wingo is currently certified at Master level.

Name: Andy Wingo
Member since: 2001-05-29 05:20:46
Last Login: 2009-12-14 09:39:54

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Some projects I hack on:

Interests: Currently hacking at Fluendo in Barcelona, making a platform for streaming live video, with on-demand as a bit of an afterthought.

Prior to that, I spent two years teaching math and science in rural northern Namibia for the Peace Corps.

My advo diary is mirrored from my web log over at There are a few other things hosted there as well.


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stack overflow

Good morning, gentle hackers. Today's article is about stack representation, how stack representations affect programs, what it means to run out of stack, and that kind of thing. I've been struggling with the issue for a while now in Guile and finally came to a nice solution. But I'm getting ahead of myself; read on for some background on the issue, and details on what Guile 2.2 will do.

stack limits

Every time a program makes a call that is not a tail call, it pushes a new frame onto the stack. Returning a value from a function pops the top frame off the stack. Stack frames take up memory, and as nobody has an infinite amount of memory, deep recursion could cause your program to run out of memory. Running out of stack memory is called stack overflow.

Most languages have a terrible stack overflow story. For example, in C, if you use too much stack, your program will exhibit "undefined behavior". If you are lucky, it will crash your program; if are unlucky, it could crash your car. It's especially bad in C, as you neither know ahead of time how much stack your functions use, nor the stack limit imposed by the user's system, and the stack limit is often quite small relative to the total memory size.

Things are better, but not much better, in managed languages like Python. Stack overflow is usually assumed to throw an exception (though I couldn't find the specification for this), but actually making that happen is tricky enough that simple programs can cause Python to abort and dump core. And still, like C, Python and most dynamic languages still have a fixed stack size limit that is usually much smaller than the heap.

Arbitrary stack limits would have an unfortunate effect on Guile programs. For example, the following implementation of the inner loop of map is clean and elegant:

(define (map f l)
  (if (pair? l)
      (cons (f (car l))
            (map f (cdr l)))

However, if there were a stack limit, that would limit the size of lists that can be processed with this map. Eventually, you would have to rewrite it to use iteration with an accumulator:

(define (map f l)
  (let lp ((l l) (out '()))
    (if (pair? l)
        (lp (cdr l) (cons (f (car l)) out))
        (reverse out))))

This second version is sadly not as clear, and it also allocates twice as much heap memory (once to build the list in reverse, and then again to reverse the list). You would be tempted to use the destructive linear-update reverse! to save memory and time, but then your code would not be continuation-safe -- if f returned again after the map had finished, it would see an out list that had already been reversed. (If you're interested, you might like this little Scheme quiz.) The recursive map has none of these problems.

a solution?

Guile 2.2 will have no stack limit for Scheme code.

When a thread makes its first Guile call, a small stack is allocated -- just one page of memory. Whenever that memory limit would be reached, Guile arranges to grow the stack by a factor of two.

Ideally, stack growth happens via mremap, and ideally at the same address in memory, but it might happen via mmap or even malloc of another memory block. If the stack moves to a different address, we fix up the frame pointers. Recall that right now Guile runs on a virtual machine, so this is a stack just for Scheme programs; we'll talk about the OS stack later on.

Being able to relocate the stack was not an issue for Guile, as we already needed them to implement delimited continuations. However, relocation on stack overflow did cause some tricky bugs in the VM, as relocation could happen at more places. In the end it was OK. Each stack frame in Guile has a fixed size, and includes space to make any nested calls; check my earlier article on the Guile 2.2 VM for more. The entry point of a function handles allocation of space for the function's local variables, and that's basically the only point the stack can overflow. The few things that did need to point into the stack were changed to be an offset from the stack base instead of a raw pointer.

Even when you grow a stack by a factor of 2, that doesn't mean you immediately take up twice as much memory. Operating systems usually commit memory to a process on a page-by-page granularity, which is usually around 4 kilobytes. Once accessed, this memory is always a part of your process's memory footprint. However, Guile mitigates this memory usage here; because it has to check for stack overflow anyway, it records a "high-water mark" stack usage since the last garbage collection. When garbage collection happens, Guile arranges to return the unused part of the stack to the operating system (using MADV_DONTNEED), but without causing the stack to shrink. In this way, the stack can grow to consume up to all memory available to the Guile process, and when the recursive computation eventually finishes, that stack memory is returned to the system.

You might wonder, why not just allocate enormous stacks, relying on the kernel to page them in lazily as needed? The biggest part of the answer is that we need to still be able to target 32-bit platforms, and this isn't a viable strategy there. Even on 64-bit, whatever limit you choose is still a limit. If you choose 4 GB, what if you want to map over a larger list? It's admittedly extreme, given Guile's current GC, but not unthinkable. Basically, your stack should be able to grow as big as your heap could grow. The failure mode for the huge-stack case is also pretty bad; instead of getting a failure to grow your stack, which you can handle with an exception, you get a segfault as the system can't page in enough memory.

The other common strategy is "segmented stacks", but the above link covers the downsides of that in Go and Rust. It would also complicate the multiple-value return convention in Guile, where currently multiple values might temporarily overrun the receiver's stack frame.

exceptional situations

Of course, it's still possible to run out of stack memory. Usually this happens because of a program bug that results in unbounded recursion, as in:

(define (faulty-map f l)
  (if (pair? l)
      (cons (f (car l)) (faulty-map f l))

Did you spot the bug? The recursive call to faulty-map recursed on l, not (cdr l). Running this program would cause Guile to use up all memory in your system, and eventually Guile would fail to grow the stack. At that point you have a problem: Guile needs to raise an exception to unwind the stack and return memory to the system, but the user might have throw handlers in place that want to run before the stack is unwound, and we don't have any stack in which to run them.

Therefore in this case, Guile throws an unwind-only exception that does not run pre-unwind handlers. Because this is such an odd case, Guile prints out a message on the console, in case the user was expecting to be able to get a backtrace from any pre-unwind handler.

runaway recursion

Still, this failure mode is not so nice. If you are running an environment in which you are interactively building a program while it is running, such as at a REPL, you might want to impose an artificial stack limit on the part of your program that you are building to detect accidental runaway recursion. For that purpose, there is call-with-stack-overflow-handler. You run it like this:

(call-with-stack-overflow-handler 10000
  (lambda ()              ; body
    (faulty-map (lambda (x) x) '(1 2 3)))
  (lambda ()              ; handler
    (error "Stack overflow!")))

→ ERROR: Stack overflow

The body procedure is called in an environment in which the stack limit has been reduced to some number of words (10000, in the above example). If the limit is reached, the handler procedure will be invoked in the dynamic environment of the error. For the extent of the call to the handler, the stack limit and handler are restored to the values that were in place when call-with-stack-overflow-handler was called.

Unlike the unwind-only exception that is thrown if Guile is unable to grow its stack, any exception thrown by a stack overflow handler might invoke pre-unwind handlers. Indeed, the stack overflow handler is itself a pre-unwind handler of sorts. If the code imposing the stack limit wants to protect itself against malicious pre-unwind handlers from the inner thunk, it should abort to a prompt of its own making instead of throwing an exception that might be caught by the inner thunk. (Overflow on unwind via inner dynamic-wind is not a problem, as the unwind handlers are run with the inner stack limit.)

Usually, the handler should raise an exception or abort to an outer prompt. However if handler does return, it should return a number of additional words of stack space to grant to the inner environment. A stack overflow handler may only ever "credit" the inner thunk with stack space that was available when the handler was instated. When Guile first starts, there is no stack limit in place, so the outer handler may allow the inner thunk an arbitrary amount of space, but any nested stack overflow handler will not be able to consume more than its limit.

I really, really like Racket's notes on iteration and recursion, but treating stack memory just like any other kind of memory isn't always what you want. It doesn't make sense to throw an exception on an out-of-memory error, but it does make sense to do so on stack overflow -- and you might want to do some debugging in the context of the exception to figure out what exactly ran away. It's easy to attribute blame for stack memory use, but it's not so easy for heap memory. And throwing an exception will solve the problem of too much stack usage, but it might not solve runaway memory usage. I prefer the additional complexity of having stack overflow handlers, as it better reflects the essential complexity of resource use.

os stack usage

It is also possible for Guile to run out of space on the "C stack" -- the stack that is allocated to your program by the operating system. If you call a primitive procedure which then calls a Scheme procedure in a loop, you will consume C stack space. Guile tries to detect excessive consumption of C stack space, throwing an error when you have hit 80% of the process' available stack (as allocated by the operating system), or 160 kilowords in the absence of a strict limit.

For example, looping through call-with-vm, a primitive that calls a thunk, gives us the following:

(use-modules (system vm vm))

(let lp () (call-with-vm lp))

→ ERROR: Stack overflow

Unfortunately, that's all the information we get. Overrunning the C stack will throw an unwind-only exception, because it's not safe to do very much when you are close to the C stack limit.

If you get an error like this, you can either try rewriting your code to use less stack space, or you can increase Guile's internal C stack limit. Unfortunately this is a case in which the existence of a limit affects how you would write your programs. The the best thing is to have your code operate without consuming so much OS stack by avoiding loops through C trampolines.

I don't know what will happen when Guile starts to do native compilation. Obviously we can't relocate the C stack, so lazy stack growth and relocation isn't a viable strategy if we want to share the C and Scheme stacks. Still, we need to be able to relocate stack segments for delimited continuations, so perhaps there will still be two stacks, even with native C compilation. We will see.

Well, that's all the things about stacks. Until next time, happy recursing!

Syndicated 2014-03-17 11:40:42 from wingolog

es6 generator and array comprehensions in spidermonkey

Good news, everyone: ES6 generator and array comprehensions just landed in SpiderMonkey!

Let's take a quick look at what comprehensions are, then talk about what just landed in SpiderMonkey and when you'll see it in a Firefox release. Thanks to Bloomberg for sponsoring this work.

comprendes, mendes

Comprehensions are syntactic sugar for iteration. Unlike for-of, which processes its body for side effects, an array comprehension processes its body for its values, collecting them into a new array. Like this:

// Before (by hand)
var foo = (function(){
             var result = [];
             for (var x of y)
             return result;

// Before (assuming y has a map() method)
var foo = { return x*x });

// After
var foo = [for (x of y) x*x];

As you can see, array comprehensions are quite handy. They're expressions, not statements, and so their result can be passed directly to whatever code needs it. This can make your program more clear, because you aren't forced to give names to intermediate values, like result. At the same time, they work on any iterable, so you can use them on more kinds of data than just arrays. Because array comprehensions don't make a new closure, you can access arguments and this even yield from within the comprehension tail.

Generator comprehensions are also nifty, but for a different reason. Let's look at an example first:

// Before
var bar = (function*(){ for (var x of y) yield y })();

// After
var bar = (for (x of y) y);

As you can see the syntactic win here isn't that much, compared to just writing out the function* and invoking it. The real advantage of generator comprehensions is their similarity to array comprehensions, and that often you can replace an array comprehension by a generator comprehension. That way you never need to build the complete list of values in memory -- you get laziness for free, just by swapping out those square brackets for the comforting warmth of parentheses.

Both kinds of comprehension can contain multiple levels of iteration, with embedded conditionals as you like. You can do [for (x of y) for (z of x) if (z % 2) z + 1] and all kinds of related niftiness. Comprehensions are almost always more concise than map and filter, with the added advantage that they are usually more efficient.

what happened

SpiderMonkey has had comprehensions for a while now, but only as a non-standard language extension you have to opt into. Now that comprehensions are in the draft ES6 specification, we can expose them to the web as a whole, by default.

Of course, the comprehensions that ES6 specified aren't quite the same as the ones that were in SM. The obvious difference is that SM's legacy comprehensions were written the other way around: [x for (x of y)] instead of the new [for (x of y) x]. There were also a number of other minor differences, which I'll list here for posterity:

  • ES6 comprehensions create one scope per "for" node -- not one for the comprehension as a whole.

  • ES6 comprehensions can have multiple "if" components, which may be followed by other "for" or "if" components.

  • ES6 comprehensions should make a fresh binding on each iteration of a "for", although Firefox currently doesn't do this (bug 449811). Incidentally, for-of in Firefox has this same problem.

  • ES6 comprehensions only do for-of iteration, not for-in iteration.

  • ES6 generator comprehensions always need parentheses around them. (The parentheses were optional in some cases for SM's old generator comprehensions.

  • ES6 generator comprehensions are ES6 generators (returning {value, done} objects), not legacy generators (StopIteration).

I should note in particular that the harmony wiki is out of date, as the feature has moved into the spec proper: array comprehensions, generator comprehensions.

For another fine article on ES6 generators, check out Ariya Hidayat's piece on comprehensions from about a year ago.

So, ES6 comprehensions just landed in SpiderMonkey today, which means it should be part of Firefox 30, which should reach "beta" in April and become a stable release in June. You can try it out tomorrow if you use a nightly build, provided it doesn't cause some crazy breakage tonight. As of this writing, Firefox will be the first browser to ship ES6 array and generator comprehensions.


I had a Monday of despair: hacking at random on something that didn't solve my problem. But I had a Tuesday morning of pleasure, when I realized that my Monday's flounderings could be cooked into a delicious mid-week bisque; the hack was obvious and small and would benefit the web as a whole. (Wednesday was for polish and Thursday on another bug, and Friday on a wild parser-to-OSR-to-assembly-and-back nailbiter; but in the end all is swell.)

Thanks again to Bloomberg for this opportunity to build out the web platform, and to Mozilla for their quality browser wares (and even better community of hackers).

This has been an Igalia joint. Until next time!

Syndicated 2014-03-07 21:11:55 from wingolog

compost, a leaf function compiler for guile

What's that out by the woodshed? It's a steaming pile -- it's full of bugs -- it's compost, a leaf function compiler for Guile!

Around this time last year, a few of us cooked up some hack-dishes to bring to a potluck for Guile 2.0's release anniversary. Last year, mine was a little OpenGL particle demo.

That demo was neat but it couldn't be as big as I would have liked it to be because it was too slow. So, this year when the potluck season rolled around again I sat down to make a little compiler for the subset of Scheme that you see in inner numeric loops -- bytevector access, arithmetic, and loops.

The result is compost. Compost compiles inner loops into native x86-64 machine code that operates on unboxed values.

As you would imagine, compost-compiled code is a lot faster than code interpreted by Guile's bytecode interpreter. I go from being able to compute and render 5K particles at 60 fps up to 400K particles or so -- an 80-fold improvement. That's swell but it gets sweller. The real advantage is that with fast inner loops, I can solve more complicated problems.

Like this one!

Last year's demo hard-coded a gravitational attractor at (0, 0, 0). This one has no hard-coded attractor -- instead, each particle attracts each other. This so-called n-body simulation is an n-squared problem, so you need to be really efficient with the primitives to scale up, and even then the limit approaches quickly.

With compost, I can get to about 1650 particles at 60 frames per second, using 700% CPU on this 4-core 2-thread-per-core i7-3770 machine, including display with the free software radeon drivers. Without compost -- that is to say, just with Guile's bytecode virtual machine -- I max out at something more like 120 particles, and only 200% CPU.

The rest of this post describes how compost works. If compilers aren't your thing, replace the rest of the words with cat noises.

meow meow meow meow meow meow meow meow

The interface to compost is of course a macro, define/compost. Here's a little loop to multiply two vectors into a third, element-wise:

(use-modules (compost syntax) (rnrs bytevectors))
(define/compost (multiply-vectors (dst bytevector?)
                                  (a bytevector?)
                                  (b bytevector?)
                                  (start exact-integer?)
                                  (end exact-integer?))
  (let lp ((n start))
    (define (f32-ref bv n)
      (bytevector-ieee-single-native-ref bv (* n 4)))
    (define (f32-set! bv n val)
      (bytevector-ieee-single-native-set! bv (* n 4) val))
    (when (< n end)
      (f32-set! dst n (* (f32-ref a n) (f32-ref b n)))
      (lp (1+ n)))))

It's low-level but that's how we roll. If you evaluate this form and all goes well, it prints out something like this at run-time:

;;; loading /home/wingo/.cache/guile/compost/

This indicates that compost compiled your code into a shared object at macro-expansion time, and then printed out that message when it loaded it at runtime. If composting succeeds, compost writes out the compiled code into a loadable shared object (.so file). It then residualizes a call to dlopen to load that file at run-time, followed by code to look up the multiply-vectors symbol and create a foreign function. If composting fails, it prints out a warning and falls back to normal Scheme (by residualizing a plain lambda).

In the beginning of the article, I called compost a "leaf function compiler". Composted functions should be "leaf functions" -- they shouldn't call other functions. This restriction applies only to the low-level code, however. The first step in composting is to run the function through Guile's normal source-to-source optimizers, resulting in a CPS term. The upshot is that you can use many kinds of abstraction inside the function, like the little f32-ref/f32-set! helpers above, but in the end Guile should have inlined or contified them all away. It's a restriction, but hey, this is just a little hack.

Let's look at some assembly. We could get disassembly just by calling objdump -d /home/wingo/.cache/guile/compost/, but let's do it a different way. Let's put that code into a file, say "/tmp/qux.scm", and add on this code at the end:

(define size #e1e8) ;; 100 million
(define f32v (make-f32vector size 2.0))
(multiply-vectors f32v f32v f32v 0 size)

OK. Now we run Guile under GDB:

$ gdb --args guile /tmp/qux.scm 
(gdb) b 'multiply-vectors'
Function "multiply-vectors" not defined.
Make breakpoint pending on future shared library load? (y or [n]) y
Breakpoint 1 ('multiply-vectors') pending.
(gdb) r
Starting program: /opt/guile/bin/guile /tmp/qux.scm
[New Thread 0x7ffff604b700 (LWP 13729)]
;;; loading /home/wingo/.cache/guile/compost/

Breakpoint 1, 0x00007ffff5322000 in multiply-vectors () from /home/wingo/.cache/guile/compost/
(gdb) step
multiply-vectors () at /tmp/qux.scm:12
12	    (when (< n end)

Word! GDB knows about the symbol, multiply-vectors. That's top. We are able to step into it, and it prints Scheme code!

Both of these swell things are because compost residualizes its compiled code as ELF files, and actually re-uses Guile's linker. The ELF that we generate can be loaded by dlopen, and its symbol tables and DWARF debugging information are known to GDB.

(In my last article on ELF I mentioned that Guile had no plans to use the system dynamic library loader (dlopen). That's still true; Guile has its own loader. I used the system loader in this place, though, just because I thought it was a neat hack.)

We can tell GDB to disassemble the next line:

(gdb) set disassemble-next-line on
(gdb) step
9	      (bytevector-ieee-single-native-ref bv (* n 4)))
=> 0x00007ffff532201d <multiply-vectors+29>:	4c 0f af c9	imul   %rcx,%r9
13	      (f32-set! dst n (* (f32-ref a n) (f32-ref b n)))
=> 0x00007ffff532203b <multiply-vectors+59>:	f2 0f 59 c1	mulsd  %xmm1,%xmm0
   0x00007ffff532203f <multiply-vectors+63>:	49 b9 04 00 00 00 00 00 00 00	movabs $0x4,%r9

GDB does OK with these things, but it doesn't have special support for Scheme, and really you would want column pointers, not just lines. That data is in the DWARF but it's not processed by GDB. Anyway here's the disassembly:

(gdb) disassemble
Dump of assembler code for function multiply-vectors:
   0x00007ffff5322000 <+0>:	push   %rbx
   0x00007ffff5322001 <+1>:	push   %rbp
   0x00007ffff5322002 <+2>:	push   %r12
   0x00007ffff5322004 <+4>:	push   %r13
   0x00007ffff5322006 <+6>:	push   %r14
   0x00007ffff5322008 <+8>:	push   %r15
   0x00007ffff532200a <+10>:	cmp    %r8,%rcx
   0x00007ffff532200d <+13>:	jge    0x7ffff5322060 <multiply-vectors+96>
   0x00007ffff5322013 <+19>:	movabs $0x4,%r9
   0x00007ffff532201d <+29>:	imul   %rcx,%r9
   0x00007ffff5322021 <+33>:	cvtss2sd (%rsi,%r9,1),%xmm0
   0x00007ffff5322027 <+39>:	movabs $0x4,%r9
   0x00007ffff5322031 <+49>:	imul   %rcx,%r9
   0x00007ffff5322035 <+53>:	cvtss2sd (%rdx,%r9,1),%xmm1
=> 0x00007ffff532203b <+59>:	mulsd  %xmm1,%xmm0
   0x00007ffff532203f <+63>:	movabs $0x4,%r9
   0x00007ffff5322049 <+73>:	imul   %rcx,%r9
   0x00007ffff532204d <+77>:	cvtsd2ss %xmm0,%xmm15
   0x00007ffff5322052 <+82>:	movss  %xmm15,(%rdi,%r9,1)
   0x00007ffff5322058 <+88>:	inc    %rcx
   0x00007ffff532205b <+91>:	jmpq   0x7ffff532200a <multiply-vectors+10>
   0x00007ffff5322060 <+96>:	movabs $0x804,%rdi
   0x00007ffff532206a <+106>:	mov    %rdi,%rax
   0x00007ffff532206d <+109>:	pop    %r15
   0x00007ffff532206f <+111>:	pop    %r14
   0x00007ffff5322071 <+113>:	pop    %r13
   0x00007ffff5322073 <+115>:	pop    %r12
   0x00007ffff5322075 <+117>:	pop    %rbp
   0x00007ffff5322076 <+118>:	pop    %rbx
   0x00007ffff5322077 <+119>:	retq   
End of assembler dump.

Now if you know assembly, this is pretty lame stuff -- it saves registers it doesn't use, it multiplies instead of adds to get the bytevector indexes, it loads constants many times, etc. It's a proof of concept. Sure beats heap-allocated floating-point numbers, though.

safety and semantics

Compost's goal is to match Guile's semantics, while processing values with native machine operations. This means that it needs to assign concrete types and representations to all values in the function. To do this, it uses the preconditions, return types from primitive operations, and types from constant literals to infer other types in the function. If it succeeds, it then chooses representations (like "double-precision floating point") and assigns values to registers. If the types don't check out, or something is unsupported, compilation bails and runtime will fall back on Guile's normal execution engine.

There are a couple of caveats, however.

One is that compost assumes that small integers do not promote to bignums. We could remove this assumption with better range analysis. Compost does do some other analysis, like sign analysis to prove that the result of sqrt is real. Complex numbers will cause compost to bail.

Compost also doesn't check bytevector bounds at run-time. This is terrible. I didn't do it though because to do this nicely you need to separate the bytevector object into two variables: the pointer to the contents and the length. Both should be register-allocated separately, and range analysis would be nice too. Oh well!

Finally, compost is really limited in terms of the operations it supports. In fact, if register allocation would spill on the function, it bails entirely :)

the source

If it's your thing, have fun over on yon gitorious. Compost needs Guile from git, and the demos need Figl, the GL library. For me this project is an ephemeral thing; a trial for future work, with some utility now, but not something I really want to support. Still, if it's useful to you, have at it.


I woke up this morning at 5 thinking about the universe, and how friggin big it is. I couldn't go back to sleep. All these particles swirling and interacting in parallel, billions upon zillions, careening around, somehow knowing what forces are on them, each somehow a part of the other. I studied physics and I never really boggled at the universe in the way I did this morning thinking about this n-body simulation. Strange stuff.

I remember back in college when I was losing my catholicism and I would be at a concert or a dance show or something and I would think "what is this, this is just particles vibrating, bodies moving in the nothing; there is no meaning here." It was a thought of despair and unmooring. I don't suffer any more over it, but mostly because I don't think about it any more. I still don't believe in some omniscient skydude or skylady, but if I did, I know he or she has got a matrix somewhere with every particle's position and velocity.

Swirl on, friends, and until next time, happy hacking.

Syndicated 2014-02-18 20:21:18 from wingolog

elf in guile

Good evening, gentle hackfolk!

Today I'd like to wrap up my three-part series of articles on what's new in Guile 2.2's compiler and runtime. I talked about the virtual machine a couple months ago, and the compiler internals just last Sunday. Today's article is about the object file format.

Sounds boring, right? Well, probably for most humans. But hackers, Guile compiles to ELF. Pretty rad, amirite? I thought so too. Read on, nerdy readers, read on!

object files

So let's consider the problem: Guile compiles to bytecode for a custom virtual machine. In the future we want to do native compilation. In both cases we'll need to write bytes out to disk in some format, and be able to load that code back into Guile. What should be the format for those bytes? For our purposes, a good object file format has a number of characteristics:

  • Above all else, it should be very cheap to load a compiled file.

  • It should be possible to statically allocate constants in the file. For example, a bytevector literal in source code can be emitted directly into the object file.

  • The compiled file should enable maximum code and data sharing between different processes.

  • The compiled file should contain debugging information, such as line numbers, but that information should be separated from the code itself. It should be possible to strip debugging information if space is tight.

These characteristics are not specific to Scheme. So why not just steal from the C and C++ people? They use the flexible ELF object file format, and so does Guile.

Note that although Guile uses ELF on all platforms, we do not use platform support for ELF. Guile implements its own linker and loader. The advantage of using ELF is not sharing code, but sharing ideas. ELF is simply a well-designed object file format.

An ELF file has two meta-tables describing its contents. The first meta-table is for the loader, and is called the program table or sometimes the segment table. The program table divides the file into big chunks that should be treated differently by the loader. Mostly the difference between these segments is their permissions.

Typically all segments of an ELF file are marked as read-only, except that part that represents modifiable static data or static data that needs load-time initialization. Loading an ELF file is as simple as mmapping the thing into memory with read-only permissions, then using the segment table to mark a small sub-region of the file as writable. This writable section is typically added to the root set of the garbage collector as well.

The other meta-table in an ELF file is the section table. Whereas the program table divides an ELF file into big chunks for the loader, the section table specifies small sections for use by introspective tools like debuggers or the like. One segment (program table entry) typically contains many sections. There may be sections outside of any segment, as well.

I know, this is a bit dry. So I made you a picture; or rather, a program that makes pictures. Here's an example of one of the pictures:

What we see here is a page map of assembler.go, a module in Guile's compiler. The upper part of the top shows each section in a different color, in the place they appear in the file. Each horizontal line is one four-kilobyte page.

As you can see, some pages contain multiple sections or parts of sections; that is the case for pages that have the same permissions (read-only versus read-write), and which don't have special alignment requirements. About halfway down after the small red .dynamic section there is a gap, indicating that the next section, .data needs to start on a separate page -- in this case because it is writable.

This page map shows the sections of the ELF file. (There are only three segments; the first read-only part, a small "dynamic" segment holding the .dynamic section, used when the file is loaded; and the final read-write section. You can't see this from the visualization, but actually everything after .data is in no segment at all -- because it's not strictly necessary at run-time. That's the ELF convention.)

(Why is this file so big, you ask? It's a complicated answer. Part of it is because much of the assembler is itself a generated program; it uses Scheme procedural macros to define emit-foo procedures for each kind of instruction, based information extracted from the VM code (link). Guile doesn't do phasing, so these macros are residualized into the object file, and because of datum->syntax, macros have a lot of associated constant literals. That probably explains the huge size of .data, as syntax objects contain vectors and lists and symbols needing run-time relocation. I would have to check, but I think .rtl-text is big mostly because of a number of dynamic type checks in this particular module that the optimizer is (rightly) unable to elide. Alack. Surely both of these can be fixed eventually.)

But enough of problems. Did I mention that I made a program? And how! I think this may be the only blog post you have ever read that has a form in it, but that's how I am rolling today. Select an ELF file on your system, click submit, and you can have your very own page map. It works on any ELF file, not just Guile's files, so you can send your or whatever.

If you don't see a form above, your blog reader must have stripped it out. Click through to the blog post itself, or go visit the tool directly. And of course the program is written in Guile -- the file upload handling, the ELF parsing, and the pixel munging (using Cairo and a homebrew charting library).

Anyway, here's another example, this time of a small file:

Here we see that this file has the same sections as our earlier bloated example, only they are smaller.

Allow me to point out some details.

Firstly, the .rtl-text section holds the bytecode. Usually this would be called .text, but I didn't want to mess with people's expectations, and the new VM used to be called the "RTL" VM. (No longer, that was a silly name.)

The .data section holds data that needs initialization, or which may be modified at runtime. .rodata holds statically allocated data that needs no run-time initialization, and which therefore can be shared between processes. The initializations themselves, like relocations, are compiled to a procedure in the .rtl-text section, linked to from the dynamic section.

And that's all the sections! Except for the introspection and debugging-related sections, that is. These sections are used by the Guile runtime to be able to answer questions like "what is the name of this function?" or "what is the source position corresponding to bytecode position X?" or "what is the documentation string for this function?" or, well, I think you get the point. None of this is usually needed at runtime, and because it is all allocated at the end of the file, that means that usually none of it is ever paged into memory.

Note that for some of this metadata we use the standard DWARF format. I think we are one of the few dynamic language runtimes that does this nifty thing.

Also, all read-only data in these ELF files is ripe for sharing between processes, paging out to disk if under memory pressure, etc. For example, if I look on the smaps file for one of the two web processes running on this server, I see:

Size:                132 kB
Rss:                 124 kB
Pss:                  62 kB

meaning that for this particular file, almost all of it is not only shareable but being shared. Good times.

Finally, all of this has a positive impact on start-up time. While I can get Guile 2.0 to start up in 11 milliseconds, with Guile 2.2 I am down to 8 milliseconds. Likewise guile -c '(sleep 100)' in Guile 2.0 uses 3144 kB of private dirty memory, compared to 1852 kB with Guile 2.2. There's still improvements to be made, but things are going well.

Well, again I find myself rambling. Check out the little ELF mapper tool I have above and let me know of any curious results. Do send your questions as well; though I've been derelict at responding, who knows, new year, new leaf right? Until next time, happy hacking.

Syndicated 2014-01-19 19:55:14 from wingolog

a continuation-passing style intermediate language for guile

Happy new year's, hackfolk!

A few weeks ago I wrote about the upcoming Guile 2.2 release, and specifically about its new register virtual machine. Today I'd like to burn some electrons on another new part in Guile 2.2, its intermediate language.

To recap, we switched from a stack machine to a register machine because, among other reasons, register machines can consume and produce named intermediate results in fewer instructions than stack machines, and that makes things faster.

To take full advantage of this new capability, it is appropriate to switch at the same time from the direct-style intermediate language (IL) that we had to an IL that names all intermediate values. This lets us effectively reason about each subexpression that goes into a computation, for example in common subexpression elimination.

As far as intermediate languages go, basically there are two choices these days: something SSA-based, or something CPS-based. I wrote an article on SSA, ANF and CPS a few years ago; if you aren't familiar with these or are feeling a little rusty, I suggest you go and take a look.

In Guile I chose a continuation-passing style language. I still don't know if I made the right choice. I'll go ahead and describe Guile's IL and then follow up with some reflections. The description below is abbreviated from a more complete version in Guile's manual.

guile's cps language

Guile's CPS language is composed of terms, expressions, and continuations. It was heavily inspired by Andrew Kennedy's Compiling with Continuations, Continued paper.

A term can either evaluate an expression and pass the resulting values to some continuation, or it can declare local continuations and contain a sub-term in the scope of those continuations.

$continue k src exp
Evaluate the expression exp and pass the resulting values (if any) to the continuation labelled k.
$letk conts body
Bind conts in the scope of the sub-term body. The continuations are mutually recursive.

Additionally, the early stages of CPS allow for a set of mutually recursive functions to be declared as a term via a $letrec term. A later pass will attempt to transform the functions declared in a $letrec into local continuations. Any remaining functions are later lowered to $fun expressions. More on "contification" later.

Here is an inventory of the kinds of expressions in Guile's CPS language. Recall that all expressions are wrapped in a $continue term which specifies their continuation.

Continue with the unspecified value.
$const val
Continue with the constant value val.
$prim name
Continue with the procedure that implements the primitive operation named by name.
$fun src meta free body
Continue with a procedure. body is the $kentry $cont of the procedure entry. free is a list of free variables accessed by the procedure. Early CPS uses an empty list for free; only after closure conversion is it correctly populated.
$call proc args
Call proc with the arguments args, and pass all values to the continuation. proc and the elements of the args list should all be variable names. The continuation identified by the term's k should be a $kreceive or a $ktail instance.
$primcall name args
Perform the primitive operation identified by name, a well-known symbol, passing it the arguments args, and pass all resulting values to the continuation.
$values args
Pass the values named by the list args to the continuation.
$prompt escape? tag handler
Push a prompt on the stack identified by the variable name tag and continue with zero values. If the body aborts to this prompt, control will proceed at the continuation labelled handler, which should be a $kreceive continuation. Prompts are later popped by pop-prompt primcalls.

The remaining element of the CPS language in Guile is the continuation. In CPS, all continuations have unique labels. Since this aspect is common to all continuation types, all continuations are contained in a $cont instance:

$cont k cont
Declare a continuation labelled k. All references to the continuation will use this label.

The most common kind of continuation binds some number of values, and then evaluates a sub-term. $kargs is this kind of simple lambda.

$kargs names syms body
Bind the incoming values to the variables syms, with original names names, and then evaluate the sub-term body.

Variables (e.g., the syms of a $kargs) should be globally unique. To bind the result of an expression a variable and then use that variable, you would continue from the expression to a $kargs that declares one variable. The bound value would then be available for use within the body of the $kargs.

$kif kt kf
Receive one value. If it is a true value, branch to the continuation labelled kt, passing no values; otherwise, branch to kf.

Non-tail function calls should continue to a $kreceive continuation in order to adapt the returned values to their uses in the calling function, if any.

$kreceive arity k
Receive values from a function return. Parse them according to arity, and then proceed with the parsed values to the $kargs continuation labelled k.

$arity is a helper data structure used by $kreceive and also by $kclause, described below.

$arity req opt rest kw allow-other-keys?
A data type declaring an arity. See Guile's manual for details.

Additionally, there are three specific kinds of continuations that can only be declared at function entries.

$kentry self tail clauses
Declare a function entry. self is a variable bound to the procedure being called, and which may be used for self-references. tail declares the $cont wrapping the $ktail for this function, corresponding to the function's tail continuation. clauses is a list of $kclause $cont instances.
A tail continuation.
$kclause arity cont
A clause of a function with a given arity. Applications of a function with a compatible set of actual arguments will continue to cont, a $kargs $cont instance representing the clause body.


Before starting Guile's compiler rewrite, I had no real-world experience with CPS-based systems. I had worked with a few SSA-based systems, and a few more direct-style systems. I had most experience with the previous direct-style system that Guile had, but never had to seriously design another kind of IL, so basically I was ignorant. It shows, I think; but time will tell if it came out OK anyway. At this point I am cautiously optimistic.

As far as fitness for purpose goes, the CPS IL works in the sense that it is part of a self-hosting compiler. I'll say no more on that point other than to mention that it has explicit support for a number of Guile semantic features: multiple-value returns; optional, rest, and keyword arguments; cheap delimited continuations; Guile-native constant literals.

Why not ANF instead? If you recall from my SSA and CPS article, I mentioned that ANF is basically CPS with fewer labels. It tries to eliminate "administrative" continuations, whereas Guile's CPS labels everything. There is no short-hand let form.

ANF proponents tout its parsimony as a strength, but I do not understand this argument. I like having labels for everything. In CPS, I have as many labels as there are expressions, plus a few for continuations that don't contain terms. I use them directly in the bytecode compiler; the compiler never has to generate a fresh label, as they are part of the CPS itself.

More importantly, labelling every control-flow point allows me to reason precisely about control flow. For example, if a function is always called with the same continuation, it can be incorporated in the flow graph of the calling function. This is called "contification". It is not the same thing as inlining, as it works for sets of recursive functions as well, and never increases code size. This is a crucial transformation for a Scheme compiler to make, as it turns function calls into gotos, and self-function calls into loop back-edges.

Guile's previous compiler did a weak form of contification, but because we didn't have names for all control points it was gnarly and I was afraid to make it any better. Now its contifier is optimal. See Fluet and Weeks' Contification using Dominators and Kennedy's CWCC, for more on contification.

One more point in favor of labelling all continuations. Many tranformations can be best cast as a two-phase process, in which you first compute a set of transformations to perform, and then you apply them. Dead-code elimination works this way; first you find the least fixed-point of live expressions, and then you residualize only those expressions. Without names, how are you going to refer to an expression in the first phase? It's nasty and much cleaner with the ubiquitous, through labelling that CPS provides.

So I am happy with CPS, relative to ANF. But what about SSA? In my previous article, I asked SSA proponents to imagine returning a block from a block. Of course it doesn't make any sense; SSA is a first-order language. But modern CPS is also first-order, is the thing! Modern CPS distinguishes "continuations" syntactically from functions, which is exactly the same as SSA's distinction between basic blocks and functions. CPS and SSA really are the same on this level.

The fundamental CPS versus SSA difference is, as Stephen Weeks noted a decade ago, one of data structures: do you group your expressions into basic blocks stored in a vector (SSA), or do you nest them into a scope tree (CPS)? It's not clear that I made the correct choice.

In practice with Guile's CPS you end up building graphs on the side that describe some aspect of your term. For example you can build a reverse-post-ordered control flow analysis that linearizes your continuations, and assigns them numbers. Then you can compute a bitvector for each continuation representing each one's reachable continuations. Then you can use this reachability analysis to determine the extent of a prompt's body, for example.

But this analysis is all on the side and not really facilitated by the structure of CPS itself; the CPS facilities that it uses are the globally unique continuation and value names of the CPS, and the control-flow links. Once the transformation is made, all of the analysis is thrown away.

Although this seems wasteful, the SSA approach of values having "implicit" names by their positions in a vector (instead of explicit ephemeral name-to-index mappings) is terrifying to me. Any graph transformation could renumber things, or leave holes, or cause vectors to expand... dunno. Perhaps I am too shy of the mutation foot-gun. I find comfort in CPS's minimalism.

One surprise I have found is that I haven't needed to do any dominator-based analysis in any of the paltry CPS optimizations I have made so far. I expect to do so once I start optimizing loops; here we see the cultural difference with SSA I guess, loops being the dominant object of study there. On the other hand I have had to solve flow equations on a few occasions, which was somewhat surprising, though enjoyable.

The optimizations I have currently implemented for CPS are fairly basic. Contification was tricky. One thing I did recently was to make all non-tail $call nodes require $kreceive continuations; if, as in the common case, extra values were unused, that was reflected in an unused rest argument. This required a number of optimizations to clean up and remove the extra rest arguments for other kinds of source expressions: dead-code elimination, the typical beta/eta reduction, and some code generation changes. It was worth it though, and now with the optimization passes things are faster than they were before.

Well, I find that I am rambling now. I know this is a lot of detail, but I hope that it helps some future compiler hacker understand more about intermediate language tradeoffs. I have been happy with CPS, but I'll report back if anything changes :) And if you are actually hacking on Guile, check the in-progress manual for all the specifics.

Happy hacking to all, and to all a good hack!

Syndicated 2014-01-12 21:58:00 from wingolog

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