the pendulum is “attached.” Second, the tail line segments aren’t properly connected in the Canvas rendering. The segments are stroked separately in order to get that gradient effect along its path. Third, it’s a lot slower, particularly when there are many pendulums to render.

In WebGL the two “masses” are rendered using that handy old circle rasterization technique on a quad. Either a triangle fan or pre-rendering the circle as a texture would probably have been a better choices. The two bars are the same quad buffers, just squeezed and rotated into place. Both were really simple to create. It’s the tail that was tricky to render.

When I wrote the original Canvas renderer, I set the super convenient lineWidth property to get a nice, thick tail. In my first cut at rendering the tail I used GL_LINE_STRIP to draw a line primitive. The problem with the line primitive is that an OpenGL implementation is only required to support single pixel wide lines. If I wanted wider, I’d have to generate the geometry myself. So I did.

Like before, I wasn’t about to dirty my hands manipulating a graphite-filled wooden stick on a piece of paper to solve this problem. No, I lifted the math from something I found on the internet again. In this case it was a forum post by paul.houx, which provides a few vector equations to compute a triangle strip from a line strip. My own modification was to add a miter limit, which keeps sharp turns under control. You can find my implementation in polyline() in my code. Here’s a close-up with the skeleton rendered on top in black:

For the first time I’m also using ECMAScript’s new template literals to store the shaders inside the JavaScript source. These string literals can contain newlines, but, even cooler, I it does string interpolation, meaning I can embed JavaScript variables directly into the shader code:

let massRadius = 0.12;
let vertexShader = `
attribute vec2 a_point;
uniform   vec2 u_center;
varying   vec2 v_point;

void main() {
    v_point = a_point;
    gl_Position = vec4(a_point * ${massRadius} + u_center, 0, 1);

Allocation avoidance

If you’ve looked at my code you might have noticed something curious. I’m using a lot of destructuring assignments, which is another relatively new addition to ECMAScript. This was part of a little experiment.

function normalize(v0, v1) {
    let d = Math.sqrt(v0 * v0 + v1 * v1);
    return [v0 / d, v1 / d];

/* ... */

let [nx, ny] = normalize(-ly, lx);

One of my goals for this project was zero heap allocations in the main WebGL rendering loop. There are no garbage collector hiccups if there’s no garbage to collect. This sort of thing is trivial in a language with manual memory management, such as C and C++. Just having value semantics for aggregates would be sufficient.

But with JavaScript I don’t get to choose how my objects are allocated. I either have to pre-allocate everything, including space for all the intermediate values (e.g. an object pool). This would be clunky and unconventional. Or I can structure and access my allocations in such a way that the JIT compiler can eliminate them (via escape analysis, scalar replacement, etc.).

In this case, I’m trusting that JavaScript implementations will flatten these destructuring assignments so that the intermediate array never actually exists. It’s like pretending the array has value semantics. This seems to work as I expect with V8, but not so well with SpiderMonkey (yet?), at least in Firefox 52 ESR.

Single precision

I briefly considered using Math.fround() to convince JavaScript to compute all the tail geometry in single precision. The double pendulum system would remain double precision, but the geometry doesn’t need all that precision. It’s all rounded to single precision going out to the GPU anyway.

Normally when pulling values from a Float32Array, they’re cast to double precision — JavaScript’s only numeric type — and all operations are performed in double precision, even if the result is stored back in a Float32Array. This is because the JIT compiler is required to correctly perform all the intermediate rounding. To relax this requirement, surround each operation with a call to Math.fround(). Since the result of doing each operation in double precision with this rounding step in between is equivalent to doing each operation in single precision, the JIT compiler can choose to do the latter.

let x = new Float32Array(n);
let y = new Float32Array(n);
let d = new Float32Array(n);
// ...
for (let i = 0; i < n; i++) {
    let xprod = Math.fround(x[i] * x[i]);
    let yprod = Math.fround(y[i] * y[i]);
    d[i] = Math.sqrt(Math.fround(xprod + yprod));

I ultimately decided not to bother with this since it would significantly obscures my code for what is probably a minuscule performance gain (in this case). It’s also really difficult to tell if I did it all correctly. So I figure this is better suited for compilers that target JavaScript rather than something to do by hand.

Lorenz system

The other demo is a Lorenz system with its famous butterfly pattern. I actually wrote this one a year and a half ago but never got around to writing about it. You can tell it’s older because I’m still using var.

Source: https://github.com/skeeto/lorenz-webgl

Like before, the equations came straight from the Wikipedia article (Lorenz.lorenz() in my code). They math is a lot simpler this time, too.

This one’s a bit more user friendly with a side menu displaying all your options. The keys are basically the same. This was completely by accident, I swear. Here are the important ones:

Witnessing chaos theory in action is the same process as before: clear it down to a single solution (C then a), then add a bunch of randomized clones (c).

There is no Canvas renderer for this one. It’s pure WebGL. The tails are drawn using GL_LINE_STRIP, but in this case it works fine that they’re a single pixel wide. If heads are turned on, those are just GL_POINT. The geometry is threadbare for this one.

There is one notable feature: The tails are stored exclusively in GPU memory. Only the “head” is stored CPU-side. After it computes the next step, it updates a single spot of the tail with glBufferSubData(), and the VBO is actually a circular buffer. OpenGL doesn’t directly support rendering from circular buffers, but it does have element arrays. An element array is an additional buffer of indices that tells OpenGL what order to use the elements in the other buffers.

Naively would mean for a tail of 4 segments, I need 4 different element arrays, one for each possible rotation:

array 0: 0 1 2 3
array 1: 1 2 3 0
array 2: 2 3 0 1
array 3: 3 0 1 2

With the knowledge that element arrays can start at an offset, and with a little cleverness, you might notice these can all overlap in a single, 7-element array:

0 1 2 3 0 1 2

Array 0 is at offset 0, array 1 is at offset 1, array 2 is at offset 2, and array 3 is at offset 3. The tails in the Lorenz system are drawn using drawElements() with exactly this sort of array.

Like before, I was very careful to produce zero heap allocations in the main loop. The FPS counter generates some garbage in the DOM due to reflow, but this goes away if you hide the help menu (?). This was long enough ago that destructuring assignment wasn’t available, but Lorenz system and rendering it were so simple that using pre-allocated objects worked fine.

Beyond just the programming, I’ve gotten hours of entertainment playing with each of these systems. This was also the first time I’ve used WebGL in over a year, and this project was a reminder of just how working with it is so pleasurable. The specification is superbly written and serves perfectly as its own reference.

Options for Structured Data in Emacs Lisp

This article has been translated into Russian by ClipArtMag.

So your Emacs package has grown beyond a dozen or so lines of code, and the data it manages is now structured and heterogeneous. Informal plain old lists, the bread and butter of any lisp, are not longer cutting it. You really need to cleanly abstract this structure, both for your own organizational sake any for anyone reading your code.

With informal lists as structures, you might regularly ask questions like, “Was the ‘name’ slot stored in the third list element, or was it the fourth element?” A plist or alist helps with this problem, but those are better suited for informal, externally-supplied data, not for internal structures with fixed slots. Occasionally someone suggests using hash tables as structures, but Emacs Lisp’s hash tables are much too heavy for this. Hash tables are more appropriate when keys themselves are data.

Defining a data structure from scratch

Imagine a refrigerator package that manages a collection of food in a refrigerator. A food item could be structured as a plain old list, with slots at specific positions.

(defun fridge-item-create (name expiry weight)
  (list name expiry weight))

A function that computes the mean weight of a list of food items might look like this:

(defun fridge-mean-weight (items)
  (if (null items)
    (let ((sum 0.0)
          (count 0))
      (dolist (item items (/ sum count))
        (setf count (1+ count)
              sum (+ sum (nth 2 item)))))))

Note the use of (nth 2 item) at the end, used to get the item’s weight. That magic number 2 is easy to mess up. Even worse, if lots of code accesses “weight” this way, then future extensions will be inhibited. Defining some accessor functions solves this problem.

(defsubst fridge-item-name (item)
  (nth 0 item))

(defsubst fridge-item-expiry (item)
  (nth 1 item))

(defsubst fridge-item-weight (item)
  (nth 2 item))

The defsubst defines an inline function, so there’s effectively no additional run-time costs for these accessors compared to a bare nth. Since these only cover getting slots, we should also define some setters using the built-in gv (generalized variable) package.

(require 'gv)

(gv-define-setter fridge-item-name (value item)
  `(setf (nth 0 ,item) ,value))

(gv-define-setter fridge-item-expiry (value item)
  `(setf (nth 1 ,item) ,value))

(gv-define-setter fridge-item-weight (value item)
  `(setf (nth 2 ,item) ,value))

This makes each slot setf-able. Generalized variables are great for simplifying APIs, since otherwise there would need to be an equal number of setter functions (fridge-item-set-name, etc.). With generalized variables, both are at the same entrypoint:

(setf (fridge-item-name item) "Eggs")

There are still two more significant improvements.

  1. As far as Emacs Lisp is concerned, this isn’t a real type. The type-ness of it is just a fiction created by the conventions of the package. It would be easy to make the mistake of passing an arbitrary list to these fridge-item functions, and the mistake wouldn’t be caught so long as that list has at least three items. An common solution is to add a type tag: a symbol at the beginning of the structure that identifies it.

  2. It’s still a linked list, and nth has to walk the list (i.e. O(n)) to retrieve items. It would be much more efficient to use a vector, turning this into an efficient O(1) operation.

Addressing both of these at once:

(defun fridge-item-create (name expiry weight)
  (vector 'fridge-item name expiry weight))

(defsubst fridge-item-p (object)
  (and (vectorp object)
       (= (length object) 4)
       (eq 'fridge-item (aref object 0))))

(defsubst fridge-item-name (item)
  (unless (fridge-item-p item)
    (signal 'wrong-type-argument (list 'fridge-item item)))
  (aref item 1))

(defsubst fridge-item-name--set (item value)
  (unless (fridge-item-p item)
    (signal 'wrong-type-argument (list 'fridge-item item)))
  (setf (aref item 1) value))

(gv-define-setter fridge-item-name (value item)
  `(fridge-item-name--set ,item ,value))

;; And so on for expiry and weight...

As long as fridge-mean-weight uses the fridge-item-weight accessor, it continues to work unmodified across all these changes. But, whew, that’s quite a lot of boilerplate to write and maintain for each data structure in our package! Boilerplate code generation is a perfect candidate for a macro definition. Luckily for us, Emacs already defines a macro to generate all this code: cl-defstruct.

(require 'cl)

(cl-defstruct fridge-item
  name expiry weight)

In Emacs 25 and earlier, this innocent looking definition expands into essentially all the above code. The code it generates is expressed in the most optimal form for its version of Emacs, and it exploits many of the available optimizations by using function declarations such as side-effect-free and error-free. It’s configurable, too, allowing for the exclusion of a type tag (:named) — discarding all the type checks — or using a list rather than a vector as the underlying structure (:type). As a crude form of structural inheritance, it even allows for directly embedding other structures (:include).

Two pitfalls

There a couple pitfalls, though. First, for historical reasons, the macro will define two namespace-unfriendly functions: make-NAME and copy-NAME. I always override these, preferring the -create convention for the constructor, and tossing the copier since it’s either useless or, worse, semantically wrong.

(cl-defstruct (fridge-item (:constructor fridge-item-create)
                           (:copier nil))
  name expiry weight)

If the constructor needs to be more sophisticated than just setting slots, it’s common to define a “private” constructor (double dash in the name) and wrap it with a “public” constructor that has some behavior.

(cl-defstruct (fridge-item (:constructor fridge-item--create)
                           (:copier nil))
  name expiry weight entry-time)

(cl-defun fridge-item-create (&rest args)
  (apply #'fridge-item--create :entry-time (float-time) args))

The other pitfall is related to printing. In Emacs 25 and earlier, types defined by cl-defstruct are still only types by convention. They’re really just vectors as far as Emacs Lisp is concerned. One benefit from this is that printing and reading these structures is “free” because vectors are printable. It’s trivial to serialize cl-defstruct structures out to a file. This is exactly how the Elfeed database works.

The pitfall is that once a structure has been serialized, there’s no more changing the cl-defstruct definition. It’s now a file format definition, so the slots are locked in place. Forever.

Emacs 26 throws a wrench in all this, though it’s worth it in the long run. There’s a new primitive type in Emacs 26 with its own reader syntax: records. This is similar to hash tables becoming first class in the reader in Emacs 23.2. In Emacs 26, cl-defstruct uses records instead of vectors.

;; Emacs 25:
(fridge-item-create :name "Eggs" :weight 11.1)
;; => [cl-struct-fridge-item "Eggs" nil 11.1]

;; Emacs 26:
(fridge-item-create :name "Eggs" :weight 11.1)
;; => #s(fridge-item "Eggs" nil 11.1)

So far slots are still accessed using aref, and all the type checking still happens in Emacs Lisp. The only practical change is the record function is used in place of the vector function when allocating a structure. But it does pave the way for more interesting things in the future.

The major short-term downside is that this breaks printed compatibility across the Emacs 25/26 boundary. The cl-old-struct-compat-mode function can be used for some degree of backwards, but not forwards, compatibility. Emacs 26 can read and use some structures printed by Emacs 25 and earlier, but the reverse will never be true. This issue initially tripped up Emacs’ built-in packages, and when Emacs 26 is released we’ll see more of these issues arise in external packages.

Dynamic dispatch

Prior to Emacs 25, the major built-in package for dynamic dispatch — functions that specialize on the run-time type of their arguments — was EIEIO, though it only supported single dispatch (specializing on a single argument). EIEIO brought much of the Common Lisp Object System (CLOS) to Emacs Lisp, including classes and methods.

Emacs 25 introduced a more sophisticated dynamic dispatch package called cl-generic. It focuses only on dynamic dispatch and supports multiple dispatch, completely replacing the dynamic dispatch portion of EIEIO. Since cl-defstruct does inheritance and cl-generic does dynamic dispatch, there’s not really much left for EIEIO — besides bad ideas like multiple inheritance and method combination.

Without either of these packages, the most direct way to build single dispatch on top of cl-defstruct would be to shove a function in one of the slots. Then the “method” is just a wrapper that call this function.

;; Base "class"

(cl-defstruct greeter

(defun greet (thing)
  (funcall (greeter-greeting thing) thing))

;; Cow "class"

(cl-defstruct (cow (:include greeter)
                   (:constructor cow--create)))

(defun cow-create ()
  (cow--create :greeting (lambda (_) "Moo!")))

;; Bird "class"

(cl-defstruct (bird (:include greeter)
                    (:constructor bird--create)))

(defun bird-create ()
  (bird--create :greeting (lambda (_) "Chirp!")))

;; Usage:

(greet (cow-create))
;; => "Moo!"

(greet (bird-create))
;; => "Chirp!"

Since cl-generic is aware of the types created by cl-defstruct, functions can specialize on them as if they were native types. It’s a lot simpler to let cl-generic do all the hard work. The people reading your code will appreciate it, too:

(require 'cl-generic)

(cl-defgeneric greet (greeter))

(cl-defstruct cow)

(cl-defmethod greet ((_ cow))

(cl-defstruct bird)

(cl-defmethod greet ((_ bird))

(greet (make-cow))
;; => "Moo!"

(greet (make-bird))
;; => "Chirp!"

The majority of the time a simple cl-defstruct will fulfill your needs, keeping in mind the gotcha with the constructor and copier names. Its use should feel almost as natural as defining functions.

Inspiration from Data-dependent Rotations

This article is an expanded email I wrote in response to a question from Frank Muller. He asked how I arrived at my solution to a branchless UTF-8 decoder:

I mean, when you started, I’m pretty the initial solution was using branches, right? Then, you’ve applied some techniques to eliminate them.

A bottom-up approach that begins with branches and then proceeds to eliminate them one at a time sounds like a plausible story. However, this story is the inverse of how it actually played out. It began when I noticed a branchless decoder could probably be done, then I put together the pieces one at a time without introducing any branches. But what sparked that initial idea?

The two prior posts reveal my train of thought at the time: a look at the Blowfish cipher and a 64-bit PRNG shootout. My layman’s study of Blowfish was actually part of an examination of a number of different block ciphers. For example, I also read the NSA’s Speck and Simon paper and then implemented the 128/128 variant of Speck — a 128-bit key and 128-bit block. I didn’t take the time to write an article about it, but note how the entire cipher — key schedule, encryption, and decryption — is just 40 lines of code:

struct speck {
    uint64_t k[32];

speck_init(struct speck *ctx, uint64_t x, uint64_t y)
    ctx->k[0] = y;
    for (uint64_t i = 0; i < 31; i++) {
        x = (x >> 8) | (x << 56);
        x += y;
        x ^= i;
        y = (y << 3) | (y >> 61);
        y ^= x;
        ctx->k[i + 1] = y;

speck_encrypt(const struct speck *ctx, uint64_t *x, uint64_t *y)
    for (int i = 0; i < 32; i++) {
        *x = (*x >> 8) | (*x << 56);
        *x += *y;
        *x ^= ctx->k[i];
        *y = (*y << 3) | (*y >> 61);
        *y ^= *x;

static void
speck_decrypt(const struct speck *ctx, uint64_t *x, uint64_t *y)
    for (int i = 0; i < 32; i++) {
        *y ^= *x;
        *y = (*y >> 3) | (*y << 61);
        *x ^= ctx->k[31 - i];
        *x -= *y;
        *x = (*x << 8) | (*x >> 56);

Isn’t that just beautiful? It’s so tiny and fast. Other than the not-very-arbitrary selection of 32 rounds, and the use of 3-bit and 8-bit rotations, there are no magic values. One could fairly reasonably commit this cipher to memory if necessary, similar to the late RC4. Speck is probably my favorite block cipher right now, except that I couldn’t figure out the key schedule for any of the other key/block size variants.

Another cipher I studied, though in less depth, was RC5 (1994), a block cipher by (obviously) Ron Rivest. The most novel part of RC5 is its use of data dependent rotations. This was a very deliberate decision, and the paper makes this clear:

RC5 should highlight the use of data-dependent rotations, and encourage the assessment of the cryptographic strength data-dependent rotations can provide.

What’s a data-dependent rotation. In the Speck cipher shown above, notice how the right-hand side of all the rotation operations is a constant (3, 8, 56, and 61). Suppose that these operands were not constant, instead they were based on some part of the value of the block:

    int r = *y & 0x0f;
    *x = (*x >> r) | (*x << (64 - r));

Such “random” rotations “frustrate differential cryptanalysis” according to the paper, increasing the strength of the cipher.

Another algorithm that uses data-dependent shift is the PCG family of PRNGs. Honestly, the data-dependent “permutation” shift is the defining characteristic of PCG. As a reminder, here’s the simplified PCG from my shootout:

spcg32(uint64_t s[1])
    uint64_t m = 0x9b60933458e17d7d;
    uint64_t a = 0xd737232eeccdf7ed;
    *s = *s * m + a;
    int shift = 29 - (*s >> 61);
    return *s >> shift;

Notice how the final shift depends on the high order bits of the PRNG state. (This one weird trick from Melissa O’Neil will significantly improve your PRNG. Xorshift experts hate her.)

I think this raises a really interesting question: Why did it take until 2014 for someone to apply a data-dependent shift to a PRNG? Similarly, why are data-dependent rotations not used in many ciphers?

My own theory is that this is because many older instruction set architectures can’t perform data-dependent shift operations efficiently.

Many instruction sets only have a fixed shift (e.g. 1-bit), or can only shift by an immediate (e.g. a constant). In these cases, a data-dependent shift would require a loop. These loops would be a ripe source of side channel attacks in ciphers due to the difficultly of making them operate in constant time. It would also be relatively slow for video game PRNGs, which often needed to run on constrained hardware with limited instruction sets. For example, the 6502 (Atari, Apple II, NES, Commodore 64) and the Z80 (too many to list) can only shift/rotate one bit at a time.

Even on an architecture with an instruction for data-dependent shifts, such as the x86, those shifts will be slower than constant shifts, at least in part due to the additional data dependency.

It turns out there are also some patent issues (ex. 1, 2). Fortunately most of these patents have now expired, and one in particular is set to expire this June. I still like my theory better.

To branchless decoding

So I was thinking about data-dependent shifts, and I had also noticed I could trivially check the length of a UTF-8 code point using a small lookup table — the first step in my decoder. What if I combined these: a data-dependent shift based on a table lookup. This would become the last step in my decoder. The idea for a branchless UTF-8 decoder was essentially borne out of connecting these two thoughts, and then filling in the middle.

Debugging Emacs or: How I Learned to Stop Worrying and Love DTrace

Update: This article was featured on BSD Now 233 (starting at 21:38).

For some time Elfeed was experiencing a strange, spurious failure. Every so often users were seeing an error (spoiler warning) when updating feeds: “error in process sentinel: Search failed.” If you use Elfeed, you might have even seen this yourself. From the surface it appeared that curl, tasked with the responsibility for downloading feed data, was producing incomplete output despite reporting a successful run. Since the run was successful, Elfeed assumed certain data was in curl’s output buffer, but, since it wasn’t, it failed hard.

Unfortunately this issue was not reproducible. Manually running curl outside of Emacs never revealed any issues. Asking Elfeed to retry fetching the feeds would work fine. The issue would only randomly rear its head when Elfeed was fetching many feeds in parallel, under stress. By the time the error was discovered, the curl process had exited and vital debugging information was lost. Considering that this was likely to be a bug in Emacs itself, there really wasn’t a reliable way to capture the necessary debugging information from within Emacs Lisp. And, indeed, this later proved to be the case.

A quick-and-dirty work around is to use condition-case to catch and swallow the error. When the bizarre issue shows up, rather than fail badly in front of the user, Elfeed could attempt to swallow the error — assuming it can be reliably detected — and treat the fetch as simply a failure. That didn’t sit comfortably with me. Elfeed had done its due diligence checking for errors already. Someone was lying to Elfeed, and I intended to catch them with their pants on fire. Someday.

I’d just need to witness the bug on one of my own machines. Elfeed is part of my daily routine, so surely I’d have to experience this issue myself someday. My plan was, should that day come, to run a modified Elfeed, instrumented to capture extra data. I would have also routinely run Emacs under GDB so that I could inspect the failure more deeply.

For now I just had to wait to hunt that zebra.

Bryan Cantrill, DTrace, and FreeBSD

Over the holidays I re-discovered Bryan Cantrill, a systems software engineer who worked for Sun between 1996 and 2010, and is most well known for DTrace. My first exposure to him was in a BSD Now interview in 2015. I had re-watched that interview and decided there was a lot more I had to learn from him. He’s become a personal hero to me. So I scoured the internet for more of his writing and talks. Besides what I’ve already linked in this article, here are a couple more great presentations:

You can also find some of his writing scattered around the DTrace blog.

Some interesting operating system technology came out of Sun during its final 15 or so years — most notably DTrace and ZFS — and Bryan speaks about it passionately. Almost as a matter of luck, most of it survived the Oracle acquisition thanks to Sun releasing it as open source in just the nick of time. Otherwise it would have been lost forever. The scattered ex-Sun employees, still passionate about their prior work at Sun, along with some of their old customers have since picked up the pieces and kept going as a community under the name illumos. It’s like an open source flotilla.

Naturally I wanted to get my hands on this stuff to try it out for myself. Is it really as good as they say? Normally I stick to Linux, but it (generally) doesn’t have these Sun technologies. The main reason is license incompatibility. Sun released its code under the CDDL, which is incompatible with the GPL. Ubuntu does infamously include ZFS, but other distributions are unwilling to take that risk. Porting DTrace is a serious undertaking since it’s got its fingers throughout the kernel, which also makes the licensing issues even more complicated.

(Update Feburary 2018: DTrace has been released under the GPLv2, allowing it to be legally integrated with Linux.)

Linux has a reputation for Not Invented Here (NIH) syndrome, and these licensing issues certainly contribute to that. Rather than adopt ZFS and DTrace, they’ve been reinvented from scratch: btrfs instead of ZFS, and a slew of partial options instead of DTrace. Normally I’m most interested in system call tracing, and my go to is strace, though it certainly has its limitations — including this situation of debugging curl under Emacs. Another famous example of NIH is Linux’s epoll(2), which is a broken version of BSD kqueue(2).

So, if I want to try these for myself, I’ll need to install a different operating system. I’ve dabbled with OmniOS, an OS built on illumos, in virtual machines, using it as an alien environment to test some of my software (e.g. enchive). OmniOS has a philosophy called Keep Your Software To Yourself (KYSTY), which is really just code for “we don’t do packaging.” Honestly, you can’t blame them since they’re a tiny community. The best solution to this is probably pkgsrc, which is essentially a universal packaging system. Otherwise you’re on your own.

There’s also openindiana, which is a more friendly desktop-oriented illumos distribution. Still, the short of it is that you’re very much on your own when things don’t work. The situation is like running Linux a couple decades ago, when it was still difficult to do.

If you’re interested in trying DTrace, the easiest option these days is probably FreeBSD. It’s got a big, active community, thorough documentation, and a huge selection of packages. Its license (the BSD license, duh) is compatible with the CDDL, so both ZFS and DTrace have been ported to FreeBSD.

What is DTrace?

I’ve done all this talking but haven’t yet described what DTrace really is. I won’t pretend to write my own tutorial, but I’ll provide enough information to follow along. DTrace is a tracing framework for debugging production systems in real time, both for the kernel and for applications. The “production systems” part means it’s stable and safe — using DTrace won’t put your system at risk of crashing or damaging data. The “real time” part means it has little impact on performance. You can use DTrace on live, active systems with little impact. Both of these core design principles are vital for troubleshooting those really tricky bugs that only show up in production.

There are DTrace probes scattered all throughout the system: on system calls, scheduler events, networking events, process events, signals, virtual memory events, etc. Using a specialized language called D (unrelated to the general purpose programming language D), you can dynamically add behavior at these instrumentation points. Generally the behavior is to capture information, but it can also manipulate the event being traced.

Each probe is fully identified by a 4-tuple delimited by colons: provider, module, function, and probe name. An empty element denotes a sort of wildcard. For example, syscall::open:entry is a probe at the beginning (i.e. “entry”) of open(2). syscall:::entry matches all system call entry probes.

Unlike strace on Linux which monitors a specific process, DTrace applies to the entire system when active. To run curl under strace from Emacs, I’d have to modify Emacs’ behavior to do so. With DTrace I can instrument every curl process without making a single change to Emacs, and with negligible impact to Emacs. That’s a big deal.

So, when it comes to this Elfeed issue, FreeBSD is much better poised for debugging the problem. All I have to do is catch it in the act. However, it’s been months since that bug report and I’m not really making this connection yet. I’m just hoping I eventually find an interesting problem where I can apply DTrace.

FreeBSD on a Raspberry Pi 2

So I’ve settled in FreeBSD as the playground for these technologies, I just have to decide where. I could always run it in a virtual machine, but it’s always more interesting to try things out on real hardware. FreeBSD supports the Raspberry Pi 2 as a Tier 2 system, and I had a Raspberry Pi 2 sitting around collecting dust, so I put it to use.

I wrote the image to an SD card, and for a few days I stretched my legs on this new system. I cloned a couple dozen of my own git repositories, ran the builds and the tests, and just got a feel for things. I tried out the ports system for the first time, mainly to discover that the low-powered Raspberry Pi 2 takes days to build some of the packages I want to try.

I mostly program in Vim these days, so it’s some days before I even set up Emacs. Eventually I do build Emacs, clone my configuration, fire it up, and give Elfeed a spin.

And that’s when the “search failed” bug strikes! Not just once, but dozens of times. Perfect! This low-powered platform is the jackpot for this particular bug, triggering it left and right. Given that I’ve got DTrace at my disposal, it’s the perfect place to debug this. Something is lying to Elfeed and DTrace will play the judge.

Before I dive in I see three possibilities:

  1. curl is reporting success but truncating its output.
  2. Emacs is quietly truncating curl’s output.
  3. Emacs is misinterpreting curl’s exit status.

With Dtrace I can observe what every curl process writes to Emacs, and I can also double check curl’s exit status. I come up with the following (newbie) DTrace script:

/execname == "curl"/
    printf("%d WRITE %d \"%s\"\n",
           pid, arg2, stringof(copyin(arg1, arg2)));

/execname == "curl"/
    printf("%d EXIT  %d\n", pid, arg0);

The /execname == "curl"/ is a predicate that (obviously) causes the behavior to only fire for curl processes. The first probe has DTrace print a line for every write(2) from curl. arg0, arg1, and arg2 correspond to the arguments of write(2): fd, buf, count. It logs the process ID (pid) of the write, the length of the write, and the actual contents written. Remember that these curl processes are run in parallel by Emacs, so the pid allows me to associate the separate writes and the exit status.

The second probe prints the pid and the exit status (the first argument to exit(2)).

I also want to compare this to exactly what is delivered to Elfeed when curl exits, so I modify the process sentinel — the callback that handles a subprocess exiting — to call write-file before any action is taken. I can compare these buffer dumps to the logs produced by DTrace.

There are two important findings.

First, when the “search failed” bug occurs, the buffer was completely empty (95% of the time) or truncated at the end of the HTTP headers (5% of the time), right at the blank line. DTrace indicates that curl did its job to the full, so it’s Emacs who’s the liar. It’s not delivering all of curl’s data to Elfeed. That’s pretty annoying.

Second, curl was line-buffered. Each line was a separate, independent write(2). I was certainly not expecting this. Normally the C library only does line buffering when the output is a terminal. That’s because it’s guessing a user may be watching, expecting the output to arrive a line at a time.

Here’s a sample of what it looked like in the log:

88188 WRITE 32 "Server: Apache/2.4.18 (Ubuntu)
88188 WRITE 46 "Location: https://blog.plover.com/index.atom
88188 WRITE 21 "Content-Length: 299
88188 WRITE 45 "Content-Type: text/html; charset=iso-8859-1
88188 WRITE 2 "

Why would curl think Emacs is a terminal?

Oh. That’s right. This is the same problem I ran into four years ago when writing EmacSQL. By default Emacs connects to subprocesses through a psuedo-terminal (pty). I called this a mistake in Emacs back then, and I still stand by that claim. The pty causes weird, annoying problems for little benefit:

Just from eyeballing the DTrace log I knew what to do: dump the pty and switch to a pipe. This is controlled with the process-connection-type variable, and fixing it is a one-liner.

Not only did this completely resolve the truncation issue, Elfeed is noticeably faster at fetching feeds on all machines. It’s no longer receiving mountains of XML one line at a time, like sucking pudding through a straw. It’s now quite zippy even on my Raspberry Pi 2, which had never been the case before (without the “search failed” bug). Even if you were never affected by this bug, you will benefit from the fix.

I haven’t officially reported this as an Emacs bug yet because reproducibility is still an issue. It needs something better than “fire off a bunch of HTTP requests across the internet in parallel from a Raspberry Pi.”

The fix reminds me of that old boilermaker story about charging a lot of money just to swing a hammer. Once the problem arose, DTrace quickly helped to identify the place to hit Emacs with the hammer.

Finally, a big thanks to alphapapa for originally taking the time to report this bug months ago.

What's in an Emacs Lambda

There was recently some interesting discussion about correctly using backquotes to express a mixture of data and code. Since lambda expressions seem to evaluate to themselves, what’s the difference? For example, an association list of operations:

'((add . (lambda (a b) (+ a b)))
  (sub . (lambda (a b) (- a b)))
  (mul . (lambda (a b) (* a b)))
  (div . (lambda (a b) (/ a b))))

It looks like it would work, and indeed it does work in this case. However, there are good reasons to actually evaluate those lambda expressions. Eventually invoking the lambda expressions in the quoted form above are equivalent to using eval. So, instead, prefer the backquote form:

`((add . ,(lambda (a b) (+ a b)))
  (sub . ,(lambda (a b) (- a b)))
  (mul . ,(lambda (a b) (* a b)))
  (div . ,(lambda (a b) (/ a b))))

There are a lot of interesting things to say about this, but let’s first reduce it to two very simple cases:

(lambda (x) x)

'(lambda (x) x)

What’s the difference between these two forms? The first is a lambda expression, and it evaluates to a function object. The other is a quoted list that looks like a lambda expression, and it evaluates to a list — a piece of data.

A naive evaluation of these expressions in *scratch* (C-x C-e) suggests they are are identical, and so it would seem that quoting a lambda expression doesn’t really matter:

(lambda (x) x)
;; => (lambda (x) x)

'(lambda (x) x)
;; => (lambda (x) x)

However, there are two common situations where this is not the case: byte compilation and lexical scope.

Lambda under byte compilation

It’s a little trickier to evaluate these forms byte compiled in the scratch buffer since that doesn’t happen automatically. But if it did, it would look like this:

;;; -*- lexical-binding: nil; -*-

(lambda (x) x)
;; => #[(x) "\010\207" [x] 1]

'(lambda (x) x)
;; => (lambda (x) x)

The #[...] is the syntax for a byte-code function object. As discussed in detail in my byte-code internals article, it’s a special vector object that contains byte-code, and other metadata, for evaluation by Emacs’ virtual stack machine. Elisp is one of very few languages with readable function objects, and this feature is core to its ahead-of-time byte compilation.

The quote, by definition, prevents evaluation, and so inhibits byte compilation of the lambda expression. It’s vital that the byte compiler does not try to guess the programmer’s intent and compile the expression anyway, since that would interfere with lists that just so happen to look like lambda expressions — i.e. any list containing the lambda symbol.

There are three reasons you want your lambda expressions to get byte compiled:

While it’s common for personal configurations to skip byte compilation, Elisp should still generally be written as if it were going to be byte compiled. General rule of thumb: Ensure your lambda expressions are actually evaluated.

Lambda in lexical scope

As I’ve stressed many times, you should always use lexical scope. There’s no practical disadvantage or trade-off involved. Just do it.

Once lexical scope is enabled, the two expressions diverge even without byte compilation:

;;; -*- lexical-binding: t; -*-

(lambda (x) x)
;; => (closure (t) (x) x)

'(lambda (x) x)
;; => (lambda (x) x)

Under lexical scope, lambda expressions evaluate to closures. Closures capture their lexical environment in their closure object — nothing in this particular case. It’s a type of function object, making it a valid first argument to funcall.

Since the quote prevents the second expression from being evaluated, semantically it evaluates to a list that just so happens to look like a (non-closure) function object. Invoking a data object as a function is like using eval — i.e. executing data as code. Everyone already knows eval should not be used lightly.

It’s a little more interesting to look at a closure that actually captures a variable, so here’s a definition for constantly, a higher-order function that returns a closure that accepts any number of arguments and returns a particular constant:

(defun constantly (x)
  (lambda (&rest _) x))

Without byte compiling it, here’s an example of its return value:

(constantly :foo)
;; => (closure ((x . :foo) t) (&rest _) x)

The environment has been captured as an association list (with a trailing t), and we can plainly see that the variable x is bound to the symbol :foo in this closure. Consider that we could manipulate this data structure (e.g. setcdr or setf) to change the binding of x for this closure. This is essentially how closures mutate their own environment. Moreover, closures from the same environment share structure, so such mutations are also shared. More on this later.

Semantically, closures are distinct objects (via eq), even if the variables they close over are bound to the same value. This is because they each have a distinct environment attached to them, even if in some invisible way.

(eq (constantly :foo) (constantly :foo))
;; => nil

Without byte compilation, this is true even when there’s no lexical environment to capture:

(defun dummy ()
  (lambda () t))

(eq (dummy) (dummy))
;; => nil

The byte compiler is smart, though. As an optimization, the same closure object is reused when possible, avoiding unnecessary work, including multiple object allocations. Though this is a bit of an abstraction leak. A function can (ab)use this to introspect whether it’s been byte compiled:

(defun have-i-been-compiled-p ()
  (let ((funcs (vector nil nil)))
    (dotimes (i 2)
      (setf (aref funcs i) (lambda ())))
    (eq (aref funcs 0) (aref funcs 1))))

;; => nil

(byte-compile 'have-i-been-compiled-p)

;; => t

The trick here is to evaluate the exact same non-capturing lambda expression twice, which requires a loop (or at least some sort of branch). Semantically we should think of these closures as being distinct objects, but, if we squint our eyes a bit, we can see the effects of the behind-the-scenes optimization.

Don’t actually do this in practice, of course. That’s what byte-code-function-p is for, which won’t rely on a subtle implementation detail.


I mentioned before that one of the potential gotchas of not byte compiling your lambda expressions is overcapturing closure variables in the interpreter.

To evaluate lisp code, Emacs has both an interpreter and a virtual machine. The interpreter evaluates code in list form: cons cells, numbers, symbols, etc. The byte compiler is like the interpreter, but instead of directly executing those forms, it emits byte-code that, when evaluated by the virtual machine, produces identical visible results to the interpreter — in theory.

What this means is that Emacs contains two different implementations of Emacs Lisp, one in the interpreter and one in the byte compiler. The Emacs developers have been maintaining and expanding these implementations side-by-side for decades. A pitfall to this approach is that the implementations can, and do, diverge in their behavior. We saw this above with that introspective function, and it comes up in practice with advice.

Another way they diverge is in closure variable capture. For example:

;;; -*- lexical-binding: t; -*-

(defun overcapture (x y)
  (when y
    (lambda () x)))

(overcapture :x :some-big-value)
;; => (closure ((y . :some-big-value) (x . :x) t) nil x)

Notice that the closure captured y even though it’s unnecessary. This is because the interpreter doesn’t, and shouldn’t, take the time to analyze the body of the lambda to determine which variables should be captured. That would need to happen at run-time each time the lambda is evaluated, which would make the interpreter much slower. Overcapturing can get pretty messy if macros are introducing their own hidden variables.

On the other hand, the byte compiler can do this analysis just once at compile-time. And it’s already doing the analysis as part of its job. It can avoid this problem easily:

(overcapture :x :some-big-value)
;; => #[0 "\300\207" [:x] 1]

It’s clear that :some-big-value isn’t present in the closure.

But… how does this work?

How byte compiled closures are constructed

Recall from the internals article that the four core elements of a byte-code function object are:

  1. Parameter specification
  2. Byte-code string (opcodes)
  3. Constants vector
  4. Maximum stack usage

While a closure seems like compiling a whole new function each time the lambda expression is evaluated, there’s actually not that much to it! Namely, the behavior of the function remains the same. Only the closed-over environment changes.

What this means is that closures produced by a common lambda expression can all share the same byte-code string (second element). Their bodies are identical, so they compile to the same byte-code. Where they differ are in their constants vector (third element), which gets filled out according to the closed over environment. It’s clear just from examining the outputs:

(constantly :a)
;; => #[128 "\300\207" [:a] 2]

(constantly :b)
;; => #[128 "\300\207" [:b] 2]

constantly has three of the four components of the closure in its own constant pool. Its job is to construct the constants vector, and then assemble the whole thing into a byte-code function object (#[...]). Here it is with M-x disassemble:

0       constant  make-byte-code
1       constant  128
2       constant  "\300\207"
4       constant  vector
5       stack-ref 4
6       call      1
7       constant  2
8       call      4
9       return

(Note: since byte compiler doesn’t produce perfectly optimal code, I’ve simplified it for this discussion.)

It pushes most of its constants on the stack. Then the stack-ref 5 (5) puts x on the stack. Then it calls vector to create the constants vector (6). Finally, it constructs the function object (#[...]) by calling make-byte-code (8).

Since this might be clearer, here’s the same thing expressed back in terms of Elisp:

(defun constantly (x)
  (make-byte-code 128 "\300\207" (vector x) 2))

To see the disassembly of the closure’s byte-code:

(disassemble (constantly :x))

The result isn’t very surprising:

0       constant  :x
1       return

Things get a little more interesting when mutation is involved. Consider this adder closure generator, which mutates its environment every time it’s called:

(defun adder ()
  (let ((total 0))
    (lambda () (cl-incf total))))

(let ((count (adder)))
  (funcall count)
  (funcall count)
  (funcall count))
;; => 3

;; => #[0 "\300\211\242T\240\207" [(0)] 2]

The adder essentially works like this:

(defun adder ()
  (make-byte-code 0 "\300\211\242T\240\207" (vector (list 0)) 2))

In theory, this closure could operate by mutating its constants vector directly. But that wouldn’t be much of a constants vector, now would it!? Instead, mutated variables are boxed inside a cons cell. Closures don’t share constant vectors, so the main reason for boxing is to share variables between closures from the same environment. That is, they have the same cons in each of their constant vectors.

There’s no equivalent Elisp for the closure in adder, so here’s the disassembly:

0       constant  (0)
1       dup
2       car-safe
3       add1
4       setcar
5       return

It puts two references to boxed integer on the stack (constant, dup), unboxes the top one (car-safe), increments that unboxed integer, stores it back in the box (setcar) via the bottom reference, leaving the incremented value behind to be returned.

This all gets a little more interesting when closures interact:

(defun fancy-adder ()
  (let ((total 0))
    `(:add ,(lambda () (cl-incf total))
      :set ,(lambda (v) (setf total v))
      :get ,(lambda () total))))

(let ((counter (fancy-adder)))
  (funcall (plist-get counter :set) 100)
  (funcall (plist-get counter :add))
  (funcall (plist-get counter :add))
  (funcall (plist-get counter :get)))
;; => 102

;; => (:add #[0 "\300\211\242T\240\207" [(0)] 2]
;;     :set #[257 "\300\001\240\207" [(0)] 3]
;;     :get #[0 "\300\242\207" [(0)] 1])

This is starting to resemble object oriented programming, with methods acting upon fields stored in a common, closed-over environment.

All three closures share a common variable, total. Since I didn’t use print-circle, this isn’t obvious from the last result, but each of those (0) conses are the same object. When one closure mutates the box, they all see the change. Here’s essentially how fancy-adder is transformed by the byte compiler:

(defun fancy-adder ()
  (let ((box (list 0)))
    (list :add (make-byte-code 0 "\300\211\242T\240\207" (vector box) 2)
          :set (make-byte-code 257 "\300\001\240\207" (vector box) 3)
          :get (make-byte-code 0 "\300\242\207" (vector box) 1))))

The backquote in the original fancy-adder brings this article full circle. This final example wouldn’t work correctly if those lambdas weren’t evaluated properly.

Initial Evaluation of the Windows Subsystem for Linux

Recently I had my first experiences with the Windows Subsystem for Linux (WSL), evaluating its potential as an environment for getting work done. This subsystem, introduced to Windows 10 in August 2016, allows Windows to natively run x86 and x86-64 Linux binaries. It’s essentially the counterpart to Wine, which allows Linux to natively run Windows binaries.

WSL interfaces with Linux programs only at the kernel level, servicing system calls the same way the Linux kernel would. The subsystem’s main job is translating Linux system calls into NT requests. There’s a series of articles about its internals if you’re interested in learning more.

I was honestly impressed by how well this all works, especially since Microsoft has long had an affinity for producing flimsy imitations (Windows console, PowerShell, Arial, etc.). WSL’s design allows Microsoft to dump an Ubuntu system wholesale inside Windows — and, more recently, other Linux distributions — bypassing a bunch of annoying issues, particularly in regards to glibc.

WSL processes can exec(2) Windows binaries, which then run in under their appropriate subsystem, similar to binfmt on Linux. In theory this nice interop should allow for some automation Linux-style even for Windows’ services and programs. More on that later.

There are some notable issues, though.

Lack of device emulation

No soundcard devices are exposed to the subsystem, so Linux programs can’t play sound. There’s a hack to talk PulseAudio with a Windows’ process that can access, but that’s about it. Generally there’s not much reason to be playing media or games under WSL, but this can be an annoyance if you’re, say, writing software that synthesizes audio.

Really, there’s almost no device emulation at all and /proc is pretty empty. You won’t see hard drives or removable media under /dev, nor will you see USB devices like webcams and joysticks. A lot of the useful things you might do on a Linux system aren’t available under WSL.

No Filesystem in Userspace (FUSE)

Microsoft hasn’t implemented any of the system calls for FUSE, so don’t expect to use your favorite userspace filesystems. The biggest loss for me is sshfs, which I use frequently.

If FUSE was supported, it would be interesting to see how the rest of Windows interacts with these mounted filesystems, if at all.

Fragile services

Services running under WSL are flaky. The big issue is that when the initial WSL shell process exits, all WSL processes are killed and the entire subsystem is torn down. This includes any services that are running. That’s certainly surprising to anyone with experience running services on any kind of unix system. This is probably the worst part of WSL.

While systemd is the standard for Linux these days and may even be “installed” in the WSL virtual filesystem, it’s not actually running and you can’t use systemctl to interact with services. Services can only be controlled the old fashioned way, and, per above, that initial WSL console window has to remain open while services are running.

That’s a bit of a damper if you’re intending to spend a lot of time remotely SSHing into your Windows 10 system. So yes, it’s trivial to run an OpenSSH server under WSL, but it won’t feel like a proper system service.

Limited graphics support

WSL doesn’t come with an X server, so you have to supply one separately (Xming, etc.) that runs outside WSL, as a normal Windows process. WSL processes can connect to that server (DISPLAY) allowing you to run most Linux graphical software.

However, this means there’s no hardware acceleration. There will be no GLX extensions available. If your goal is to run the Emacs or Vim GUIs, that’s not a big deal, but it might matter if you were interested in running a browser under WSL. It also means it’s not a suitable environment for developing software using OpenGL.

Filesystem woes

The filesystem manages to be both one of the smallest issues as well as one of the biggest.

Filename translation

On the small issue side is filename translation. Under most Linux filesystems — and even more broadly for unix — a filename is just a bytestring. They’re not necessarily UTF-8 or any other particular encoding, and that’s partly why filenames are case-sensitive — the meaning of case depends on the encoding.

However, Windows uses a pseudo-UTF-16 scheme for filenames, incompatible with bytestrings. Since WSL lives within a Windows’ filesystem, there must be some bijection between bytestring filenames and pseudo-UTF-16 filenames. It will also have to reject filenames that can’t be mapped. WSL does both.

I couldn’t find any formal documentation about how filename translation works, but most of it can be reverse engineered through experimentation. In practice, Linux filenames are UTF-8 encoded strings, and WSL’s translation takes advantage of this. Filenames are decoded as UTF-8 and re-encoded as UTF-16 for Windows. Any byte that doesn’t decode as valid UTF-8 is silently converted to REPLACEMENT CHARACTER (U+FFFD), and decoding continues from the next byte.

I wonder if there are security consequences for different filenames silently mapping to the same underlying file.

Exercise for the reader: How is an unmatched surrogate half from Windows translated to WSL, where it doesn’t have a UTF-8 equivalent? I haven’t tried this yet.

Even for valid UTF-8, there are many bytes that most Linux filesystems allow in filenames that Windows does not. This ranges from simple things like ASCII backslash and colon — special components of Windows’ paths — to unusual characters like newlines, escape, and other ASCII control characters. There are two different ways these are handled:

  1. The C drive is available under /mnt/c, and WSL processes can access regular Windows files under this “mountpoint.” Attempting to access filenames with invalid characters under this mountpoint always results in ENOENT: “No such file or directory.”

  2. Outside of /mnt/c is WSL territory, and Windows processes aren’t supposed to touch these files. This allows for more freedom when translating filenames. REPLACEMENT CHARACTER is still used for invalid UTF-8 sequences, but the forbidden characters, including backslashes, are all permitted. They’re translated to #XXXX where X is hexadecimal for the normally invalid character. For example, a:b becomes a#003Ab.

While WSL doesn’t let you get away with all the crazy, ill-advised filenames that Linux allows, it’s still quite reasonable. Since Windows and Linux filenames aren’t entirely compatible, there’s going to be some trade-off no matter how this translation is done.

Filesystem performance

On the other hand, filesystem performance is abysmal, and I doubt the subsystem is to blame. This isn’t a surprise to anyone who’s used moderately-sized Git repositories on Windows, where the large numbers of loose files brings things to a crawl. This has been a Windows issue for years, and that’s even before you start plugging in the typically “security” services — virus scanners, whitelists, etc. — that are typically present on a Windows system and make this even worse.

To test out WSL, I went around my normal business compiling tools and making myself at home, just as I would on Linux. Doing nearly anything in WSL was noticably slower than doing the same on Linux on the exact same hardware. I didn’t run any benchmarks, but I’d expect to see around an order of magnitude difference on average for filesystem operations. Building LLVM and Clang took a couple hours rather than the typical 20 minutes.

I don’t expect this issue to get fixed anytime soon, and it’s probably always going to be a notable limitation of WSL.

So is WSL useful?

One of my hopes for WSL appears to be unfeasible. I thought it might be a way to avoid porting software from POSIX to Win32. I could just supply Windows users with the same Linux binary and they’d be fine. However, WSL requires switching Windows into a special “developer mode,” putting it well out of reach of the vast majority of users, especially considering the typical corporate computing environment that will lock this down. In practice, WSL is only useful to developers. I’m sure this is no accident. (Developer mode is no longer required as of October 2017.)

Mostly I see WSL as a Cygwin killer. Unix is my IDE and, on Windows, Cygwin has been my preferred go to for getting a solid unix environment for software development. Unlike WSL, Cygwin processes can make direct Win32 calls, which is occasionally useful. But, in exchange, WSL will overall be better equipped. It has native Linux tools, including a better suite of debugging tools — even better than you get in Windows itself — Valgrind, strace, and properly-working GDB (always been flaky in Cygwin). WSL is not nearly as good as actual Linux, but it’s better than Cygwin if you can get access to it.

Render Multimedia in Pure C

In a previous article I demonstrated video filtering with C and a unix pipeline. Thanks to the ubiquitous support for the ridiculously simple Netpbm formats — specifically the “Portable PixMap” (.ppm, P6) binary format — it’s trivial to parse and produce image data in any language without image libraries. Video decoders and encoders at the ends of the pipeline do the heavy lifting of processing the complicated video formats actually used to store and transmit video.

Naturally this same technique can be used to produce new video in a simple program. All that’s needed are a few functions to render artifacts — lines, shapes, etc. — to an RGB buffer. With a bit of basic sound synthesis, the same concept can be applied to create audio in a separate audio stream — in this case using the simple (but not as simple as Netpbm) WAV format. Put them together and a small, standalone program can create multimedia.

Here’s the demonstration video I’ll be going through in this article. It animates and visualizes various in-place sorting algorithms (see also). The elements are rendered as colored dots, ordered by hue, with red at 12 o’clock. A dot’s distance from the center is proportional to its corresponding element’s distance from its correct position. Each dot emits a sinusoidal tone with a unique frequency when it swaps places in a particular frame.

Original credit for this visualization concept goes to w0rthy.

All of the source code (less than 600 lines of C), ready to run, can be found here:

On any modern computer, rendering is real-time, even at 60 FPS, so you may be able to pipe the program’s output directly into your media player of choice. (If not, consider getting a better media player!)

$ ./sort | mpv --no-correct-pts --fps=60 -

VLC requires some help from ppmtoy4m:

$ ./sort | ppmtoy4m -F60:1 | vlc -

Or you can just encode it to another format. Recent versions of libavformat can input PPM images directly, which means x264 can read the program’s output directly:

$ ./sort | x264 --fps 60 -o video.mp4 /dev/stdin

By default there is no audio output. I wish there was a nice way to embed audio with the video stream, but this requires a container and that would destroy all the simplicity of this project. So instead, the -a option captures the audio in a separate file. Use ffmpeg to combine the audio and video into a single media file:

$ ./sort -a audio.wav | x264 --fps 60 -o video.mp4 /dev/stdin
$ ffmpeg -i video.mp4 -i audio.wav -vcodec copy -acodec mp3 \

You might think you’ll be clever by using mkfifo (i.e. a named pipe) to pipe both audio and video into ffmpeg at the same time. This will only result in a deadlock since neither program is prepared for this. One will be blocked writing one stream while the other is blocked reading on the other stream.

Several years ago my intern and I used the exact same pure C rendering technique to produce these raytracer videos:

I also used this technique to illustrate gap buffers.

Pixel format and rendering

This program really only has one purpose: rendering a sorting video with a fixed, square resolution. So rather than write generic image rendering functions, some assumptions will be hard coded. For example, the video size will just be hard coded and assumed square, making it simpler and faster. I chose 800x800 as the default:

#define S     800

Rather than define some sort of color struct with red, green, and blue fields, color will be represented by a 24-bit integer (long). I arbitrarily chose red to be the most significant 8 bits. This has nothing to do with the order of the individual channels in Netpbm since these integers are never dumped out. (This would have stupid byte-order issues anyway.) “Color literals” are particularly convenient and familiar in this format. For example, the constant for pink: 0xff7f7fUL.

In practice the color channels will be operated upon separately, so here are a couple of helper functions to convert the channels between this format and normalized floats (0.0–1.0).

static void
rgb_split(unsigned long c, float *r, float *g, float *b)
    *r = ((c >> 16) / 255.0f);
    *g = (((c >> 8) & 0xff) / 255.0f);
    *b = ((c & 0xff) / 255.0f);

static unsigned long
rgb_join(float r, float g, float b)
    unsigned long ir = roundf(r * 255.0f);
    unsigned long ig = roundf(g * 255.0f);
    unsigned long ib = roundf(b * 255.0f);
    return (ir << 16) | (ig << 8) | ib;

Originally I decided the integer form would be sRGB, and these functions handled the conversion to and from sRGB. Since it had no noticeable effect on the output video, I discarded it. In more sophisticated rendering you may want to take this into account.

The RGB buffer where images are rendered is just a plain old byte buffer with the same pixel format as PPM. The ppm_set() function writes a color to a particular pixel in the buffer, assumed to be S by S pixels. The complement to this function is ppm_get(), which will be needed for blending.

static void
ppm_set(unsigned char *buf, int x, int y, unsigned long color)
    buf[y * S * 3 + x * 3 + 0] = color >> 16;
    buf[y * S * 3 + x * 3 + 1] = color >>  8;
    buf[y * S * 3 + x * 3 + 2] = color >>  0;

static unsigned long
ppm_get(unsigned char *buf, int x, int y)
    unsigned long r = buf[y * S * 3 + x * 3 + 0];
    unsigned long g = buf[y * S * 3 + x * 3 + 1];
    unsigned long b = buf[y * S * 3 + x * 3 + 2];
    return (r << 16) | (g << 8) | b;

Since the buffer is already in the right format, writing an image is dead simple. I like to flush after each frame so that observers generally see clean, complete frames. It helps in debugging.

static void
ppm_write(const unsigned char *buf, FILE *f)
    fprintf(f, "P6\n%d %d\n255\n", S, S);
    fwrite(buf, S * 3, S, f);

Dot rendering

If you zoom into one of those dots, you may notice it has a nice smooth edge. Here’s one rendered at 30x the normal resolution. I did not render, then scale this image in another piece of software. This is straight out of the C program.

In an early version of this program I used a dumb dot rendering routine. It took a color and a hard, integer pixel coordinate. All the pixels within a certain distance of this coordinate were set to the color, everything else was left alone. This had two bad effects:

Instead the dot’s position is computed in floating point and is actually rendered as if it were between pixels. This is done with a shader-like routine that uses smoothstep — just as found in shader languages — to give the dot a smooth edge. That edge is blended into the image, whether that’s the background or a previously-rendered dot. The input to the smoothstep is the distance from the floating point coordinate to the center (or corner?) of the pixel being rendered, maintaining that between-pixel smoothness.

Rather than dump the whole function here, let’s look at it piece by piece. I have two new constants to define the inner dot radius and the outer dot radius. It’s smooth between these radii.

#define R0    (S / 400.0f)  // dot inner radius
#define R1    (S / 200.0f)  // dot outer radius

The dot-drawing function takes the image buffer, the dot’s coordinates, and its foreground color.

static void
ppm_dot(unsigned char *buf, float x, float y, unsigned long fgc);

The first thing to do is extract the color components.

    float fr, fg, fb;
    rgb_split(fgc, &fr, &fg, &fb);

Next determine the range of pixels over which the dot will be draw. These are based on the two radii and will be used for looping.

    int miny = floorf(y - R1 - 1);
    int maxy = ceilf(y + R1 + 1);
    int minx = floorf(x - R1 - 1);
    int maxx = ceilf(x + R1 + 1);

Here’s the loop structure. Everything else will be inside the innermost loop. The dx and dy are the floating point distances from the center of the dot.

    for (int py = miny; py <= maxy; py++) {
        float dy = py - y;
        for (int px = minx; px <= maxx; px++) {
            float dx = px - x;
            /* ... */

Use the x and y distances to compute the distance and smoothstep value, which will be the alpha. Within the inner radius the color is on 100%. Outside the outer radius it’s 0%. Elsewhere it’s something in between.

            float d = sqrtf(dy * dy + dx * dx);
            float a = smoothstep(R1, R0, d);

Get the background color, extract its components, and blend the foreground and background according to the computed alpha value. Finally write the pixel back into the buffer.

            unsigned long bgc = ppm_get(buf, px, py);
            float br, bg, bb;
            rgb_split(bgc, &br, &bg, &bb);

            float r = a * fr + (1 - a) * br;
            float g = a * fg + (1 - a) * bg;
            float b = a * fb + (1 - a) * bb;
            ppm_set(buf, px, py, rgb_join(r, g, b));

That’s all it takes to render a smooth dot anywhere in the image.

Rendering the array

The array being sorted is just a global variable. This simplifies some of the sorting functions since a few are implemented recursively. They can call for a frame to be rendered without needing to pass the full array. With the dot-drawing routine done, rendering a frame is easy:

#define N     360           // number of dots

static int array[N];

static void
    static unsigned char buf[S * S * 3];
    memset(buf, 0, sizeof(buf));
    for (int i = 0; i < N; i++) {
        float delta = abs(i - array[i]) / (N / 2.0f);
        float x = -sinf(i * 2.0f * PI / N);
        float y = -cosf(i * 2.0f * PI / N);
        float r = S * 15.0f / 32.0f * (1.0f - delta);
        float px = r * x + S / 2.0f;
        float py = r * y + S / 2.0f;
        ppm_dot(buf, px, py, hue(array[i]));
    ppm_write(buf, stdout);

The buffer is static since it will be rather large, especially if S is cranked up. Otherwise it’s likely to overflow the stack. The memset() fills it with black. If you wanted a different background color, here’s where you change it.

For each element, compute its delta from the proper array position, which becomes its distance from the center of the image. The angle is based on its actual position. The hue() function (not shown in this article) returns the color for the given element.

With the frame() function complete, all I need is a sorting function that calls frame() at appropriate times. Here are a couple of examples:

static void
shuffle(int array[N], uint64_t *rng)
    for (int i = N - 1; i > 0; i--) {
        uint32_t r = pcg32(rng) % (i + 1);
        swap(array, i, r);

static void
sort_bubble(int array[N])
    int c;
    do {
        c = 0;
        for (int i = 1; i < N; i++) {
            if (array[i - 1] > array[i]) {
                swap(array, i - 1, i);
                c = 1;
    } while (c);

Synthesizing audio

To add audio I need to keep track of which elements were swapped in this frame. When producing a frame I need to generate and mix tones for each element that was swapped.

Notice the swap() function above? That’s not just for convenience. That’s also how things are tracked for the audio.

static int swaps[N];

static void
swap(int a[N], int i, int j)
    int tmp = a[i];
    a[i] = a[j];
    a[j] = tmp;
    swaps[(a - array) + i]++;
    swaps[(a - array) + j]++;

Before we get ahead of ourselves I need to write a WAV header. Without getting into the purpose of each field, just note that the header has 13 fields, followed immediately by 16-bit little endian PCM samples. There will be only one channel (monotone).

#define HZ    44100         // audio sample rate

static void
wav_init(FILE *f)
    emit_u32be(0x52494646UL, f); // "RIFF"
    emit_u32le(0xffffffffUL, f); // file length
    emit_u32be(0x57415645UL, f); // "WAVE"
    emit_u32be(0x666d7420UL, f); // "fmt "
    emit_u32le(16,           f); // struct size
    emit_u16le(1,            f); // PCM
    emit_u16le(1,            f); // mono
    emit_u32le(HZ,           f); // sample rate (i.e. 44.1 kHz)
    emit_u32le(HZ * 2,       f); // byte rate
    emit_u16le(2,            f); // block size
    emit_u16le(16,           f); // bits per sample
    emit_u32be(0x64617461UL, f); // "data"
    emit_u32le(0xffffffffUL, f); // byte length

Rather than tackle the annoying problem of figuring out the total length of the audio ahead of time, I just wave my hands and write the maximum possible number of bytes (0xffffffff). Most software that can read WAV files will understand this to mean the entire rest of the file contains samples.

With the header out of the way all I have to do is write 1/60th of a second worth of samples to this file each time a frame is produced. That’s 735 samples (1,470 bytes) at 44.1kHz.

The simplest place to do audio synthesis is in frame() right after rendering the image.

#define FPS   60            // output framerate
#define MINHZ 20            // lowest tone
#define MAXHZ 1000          // highest tone

static void
    /* ... rendering ... */

    /* ... synthesis ... */

With the largest tone frequency at 1kHz, Nyquist says we only need to sample at 2kHz. 8kHz is a very common sample rate and gives some overhead space, making it a good choice. However, I found that audio encoding software was a lot happier to accept the standard CD sample rate of 44.1kHz, so I stuck with that.

The first thing to do is to allocate and zero a buffer for this frame’s samples.

    int nsamples = HZ / FPS;
    static float samples[HZ / FPS];
    memset(samples, 0, sizeof(samples));

Next determine how many “voices” there are in this frame. This is used to mix the samples by averaging them. If an element was swapped more than once this frame, it’s a little louder than the others — i.e. it’s played twice at the same time, in phase.

    int voices = 0;
    for (int i = 0; i < N; i++)
        voices += swaps[i];

Here’s the most complicated part. I use sinf() to produce the sinusoidal wave based on the element’s frequency. I also use a parabola as an envelope to shape the beginning and ending of this tone so that it fades in and fades out. Otherwise you get the nasty, high-frequency “pop” sound as the wave is given a hard cut off.

    for (int i = 0; i < N; i++) {
        if (swaps[i]) {
            float hz = i * (MAXHZ - MINHZ) / (float)N + MINHZ;
            for (int j = 0; j < nsamples; j++) {
                float u = 1.0f - j / (float)(nsamples - 1);
                float parabola = 1.0f - (u * 2 - 1) * (u * 2 - 1);
                float envelope = parabola * parabola * parabola;
                float v = sinf(j * 2.0f * PI / HZ * hz) * envelope;
                samples[j] += swaps[i] * v / voices;

Finally I write out each sample as a signed 16-bit value. I flush the frame audio just like I flushed the frame image, keeping them somewhat in sync from an outsider’s perspective.

    for (int i = 0; i < nsamples; i++) {
        int s = samples[i] * 0x7fff;
        emit_u16le(s, wav);

Before returning, reset the swap counter for the next frame.

    memset(swaps, 0, sizeof(swaps));

Font rendering

You may have noticed there was text rendered in the corner of the video announcing the sort function. There’s font bitmap data in font.h which gets sampled to render that text. It’s not terribly complicated, but you’ll have to study the code on your own to see how that works.

Learning more

This simple video rendering technique has served me well for some years now. All it takes is a bit of knowledge about rendering. I learned quite a bit just from watching Handmade Hero, where Casey writes a software renderer from scratch, then implements a nearly identical renderer with OpenGL. The more I learn about rendering, the better this technique works.

Before writing this post I spent some time experimenting with using a media player as a interface to a game. For example, rather than render the game using OpenGL or similar, render it as PPM frames and send it to the media player to be displayed, just as game consoles drive television sets. Unfortunately the latency is horrible — multiple seconds — so that idea just doesn’t work. So while this technique is fast enough for real time rendering, it’s no good for interaction.

null program

Chris Wellons