Honest to a Segfault

The distinction between essential complexity and accidental complexity is a useful one — it allows you to identify the parts of your design where you're stumbling over yourself instead of working against something truly reflected in the problem domain.

The simplest-solution-that-could-possibly-work (SSTCPW) concept is inherently appealing in that, by design, you're trying to minimize these pieces that you may come to stumble over. Typically, when you take this approach, you acknowledge that an unanticipated change in requirements will entail major rework, and accept that fact in light of the perceived benefits.

Benefits cited typically include:

• Less design to validate.

• Less implementation to perform.

• Less surface area to debug.

• Increased confidence the resulting product executes properly (though perhaps modestly in scope).

As a more quantifiable example: if a SSTCPW contains comparatively less code paths than an alternative solution, you can see how some of the above merits could fall out of it.

This also demonstrates some of the appeal of fail-fast and crash-only approaches to software implementation, in that cutting out unanticipated program inputs and states, via an acceptance of "failure" as a concept, tends to hone in on SSTCPW.

Contrast

In my head, this approach is contrasted most starkly against an approach called big-design-up-front (BDUF). The essence of BDUF is that, in the design process, one attempts to consider the whole set of possible requirements (typically both currently-known and projected) and build into the initial design and implementation the flexibility and structure to accommodate large swaths of them in the future, if not in the current version.

In essence, this approach acknowledges that the target is likely moving, tries to anticipate the target's movement, and takes steps to remain one step ahead of the game by building in flexibility, genericity, and a more 1:1-looking mapping between the problem domain and the code constructs.

Benefits cited usually relate to ongoing maintenance in some sense and typically include:

• Reuse via genericity.

• Flexibility for feature addition.

• A more robust model of the problem domain imbued in the program.

Head to head

In a lot of software engineering doctrine that I've read, been taught, and toyed with throughout the years, the prevalence of unknown and ever-changing business requirements for application software has lent a lot of credence to BDUF, especially in that space.

There have also been enabling trends for this mentality; for example, the introduction of indirection through abstractions has monumentally less cost on today's JVM than on the Java interpreter of yore. In that same sense, C++ has attempted to satisfy an interesting niche in the middle ground with its design concept of "zero cost abstractions", which intend to be known-reducible to more easily understood and more predictable underlying code forms at compile time. On the hardware side, the steady provisioning of single-thread performance and memory capacity throughout the years has also played an enabling role.

By contrast, the system-software implementation doctrine and conventional wisdom skews heavily towards SSTCPW, in that any "additional" design reflected in the implementation tends to come under higher levels of duress from a {performance, code-size, debuggability, correctness} perspective. Ideas like "depending on concretions" — which I specifically use because it's denounced by the D in SOLID — are wholly accepted in SSTCPW given that it (a) makes the resulting artifact simpler to understand in some sense (b) without sacrificing the ability to meet necessary requirements.

So what's the underlying trick in acting on a SSTCPW philosophy? You have to do enough design work (and detailed engineering legwork) to distinguish between what is necessary and what is wanted, and have some good-taste arbitration process to distinguish between the two when there's disagreement about the classification. As part of that process, you have to make the most difficult decisions: what you definitely will not do and what the design will not accommodate without major rework.

In reply

Andrew (@ndrwdn) asked a great followup question to the last entry on systems programming at my alma mater:

What follows are a few quick-and-general pointers on "I want to start doing lower level stuff, but need a motivating direction for a starter project." They're somewhat un-tested because I haven't mentored any apps-to-systems transitions, but, as somebody who plays on both sides of that fence, I think they all sound pretty fun.

A word of warning: systems programming may feel crude at first compared to the managed languages and application-level design you're used to. However, even among experts, the prevalence of footguns motivates simple designs and APIs, which can be a beautiful thing. As a heuristic, when starting out, just code it the simple, ungeneralized way. If you're doing something interesting, hard problems are likely to present themselves anyhow!

Microcontrollers rock

Check out sites like hackaday.com to see the incredible feats that people accomplish through microcontrollers and hobby time. When starting out, it's great to get the tactile feedback of lighting up a bright blue LED or successfully sending that first UDP packet to your desktop at four in the morning.

Microcontroller-based development is also nice because you can build up your understanding of C code, if you're feeling rusty, from basic usage — say, keeping everything you need to store as a global variable or array — to fancier techniques as you improve and gain experience with what works well.

Although I haven't played with them specifically, I understand that Arduino boards are all the rage these days — there are great tutorials and support communities out on the web that love to help newbies get started with microcontrollers. AVR freaks was around even when I was programming on my STK500. I would recommend reading some forums to figure out which board looks right for you and your intended projects.

At school, people really took to Bruce Land's microcontroller class, because you can't help but feel the fiero as you work towards more and more ambitious project goals. Since that class is still being taught, look to the exercises and projects (link above) as good examples of what's possible with bright students and four credits worth of time. [*]

Start fixing bugs on low-level open source projects

Many open source projects love to see willing new contributors. Especially check out projects a) that are known for having good/friendly mentoring and b) that you think are cool (which will help you stay motivated).

I know one amazing person I worked with at Mozilla got into the project by taking his time to figure out how to properly patch some open bugs. If you take that route, either compare your patch to what the project member has already posted, or request that somebody give you feedback on your patch. This is another good way to pick up mentor-like connections.

Check out open courseware for conceptual background

I personally love the rapid evolution of open courseware we're seeing. If you're feeling confident, pick a random low-level thing you've heard-of-but-never-quite-understood, type it into a search engine, and do a deep dive on a lecture or series. If you want a more structured approach, a simple search for systems programming open courseware has quite educational looking results.

General specifics: OSes and reversing

OSes

If you're really into OSes, I think you should just dive in and try writing a little kernel on top of your hardware of choice in qemu (a hardware emulator). Quick searches turn up some seemingly excellent tutorials on writing simple OS kernels on qemu, and writing simple OSes for microcontrollers is often a student project topic in courses like the one I mention above. [†]

With some confidence, patience, maybe a programming guide, and recall of some low-level background from school, I think this should be doable. Some research will be required on effective methods of debugging, though — that's always the trick with bare metal coding.

Or, for something less audacious sounding: build your own Linux kernel with some modifications to figure out what's going on. There are plenty of guides on how to do this for your Linux distribution of choice, and you can learn a great deal just by fiddling around with code paths and using printk. Try doing something on the system (in userspace) that's simple to isolate in the kernel source using grep — like mmapping /dev/mem or accessing an entry in /proc — to figure out how it works, and leave no stone unturned.

I recommend taking copious notes, because I find that's the best way to trace out any complex system. Taking notes makes it easy to refer back to previous realizations and backtrack at will.

Read everything that interests you on Linux Kernel Newbies, and subscribe to kernel changelog summaries. Attempt to understand things that interest you in the source tree's /Documentation. Write a really simple Linux Kernel Module. Then, refer to freely available texts for help in making it do progressively more interesting things. Another favorite read of mine was Understanding the Linux Kernel, if you have a hobby budget or a local library that carries it.

Reversing

This I know less about — pretty much everybody I know that has done significant reversing is an IDA wizard, and I, at this point, am not. They are also typically Win32 experts, which I am not. Understanding obfuscated assembly is probably a lot easier with powerful and scriptable tools of that sort, which ideally also have a good understanding of the OS. [‡]

However, one of the things that struck me when I was doing background research for attack mitigation patches was how great the security community was at sharing information through papers, blog entries, and proof of concept code. Also, I found that there are a good number of videos online where security researchers share their insights and methods in the exploit analysis process. Video searches may turn up useful conference proceedings, or it may be more effective to work from the other direction: find conferences that deal with your topic of interest, and see which of those offer video recordings.

During my research on security-related things, a blog entry by Chris Rohlf caused Practical Malware Analysis to end up on my wishlist as an introductory text. Seems to have good reviews all around. Something else to check out on a trip to the library or online forums, perhaps.

Footnotes

Bryan also asked me this at NodeConf last year, where I was chatting with him about the then-in-development IonMonkey:

I remembered my talk with Bryan when I went to recruit there last year and asked the same interview question that he references — except with the pointer uninitialized so candidates would have to enumerate the possibilities — to see what evidence I could collect. My thoughts on the issue haven't really changed since that chat, so I'll just repeat them here.

(And, although I do not speak for my employer, for any programmers back in Ithaca who think systems programming and stuff like Birman's class is cool beans, my team is hiring both full time and interns in the valley, and I would be delighted if you decided to apply.)

My overarching thought: bring the passion

Many of the people I'm really proud that my teams have hired out of undergrad are just "in love" with systems programming, just as a skilled artisan "cares" about their craft. They work on personal projects and steer their trajectory towards it somewhat independent of the curriculum.

Passion seems to be pretty key, along with follow-through, and ability to work well with others, in the people I've thumbs-up'd over the years. Of course I always want people who do well in their more systems-oriented curriculum and live in a solid part the current-ability curve, but I always have an eye out for the passionately interested ones.

So, I tend to wonder: if an org has a "can systems program" distribution among the candidates, can you predict the existence of the outliers at the career fair from the position of the fat part of that curve?

Anecdotally, myself and two other systems hackers on the JavaScript engine came from the same undergrad program, modulo a few years, although we took radically different paths to get to the team. They are among the best and most passionate systems programmers I've ever known, which also pushes me to think passionate interest may be a high-order bit.

Regardless, it's obviously in systems companies' best interest to try to get the most bang per buck on recruiting trips, so you can see how Bryan's point of order is relevant.

My biased take-away from my time there

I graduated less than a decade ago, so I have my own point of reference. From my time there several years ago, I got the feeling that the mentality was:

• C/C++ are horrible teaching languages, so they shouldn't really be taught in general curricula in circumstances where they can be avoided.

• Java and applications-level programming is where most of the well-paying industry jobs are. (Not sure how true this is or was, but it seemed to be the conventional wisdom at the time.)

• It's a Windows world. And, if it's not a Windows world, you've probably got a VM under you.

This didn't come from any kind of authority, it's just putting into words the "this is how things are done around here" understanding I had at the time. All of them seemed reasonable in context, though I didn't think I wanted to head down the path alluded by those rules of thumb. Of course these were, in the end, just rules of thumb: we still had things like a Linux farm used by some courses.

I feel that the "horrible for teaching" problem extends to other important real-world systems considerations as well: I learned MIPS and Alpha [*], presumably due to their clean RISC heritage, but golly do I ever wish I was taught more about specifics of x86 systems. And POSIX systems. [†]

Of course that kind of thing — picking a "real-world" ISA or compute platform — can be a tricky play for a curriculum: what do you do about the to-be SUN folks? Perhaps you've taught them all this x86-specific nonsense when they only care about SPARC. How many of the "there-be-dragons" lessons from x86 would cross-apply?

There's a balance between trade and fundamentals, and I feel I was often reminded that I was there to cultivate excellent fundamentals which could later be applied appropriately to the trends of industry and academia.

But seriously, it's just writing C...

For my graduating class, CS undergrad didn't really require writing C. The closest you were forced to get was translating C constructs (like loops and function calls) to MIPS and filling in blanks in existing programs. You note the bijection-looking relationship between C and assembly and can pretty much move on.

I tried to steer to hit as much interesting systems-level programming as possible. To summarize a path to learning a workable amount of systems programming in my school of yore, in hopes it will translate to something helpful existing today:

• You may have read K&R, but as a newbie it makes sense to beef up on fundamentals, so CS 116: Introduction to C Programming doesn't hurt (and you meet other passionate systems programming people in the process).

• CS 415: Operating Systems Practicum made you write C. Sadly, we were given a library for context switching userspace threads on top of the Win32 API in MSVC that we didn't really have to dig into. We had to write things like concurrency primitives, a scheduler, and a rudimentary filesystem that operated in terms of a soft (i.e. fake) disk model. I think there may have been some networking in there as well. The course was being revamped at the time, so I hope it's more bare-metal now with something practical like qemu.

• ECE 476: Designing with Microcontrollers was an amazing class for integrating whatever you were most passionate about from CS and ECE curricula. Though at the time we were using 8-bit Atmels on a proprietary compiler that had no dynamic allocation support, you had to write both assembly and C code and talk to your system board via I/O ports. Plus, I got to be a little sneaky and use avr-gcc.

• ECE 473: Optimizing Compilers targeted Alpha at the time, but was a great big systems project that taught a lot about machine specifics and code generation (interfacing to syscalls, executable and linkable formats).

• ECE 575: High-Performance Microprocessor Architecture made you write real and well-performing C applications for things like cache modeling with static binary translation. This was a very formative course for me.

• I did a bunch of independent projects to mess around and better understand areas where I was lacking knowledge.

• I did work with systems researchers at the university. Some were unwilling to take any undergrads as a policy, but some groups are more amenable.

I'm not a good alum in failing to keep up with the goings-ons but, if I had a recommendation based on personal experience, it'd be to do stuff like that. Unfortunately, I've also been at companies where the most basic interview question is "how does a vtable actually work" or on nuances of C++ exceptions, so for some jobs you may want to take an advanced C++ class as well.

Understanding a NULL pointer deref isn't writing C

Eh, it kind of is. On my recruiting trip, if people didn't get my uninitialized pointer dereference question, I would ask them questions about MMUs if they had taken the computer organization class. Some knew how an MMU worked (of course, some more roughly than others), but didn't realize that OSes had a policy of keeping the null page mapping invalid.

So if you understand an MMU, why don't you know what's going to happen in the NULL pointer deref? Because you've never actually written a C program and screwed it up. Or your haven't written enough assembly with pointer manipulation. If you've actually written a Java program and screwed it up you might say NullPointerException, but then you remember there are no exceptions in C, so you have to quickly come up with an answer that fits and say zero.

I think another example might help to illustrate the disconnect: the difference between protected mode and user mode is well understood among people who complete an operating systems course, but the conventions associated with them (something like "tell me about init"), or what a "traditional" physical memory space actually looks like, seem to be out of scope without outside interest.

This kind of interview scenario is usually time to fluency sensitive — wrapping your head around modern C and sane manual memory management isn't trivial, so it does require some time and experience. Plus when you're working regularly with footguns, team members want a basic level of trust in coding capability. It's not that you think the person can't do the job, it's just not the right timing if you need to find somebody who can hit the ground running. Bryan also mentions this in his email.

Thankfully for those of us concerned with the placement of the fat part of the distribution, it sounds like Professor Sirer is saying it's been moving even more in the right direction in the time since I've departed. And, for the big reveal, I did find good systems candidates on my trip, and at the same time avoided freezing to death despite going soft in California all these years.

Brain teaser

I'll round this entry off with a little brain teaser for you systems-minded folks: I contend that the following might not segfault.

// ...

int main() {
    mysterious_function();
    A *a = NULL;
    printf("%d\n", a->integer_member);
    return EXIT_SUCCESS;
}

How many reasons can you enumerate as to why? What if we eliminate the call to the mysterious function?

Footnotes

[Latest from the "I can't believe I'm writing a blog entry about this" department, but the context and surrounding discussion is interesting. --Ed]

If you're like me, or one of the other thousands of concerned parents who has borne C code into this cruel, topsy-turvy, and oftentimes undefined world, you read the C standard aloud to your programs each night. It's comforting to know that K&R are out there, somewhere, watching over them, as visions of Duff's Devices dance in their wee little heads.

The shocking truth

In all probability, you're one of today's lucky bunch who find out that the signedness of the char datatype in C is undefined. The implication being, when you write char, the compiler is implicitly (but consistently) giving it either the signed or unsigned modifier. From the spec: [*]

The three types char, signed char, and unsigned char are collectively called the character types. The implementation shall define char to have the same range, representation, and behavior as either signed char or unsigned char.

...

Irrespective of the choice made, char is a separate type from the other two and is not compatible with either.

—ISO 9899:1999, section "6.2.5 Types"

Why is char distinct from the explicitly-signed variants to begin with? A great discussion of historical portability questions is given here:

Fast forward [to 1993] and you'll find no single "load character from memory and sign extend" in the ARM instruction set. That's why, for performance reasons, every compiler I'm aware of makes the default char type signed on x86, but unsigned on ARM. (A workaround for the GNU GCC compiler is the -fsigned-char parameter, which forces all chars to become signed.)

—Portability and the ARM Processor, Trevor Harmon, 2003

It's worth noting, though, that in modern times there are both LDRB (Load Register Byte) and LDRSB (Load Register Signed Byte) instructions available in the ISA that do sign extension after the load operation in a single instruction. [†]

So what does this mean in practice? Conventional wisdom is that you use unsigned values when you're bit bashing (although you have to be extra careful bit-bashing types smaller than int due to promotion rules) and signed values when you're doing math, [‡] but now we have this third type, the implicit-signedness char. What's the conventional wisdom on that?

Signedness-un-decorated char is for ASCII text

If you find yourself writing:

char some_char = NUMERIC_VALUE;

You should probably reconsider. In that case, when you're clearly doing something numeric, spring for a signed char so the effect of arithmetic expressions across platforms is more clear. But the more typical usage is still good:

char some_char = 'a';

For numeric uses, also consider adopting a fixed-width or minimum-width datatype from <stdint.h>. You really don't want to hold the additional complexity of char signedness in your head, as integer promotion rules are already quite tricky.

Examples to consider

Some of the following mistakes will trigger warnings, but you should realize there's something to be aware of in the warning spew (or a compiler option to consider changing) when you're cross-compiling for ARM.

Example of badness: testing the high bit

Let's say you wanted to see if the high bit were set on a char. If you assume signed chars, this easy-to-write comparison seems legit:

if (some_char < 0)

But if your char type is unsigned that test will never pass.

Example of badness: comparison to negative numeric literals

You could also make the classic mistake:

char c = getchar(); // Should actually be placed in an int!
while (c != EOF)

This comparison would never return true with an 8-bit unsigned char datatype and a 32-bit int datatype. Here's the breakdown:

When getchar() returns ((signed int) -1) to represent EOF, you'll truncate that value into 0xFFu (because chars are an unsigned 8-bit datatype). Then, when you compare against EOF, you'll promote that unsigned value to a signed integer without sign extension (preserving the bit pattern of the original, unsigned char value), and get comparison between 0xFF (255 in decimal) and 0xFFFFFFFF (-1 in decimal). For all the values in the unsigned char range, I hope it's clear that this test will never pass. [§]

To make the example a little more obvious we can replace the call to getchar() and the EOF with a numeric -1 literal and the same thing will happen.

char c = -1;
assert(c == -1); // This assertion fails. Yikes.

That last snippet can be tested by compiling in GCC with -fsigned-char and -funsigned-char if you'd like to see the difference in action.

Footnotes

Disclaimer

I've caught some flak over publishing my "selfish" (read: empirical testing that yields results which are only relevant to me) multi-language-engine-and-standard-library "shootout" (read: I wrote the same basic functionality across multiple languages, somewhat like on the shootout.alioth.debian.org site, the Computer Language Benchmarks Game). I value the concept and process of learning in the open, but it may require more time and consideration of clarity than I had given in this entry. Taking it down would apparently be a breach of etiquette, so please read the following TL;DR as a primer.

TL;DR: I encourage you to personally try writing small utilities against a variety of language engines when you have the opportunity. Consider how much tweaking of the original code you have to do in order to obtain a well-performing implementation. Weigh the development effort and your natural proficiency against the performance, clarity, and safety of the resulting program. Gather evidence and be eager to test your cost assumptions. Commit to learning about sources of overhead and unforeseen characteristics of your libraries. You may be surprised which engines give the best bang per time spent.

It has also been suggested to me that all native languages are within ~3x of one another on generated code performance, and the rest of the difference is generally attributable to the library or algorithm, so that's an interesting rule of thumb to keep in mind.

If you'd like to see how to write a small utility against a variety of language engines, you can check out the Github repo.

Introduction

We tend to throw around "orders of magnitude" when it comes to "programming language speeds", even though we know that the concept of a programming language having a speed for arbitrary programs makes little sense. But, when I'm coding up something small, I find myself pondering a very concrete question: which available language engine (language implementation and libraries) could I reasonably write this little program against that would give the best speed over development effort?

I'm not looking to optimize all the buttery nooks and crannies of this program, nor do I want to drill into potential deficiencies in the I/O subsystem: I just want to make a painless little utility that doesn't require me to go on a lunch break.

XKCD knows what I'm talking about:

I was writing a very simple, single-threaded program to generate about a billion uniformly random int32s in a text file, and I decided I would do a selfish little shootout: write the same program in a set of "viable" languages (remember, this is all about me :-), unscientifically use time(1) on the programs a few times, consider how painful it was to write, and see what the runtimes come out to be.

For 100 million integers on my CentOS Bloomfield box, these were the runtimes for my initial, naive implementations and their lightly tweaked counterparts:

Like all developers, I have varied levels of expertise across languages and their standard libraries; but, as I said, this is a selfish shootout, so my competence in a given language is considered part of the baseline. You'll see in the comments that many readers identified performance bugs in these code samples.

There are also caveats for the random numbers I was generating in OCaml (due to tag bit stealing).

For a billion integers the naive C++0x version took 1m 42s and the naive Java version took 2m 18s (1.35x slower). I didn't want to spend the time to slow down the others by an order of magnitude.

As a result — with perpetual intent to improve my abilities in all engines I work with, willful ignorance of the reasoning, acknowledgement that I need to perform more experiments like this to draw a more reasonable conclusion over time, and malice aforethought — I'll hereby declare myself guilty of leaning a bit more towards writing things like this in C++ when I want better runtimes in the giga range for little IO-and-compute programs.

Show me the code!

I threw the code up on github, but the versions that I wrote naively (before optimization suggestions) are duplicated here for convenience.

C++0x:

#include <cstdlib>
#include <cstdio>
#include <random>

int
main(int argc, char **argv)
{
    if (2 != argc) {
        fprintf(stderr, "Usage: %s <elem_count>\n", argv[0]);
        return EXIT_FAILURE;
    }

    long long count = atoll(argv[1]);

    std::mt19937 rng;
    rng.seed(time(NULL));

    std::uniform_int<int32_t> dist;

    FILE *file = fopen("vec_gen.out", "w");
    if (NULL == file) {
        perror("could not open vector file for writing");
        return EXIT_FAILURE;
    }

    for (long long i = 0; i < count; ++i) {
        int32_t r = dist(rng);
        fprintf(file, "%d\n", r);
    }

    fclose(file);
    return EXIT_SUCCESS;
}

Java:

import java.io.BufferedWriter;
import java.io.FileWriter;
import java.util.Random;
import java.io.IOException;

class VecGen
{
    static final int INT_MAX = 2147483647;

    public static void main(String args[]) {
        if (args.length != 1) {
            System.err.println("Usage: VecGen <elem_count>");
            System.exit(-1);
        }

        int count = Integer.parseInt(args[0]);

        try {
            FileWriter fw = new FileWriter("vec_gen.out");
            BufferedWriter bw = new BufferedWriter(fw);

            Random rng = new Random();

            for (int i = 0; i < count; ++i) {
                int r = rng.nextInt(INT_MAX);
                bw.write(r + "\n");
            }

            bw.close();
        } catch (IOException e) {
            System.err.println("Received I/O exception: " + e);
            System.exit(-2);
        }
    }
};

Python:

import random
import sys

def main():
    if len(sys.argv) != 2:
        print >> sys.stderr, "Usage: %s <elem_count>" % sys.argv[0]
        exit(-1)

    count = int(sys.argv[1])
    random.seed()

    with open('vec_gen.out', 'w') as file:
        for i in xrange(count):
            r = random.getrandbits(31)
            print >> file, r

if __name__ == '__main__':
    main()

OCaml:

if (Array.length Sys.argv) <> 2 then (
  let msg = "Usage: " ^ (Array.get Sys.argv 0) ^ " <elem_count>\n" in
  prerr_string msg;
  exit (-1)
) else (
  let count = int_of_string (Array.get Sys.argv 1) in
  let file = open_out "vec_gen.out" in
  let rec write_rand_line n =
    if n = 0 then ()
    else
      let r = Random.bits () in
      output_string file ((string_of_int r) ^ "\n");
      write_rand_line (n - 1)
  in
  write_rand_line count;
  exit 0
)

Lua:

function main(args)
    if #args ~= 1 then
        io.stderr:write("Usage: " .. args[0] .. " <elem_count>\n")
        os.exit(-1)
    end

    local count = tonumber(args[1])

    math.randomseed(os.time())

    local upper = math.floor(2^31 - 1)

    io.output(io.open("vec_gen.out", "w"))
    for i = 1,count do
        local r = math.random(0, upper)
        io.write(r)
        io.write("\n")
    end

    io.close()
end

main(arg)

Rust:

import io::writer_util;

fn main(args: [str]) {
    if vec::len(args) != 2u {
        let usage = #fmt("Usage: %s <elem_count>\n", args[0]);
        io::stderr().write_str(usage);
        os::set_exit_status(-1);
        ret;
    }

    let count = option::get(int::from_str(args[1]));
    let rng = rand::seeded_rng(rand::seed());

    let fw = result::get(io::buffered_file_writer("vec_gen.out"));
    let mut i = 0;
    while i < count {
        let r = rng.next() & (0x7fffffffu as u32);
        fw.write_line(int::to_str(r as int, 10u));
        i += 1;
    }
}

Go:

package main

import (
    "bufio"
    "fmt"
    "log"
    "math/rand"
    "os"
    "strconv"
)

func main() {
    if len(os.Args) != 2 {
        fmt.Fprintf(os.Stderr, "usage: %s <elem_count>\n", os.Args[0])
        os.Exit(-1)
    }

    count, err := strconv.Atoi(os.Args[1])
    if err != nil { panic(err) }

    file, err := os.Create("vec_gen.out")
    if err != nil { panic(err) }
    defer file.Close()

    bw := bufio.NewWriter(file)
    defer func() {
        err := bw.Flush()
        if err != nil { log.Fatal(err) }
    }()

    for i := 0; i < count; i++ {
        r := rand.Int31()
        fmt.Fprintf(bw, "%d\n", r)
    }
}

Ruby:

def main()
    if ARGV.length != 1 then
        warn "Usage: #{$0} <elem_count>"
        exit -1
    end

    count = ARGV[0].to_i
    file = File.open "vec_gen.out", "w"
    upper = 2147483647

    for i in 1..count do
        r = rand(upper)
        file.write r
        file.write "\n"
    end
end

main()

Updates

2012-06-03 2100

Reflect Makefile switch to ocaml native compiler, I was using the bytecode compiler.

2012-06-04 1500

Add Ruby, because I seem to remember enough of it.

2012-06-04 1930

Update Rust numbers per Graydon's comment. The code under test remained unchanged for the entry's inline results table.

By the 1840s, a "Railroad Mania" was raging, with stocks selling on multiples of passenger miles, a precursor for multiples of page views that Yahoo stock would trade on 150 years later. An inventor named Charles Babbage complained that "the railroad mania withdrew from other pursuits the most intellectual and skillful draftsmen." [...] Investors made money, investors lost money, but in the best and worst of times, the railroads got built, and people and goods were shuffled about.

—How We Got Here, A Slightly Irreverent History of Technology and Markets

I think that, these days, Babbage would be looking for the systems programmers.