Don’t use wrapper

It’s a bad word for a piece of code, and you should feel bad for using it. Here is why:

1. It is easily phonetically confused with “rapper”

Well, this one is actually funny. Really the only redeeming quality. So if someone tells me that they “made a wrapper”, I immediately giggle a bit inside.

2. Wrapping things is a programmers job.

As programmers, we are in the business of abstractions, and a function clearly is an abstraction. A function that calls something else wraps that something else. So isn’t everything a wrapper?
Who would say that the following function is a wrapper?

template <class T>
T multiplication_wrapper(T a, T b)
{
  return a * b;
}

It does wrap the multiplication operator, does it not? Of course, the example is contrived, but many people call equally simple functions “wrapper functions”.

3. It is often a bad analogy

When you wrap something, like a present, you first need to unwrap it to actually use it. So in that case, it acts more like a kind of envelope. This is clearly not the case for what most people call wrappers. You could wrap some data in a .zip file – that would make sense! But no one uses it like that.
Another use of the word wrap implies something that goes around something else, forming a fixture of sorts. Like a wraparound baby sling. So I guess this could work for some uses, like a protection layer. Again, it is not used like that.
Finally, there’s wrapping up something, as in finishing something. Well, maybe if you’re wrapping your main function around the rest of your code, you can finish writing your program. A very monolithic approach.

There are plenty of better alternatives

In most cases, what people should rather use is either facade or adapter. Both names convey a lot more meaning than wrapper. A facade is something that wraps code to make the interface nicer. An adapter wraps an interface to integrate with some other piece of code. Both are structural design patterns. Both wrap something. But then again, that could be said for all of the structural design patterns. Or, most code. Except maybe assembler?

So please, calling something a wrapper is not enough. You might as well just call it function/object/abstraction. Use adapter, facade, decorator, proxy etc.. The why is more important than the what.

How to teach C++

In the closing Keynote of this year’s Meeting C++, Nicolai Josuttis remarked how hard it can be to teach C++ with its ever expanding complexity. His example was teaching rookies about initialization in C++, i.e. whether to use assignment =, parens () or curly braces {}. He also asked for more application-level programmers to participate.

Well, I am an application programmer, and I also have experience with teaching C++. Last year I held a C++ introductory course for experienced C programmers who mostly had never used C++ before. From my experience, I can completely agree with what Nico had to say about teaching C++. The language and its subtlety can be truly overwhelming.

Most of the complexity in C++ boils down to tuning your code for optimal performance. We cannot just leave that out, can we? After all, the sole reason to use C++ is performance, right?

My approach

Performance is one key reason for using C++, no doubt about it. There is a few more, but let us not get distracted. What is even more important is the potential to optimize for performance. But you can do that later! It is actually quite crazy what performance crimes you can get away with in C++ and still have something pretty fast overall, especially with move-semantics and improved RVO.

Given that, I picked a simple subset to start with:

  • Pass by-value only, do not use pointers nor references.
  • Structure your programs around simple data-only structs and functions transforming them.
  • Make good use of the data structures and algorithms from std.

Believe me, you too can write pretty useful programs this way. This is not too far from a data-oriented style anyways.
So, yes, we can actually leave out the performance specific parts for quite some time, while still making sure our programs can be optimized eventually.

And from there?

You can gradually start introducing references and move semantics, when measurement shows copying affecting the performance. This way you can introduce tooling like a profiler and see the effects off passing things around by reference in a language where everything, by default, is passed by value. These things will start to make sense in a context.

The arguably better approach to move semantics is of course types that prevent you from copying, but allow moving. Most resources with side-effects are like this: file handles, locks, threads. You will need to introduce RAII for this to make sense, e.g. writing ctors and dtors.

But you still do not need to write templates, use virtual-function polymorphism, or even pointers. But when you get to those, you will have a good context to use them.

Have you tought C++ and some experience to share? I would like to hear about it!

3 rules for projects under version control

Checking out a project under version control should be easy and repeatable. Here are a few tips on how to achieve that:

1. Self-containment

You should not need a specifically configured machine to start working on a project. Ideally, you clone the project and get started. Many things can be a problem to achieve that.
Maybe your project is needs a specific operating system, dependency installed, hardware or database setup to run. I personally draw that line at this:
You get a manual with the code that helps you to set up your development environment once and this should be as automated & easy as possible. You should always be able to run your projects without periphery, i.e. specific hardware that can be plugged in or databases that need to be installed.
To achieve this for hardware, you can often fake it via polymorphic interfaces and dependency injection, much like mocking it for testing. The same can be done with databases – or you can use in-memory databases as a fallback.

2. Separate build-artifacts

Building the project into an executable form should be clearly separated from the source-controlled files. For example, the build should never ever modify a file that is under version control. Ideally, the “build” directory is completely independent from the source – enabling a true out-of-source build. However, it is often a good middle ground to allow building in a few dedicated directories in your source repository – but these need to be in .gitignore.

3. Separate runtime data

In the same way, running your project should not touch any source controlled files. Ideally, the project can be run out-of-source. This is trivial for small programs that do not have data, but once some data needs to be managed by the source control system, it gets a little more tricky for the executables to find the data. For data that needs to be changed by the program (we call these “stores”), it is advisable to maintain templates in the VCS or in codes, and copy them to the runtime directory during the build process. For data that is not changed by running the program, such as images, videos, translation-tables etc., you can copy them as well, or make sure the program finds them in the source repository.

Following these guidelines will make it easier to work with version control, especially when multiple people are involved.

luabind deboostified tips and tricks

luabind deboostified is a fork of the luabind project that helps exposing APIs to Lua. As the name implies, it replaces the boost dependency with modern C++, which makes it a lot more pleasant to work with.

Here are a few tips and tricks I learned while working with it. Some tricks might be applicable to the original luabind – I do not know.

1. Splitting module registration

You can split the registration code for different classes. I usually add a register function per class, like this:

struct A {
  void doSomething();
  static luabind::scope registerWithLua();
};

struct B {
  void goodStuff();
  static luabind::scope registerWithLua();
};

You can then combine their registration code into a single module on the Lua side:

void registerAll(lua_State* L) {
  luabind::module(L)[
    A::registerWithLua(),
    B::registerWithLua()];
}

The implementation of a registration function looks like this:

luabind::scope A::registerWithLua()
{
  return luabind::class_<A>("A")
    .def("doSomething", &A::doSomething);
}

2. Multiple policies and multiple return values

Unlike C++, Lua has real multiple return values. You can use that by utilizing the return value policies that luabind offers. Lets say, you want to write this in Lua:

local x, y = a.getPosition()

The C++ side could look like this:

void getPosition(A const& a, float& x, float& y);

The deboostified fork needs its policies supplied in a type list. Let’s use a small helper meta-function to build that:

template <typename... T>
using joined = 
  typename luabind::meta::join<T...>::type;

Once you have that, you can expose it like this:

luabind::def("getPosition", &getPosition,
              joined<
                luabind::pure_out_value<2>,
                luabind::pure_out_value<3>
              >());

3. Specialized data structures using luabind::object

Using the converters in luabind is not the only way to make Lua values from C++. Almost everything you can do in Lua itself, you can do with luabind::object. Here is a somewhat contrived example:

luabind::object repeat(luabind::object what,
                       int count) {
  // Create a new table object
  auto result = luabind::newtable(
    what.interpreter());
  // Fill it as an array [1..N]
  for (int i = 1; i <= count; ++i)
    result[i] = what;
  return result;
}

This function can then be exported via luabind::def and used just like any other function. This is just the tip of the iceberg, though. For example, you can also write functions that, at runtime, behave differently when a number is passed in as when a table is passed in. You can find out the Lua type with luabind::type(myObject).

Of course, as soon as you want to create new objects to return to Lua, you need the lua_State pointer in that function. Using the interpreter from a passed-in luabind::object is one way, but I have yet to find another pleasant way to do this. It is probably possible to use the policies to do this, and have them pass that in as a special parameter, but for now I am using some complicated machinery to bind lambda functions that capture the Lua interpreter.

That’s it for now..

Keep in mind that these are not thoroughly researched best-practices, but patterns I have used to solve actual problems. There might be better solutions out there – if you know any, please let me know. Hope this helped!

Decoding non-utf8 server responses using the Fetch API

The new Javascript Fetch API is really nice addition to the language and my preferable, and in fact the only bearable, way to do server requests.
The Promise based API is a lot nicer than older, purely callback-based, approaches.

The usual approach to get a text response from a server using the Fetch API looks like this:

let request = fetch(url)
  .then(response => response.text())
  .then(handleText);

But this has one subtle problem:

I was building a client application that reads weather data from a small embedded device. We did not have direct access to changing the functionality of that device, but we could upload static pages to it, and use its existing HTML API to query the amount of registered rainfall and lightning strikes.

Using the fetch API, I quickly got the data and extracted it from the HTML but some of the identifiers had some screwed up characters that looked like decoding problems. So I checked whether the HTTP Content-Type was set correctly in the response. To my surprise it was correctly set as Content-Type: text/html; charset=iso-8859-1.

So why did my Javascript Application not get that? After some digging, it turned out that Response’s text() function always decodes the payload as utf-8. The mismatch between that and the charset explained the problem!

Obviously, I had to do the decoding myself. The solution I picked was to use the TextDecoder class. It can decode an ArrayBuffer with a given encoding. Luckily, that is easy to get from the response:

let request = fetch(url)
  .then(response => response.arrayBuffer())
  .then(buffer => {
    let decoder = new TextDecoder("iso-8859-1");
    let text = decoder.decode(buffer);
    handleText(text);
  });

Since I only had to support that single encoding, that worked well for me. Keep in mind that the TextDecoder is still experimental Technology. However, we had a specific browser as a target and it works there. Lucky us!

Game Optimization Resolved

In my last blog post, I explained a performance problem in my game abstractanks but not how I solved it.

So I had not done any optimization work in a while, so the first thing I did turned out to be an error. And not only in hindsight – I actually knew how to tackle a problem like that – I just temporarily forgot at that point.

Going down the rabbit hole

Where we left off, my profiler showed FriendlyUnitOccupies as the culprit. That function basically does circle/circle collision detection using a quad-tree as the spatial acceleration structure. Looking at the samples from my profiler, I could see that that it was descending into the tree quite deeply. Like all tree structures, a quad-tree does pointer-chasing which is very bad for modern CPUs. So I figured I should look at how to optimize that. The data structure was implemented in a hurry, so there seemed plenty to do:

  • Instead of recursing into each node, use tail-call optimization and early culling to speed up traversal.
  • Pre-cache the query with the max-search radius and the other requirements to the units, e.g. not dead, same team, etc.. and then use that to build a new tree for the actual queries.

Because the data structure was pretty non-generic, I started to basically rewrite it to use it in this scenario. While I was about half way through with that, it dawned on me that I was barking at the wrong tree.

Taking a step back

The excellent book Video Game Optimization has some great advice on which level to attack an optimization problem.

  1. System-level. Can you change the system to do something differently and still solve your problem?
  2. Algorithm-level. Are you using the most efficient right algorithm for the data you have?
  3. Micro-level. Are you not wasting any processing power on the lower levels?

I was already on the algorithm level. So I went back to the systemic level: What if the AI did not try to change the target position that often, maybe just every few seconds? That effectively meant lowering the AIs APM. It’s not a bad solution, especially since that makes the AI behave more human. But on the other hand, real-time games, as the name implies, have a soft real-time requirement. So you generally like to avoid huge workloads that go over your frame budget. With how slow the algorithm was, that could easily be the case. The solution is then to do the work concurrently, either by splitting it up or doing it in the background. Both solutions seemed difficult, since the AI code does currently not allow for easy concurrency. So that idea was out.

What if the parking-positions where cached? Subsequent calls to get parking positions could probably reuse a lot of the positions that were computed in previous frames, given that the target point only moves by a little bit each frame. I figured that might work, but it requires more housekeeping and data-dependencies – the result of the previous query needs to be used for the next. That seemed complex and therefore brittle.

A Solution?

Temporal coherency was a pretty good idea though, but not the scale was to big this time. What if I exploited it within a single frame? Now the original code did obscure this, but maybe it gets a little more clear if I write it like this:

optional<v2> GameWorld::FindFreePosition(v2 Center, std::vector<v2> const& Occupied)
{
  auto CheckPosition = [&](v2 Candiate)
  {
    if (!IsPassable(Candidate))
      return false;

    if (OverlapsWith(Occupied))
      return false;

    return !FriendlyUnitOccupies(Candidate);  
  };
  auto Samples = SampledPositions(Center, SomeRandomness());
  auto Found = find_if(SampledPositions.begin(), SampledPositions.end(), CheckPosition(Position));
  
  return (Found != SampledPositions.end()) ? *Found : none;
}

Now as I explained in the previous post, this was called in a loop for each unit to be parked.

std::vector<v2> GameWorld::FindParkingPositions(v2 Center, std::size_t N)
{
  std::vector<v2> Results;
  for (std::size_t i = 0; i < N; ++i)
  {
    auto MaybePosition = FindFreePosition(Center, Results);
    if (!MaybePosition) // No more free space?
      break;
    Results.push_back(*MaybePosition);
  }
  return Results;
}

Easy to see: counting the number of CheckPosition calls, this algorithm is O(n) in number of sampled positions. The number of sampled positions depends linearly on the number of units to be parked, because more units obviously need more parking positions, essentially making this O(n²) for the unit count! But the positions get resampled for each unit – with the only change being the little bit of randomness that is injected everytime. In other words, each call would just test false for sampled positions roughly corresponding to the units that are already placed.

So what I did was a very small change: only inject the randomness once and merge the loops:

auto Samples = SampledPositions(Center, SomeRandomness());
std::vector<v2> Results;

for (auto const& Sample : Samples)
{
  if (CheckPosition(Sample))
    Results.push_back(Sample);

  if (Result.size() >= N)
    break;
}
return Results;

And this did the trick! The algorithm’s run-time when below the 1ms range, and the smaller variation in randomness is not really visible.

Conslusions

I was thrown off-track be the false conclusion that CheckPositions was too slow when it was in fact just called too often. Context is key! Always approach these things outside-in.
Using less-than-optimal abstractions obscured the opportunity to hoist out the sample generation from me. Iteration is always a separate concern, even when it is not on containers!

A Game Optimization War Story

As our customers surely know, I’m not working here on fridays. This is because that’s the time I allocate to my side project, an arcade real-time strategy game called abstractanks. It is a passion project above all else, but of course, I am also learning a lot, much of which I can apply to my “day job” here as well. Today I want to share the story of how I optimized a critical bit of code in that game.

The Big Slowdown

While working on scripted missions, one main element I am using is to make a group of units attack when you enter an area (a.k.a. a zone-trigger). This seems easy enough, but was causing massive slowdowns as soon as the enemy group started moving. My average logic frame-time jumped from 0.3 ms to more than 1500 ms, which essentially makes the game unplayable. When seeing a performance problem, your first instinct should always be to profile it. So I booted up WPR/WPA and did just that. Once I had the profile, I followed the most-sampled path in the stack and found my way to the supposed culprit: the parking algorithm.

Context

When optimizing, you need as much context as you possible to find the best possible course of action. So let me explain how that algorithm fits into the broader picture.

Parking

My main game-mechanic is moving around your units. You do this by selecting a group and then clicking somewhere on the map to issue the move-order. In addition to path-finding process, this also runs an algorithm I call park-planning (as in parking a car). It makes sure that the units know to position themselves around the target point in a roughly circular shape once they arrive. It is essential to the interaction of this mechanic with the capturing of objectives, which are circular as well. Before this was implemented, the units would just decelerate after passing the target point. This caused them to “overshot” and miss the objectives, which was frustrating to the players: they clicked in the right place, but the units would not stop there, but slightly behind it. To make things worse, units arriving later, would bump into those that were already there, further pushing them away and clumping up.

AI Moving

In my particular case, the AI enemy was repeatedly issuing move-orders to close in on the intruder – the player. Since the player group usually also moved, the AI was trying to adapt by changing the move order every frame (effectively working at around 2000 APMs).

Diving into the code

My park-planning implementation is divided into two steps: finding enough parking spots, and then assigning units to it. The profiler was showing that the first part was the problem while the assignment was negligible in terms of run-time. Historically, the first step was reusing and extending some code I first wrote for spawning units, which worked like this:

optional<v2> GameWorld::FindFreePosition(v2 Center, std::vector<v2> const& Occupied)
{
  auto CheckPosition = [&](v2 Candiate)
  {
    if (!IsPassable(Candidate))
      return false;

    if (OverlapsWith(Occupied))
      return false;

    return !FriendlyUnitOccupies(Candidate);   
  };

  if (CheckPosition(Center))
    return Center;

  auto Radius = UNIT_SIZE;
  while (Radius < MAX_SEARCH_RADIUS)
  {
    // Roll a random starting angle
    auto AngleOffset = RandomAngle();
    auto Angle = 0.f;
    while (Angle < 2*Pi)
    {
      auto Candidate = Center + AngleVector(Angle + AngleOffset)*Radius;
      if (CheckPosition(Candidate))
        return Candidate;

      // Move along this circle
      Angle += 2*Pi*Radius / UNIT_SIZE / OVERSAMPLING_FACTOR;
    }

    // Increase the Radius
    Radius += UNIT_SIZE;
  }
  return none;  
}

Note that all the functions in the CheckPosition lambda are “size aware” and respect the UNIT_SIZE – so they are slightly more complex than what the pseudo-code here would have you believe.
The occupied parameter was added for the parking-position finding. It successively fills up the std::vector with positions and uses them once it found enough.

Back to the profiling results: They were showing that most of the time was spent in the FriendlyUnitOccupies, followed by IsPassable and and then OverlapsWith. FriendlyUnitOccupies dominated the time by about 8x times the rest. That function uses a quad-tree to accelerate spatial queries for other units.

Next steps

Obviously, this code uses pretty simplistic approach to the problem – basically just brute-forcing it. But that’s good now there are many different paths to take, many optimization opportunities. My approach was a relatively simple change that got the frame time back down below 1 ms, but before I did that, I considered many and tested a few other different approaches. I will talk about that in detail in my next post. How would you approach this?