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26 October, 2016

Over-engineering (the root of all evil)


Over-engineering: using prematurely for tools, abstractions or technical solutions, resulting in wasted effort and unnecessary complexity.

When is a technique used prematurely? When it doesn't solve a concrete, current problem. It is tempting to define good engineering in terms of simplicity or ease of development, but I think it's actually a slippery slope. Simplicity means different things to different people. 

One could see programming as compression (and indeed it is), but we have to realize that compression, or terseness, per se, is not a goal. The shortest program possible is most often not the simplest for people to work with, and the dangers of compression are evident to anybody that had to go through a scientific education: when I was in university the exams that were by far the most difficult came with the smallest textbooks...

Simplicity also means different things to different people. To someone in charge of low-level optimizations working with fewer abstractions can be easier than having to dive through many software layers. To a novice, a software written in a more idiomatic way for a given language might be much easier than something adapted to be domain specific.

Problems, on the other hand, measurable, concrete issues, are a better framework. 

They are still a soft, context-dependent and team-dependent metric, but trying to identify problems, solutions, and their costs brings design decision from an aesthetic (and often egocentric) realm to a concrete one.
Note: This doesn't mean that good code is not or shouldn't be "beautiful" or elegant, but these are not goals, they are just byproducts of solving certain problems the code might have.
Also, "measurable" does not mean we need precise numbers attached to our evaluations, in practice, most things can't be better than fuzzy guesses, and that's perfectly fine.

Costs and benefits

Costs are seldom discussed. If a technique, an abstraction, an engineering solution doesn't come with drawbacks, it's probably because either it's not doing much, or because we've not been looking hard enough. 

  • Are we imposing a run-time cost? What about the debug build? Are we making it less usable?
  • Are we increasing build-times, lowering iteration times? 
  • A human one, in terms of complexity, obfuscation, ability to on-board new engineers?
  • Are we making the debugging experience worse? Profiling?
  • Do our tools support the design well? Are we messing up with our source control, static analyzer and so on?
  • Are we decreasing code malleability? Introducing more coupling, dependencies? 
  • Reducing the ability to reason locally about code? Making details that matter in our context hidden at call-site? Making semantics less explicit, or less coupled to a given syntax? Violating invariants and assumptions that our code-base generally employs?
  • Does it work well with the team culture? With code reviews or automated testing or any other engineering practice of the team?
We have to be aware of the trade-offs to discuss an investment. But our tendency to showcase the benefits our ideas and hide the costs is a real issue in education, in research, in production. It's hardwired in the way we work. It's not (most often) even a matter of malice, it's simply the way we are trained to reason, we seek success and shy away from discussing failure.

I've seen countless time people going on stage, or writing articles and book, honestly trying to describe why given ideas are smart and can work while totally forgetting the pain they experience every day due to them.


And that's why over-engineering truly is the root of all evil. Because it's vicious, it's insidious, and we're not trained at all to recognize it. 

It is possible to go out and buy technical books, maybe go to a university, and learn tens or hundreds of engineering techniques and best practices. On the other hand, there is almost nothing, other than experience and practice, that teaches restraint and actual problem solving.

We know what under-engineering is, we can recognize duplicated code, brittle, and unsafe code, badly structured code. We have terminology, we have methodologies. Testing, refactoring, coverage analysis...

In most of the cases, on the other hand, we are not trained to understand over-engineering at all.

Note: In fact over-engineering it's often more "pronounced" in good junior candidates, whose curiosity lead them to learn lots of programming techniques, but that have no experience in their pitfalls and can easily stray from concrete problem solving.

This means most of the times when over-engineering happens it tends to persist, we don't go back from big architectures and big abstractions to simpler systems, we tend to build on top of them. Somewhere along the road, we made a bad investment, with the wrong tradeoffs, but now we're committed to it.

Over-engineering tends to look much more reasonable, more innocent than under-engineering. It's not bad code. It's not ugly code. It's just premature and useless, we don't need it, we're paying a high price for it, but we like it. And we like technology, we like reading about it, keeping ourselves up-to-date, adopting the latest techniques and developments. And at a given point we might even start thinking that we did make the right investment, that the benefits are worth it, especially as we seldom have objective measures or our work and we can always find a rationalization of almost any choice.

I'd say that under-engineering leads to evident technical debt, while over-engineering creates hidden technical debt, which is much more dangerous. 

The key question is "why?". If the answer comes back to a concrete problem with a positive ROI, then you're probably doing it right. If it's some vague other quality like "sharing", "elegance", "simplicity", then it's probably wrong, as these are not end goals.

When in doubt, I find it's better to err on the side of under-engineering, as it tends to be more productive than the opposite, even if it is more reviled.

"Premature optimization is the root of all evil" - Hoare, popularized by Knuth.

I think over-engineering is a super-set of premature optimization. In the seventies, when this quote originated, that was the most common form of this more "fundamental" evil.
Ironically, this lesson has been in the decades so effective that nowadays it actually helps over-engineering, as most engineers read it incorrectly, thinking that in general performance is not a concern early on in a project.

Intermission: some examples

- Let's say we're working on a Windows game made in Visual Studio. Let's say that you are using a Visual Studio solution and it's done badly, it uses absolute paths and requires the source-code and maybe some libraries to be in a specific directory tree on the hard drive. Anybody can tell that's a bad design, and the author might be scorned for such an "unprofessional" choice, but in practice, the problems that it could cause are minimal and can be trivially fixed by any programmer.

On the other hand, let's say we started using, for no good reason, a more complex build system, maybe packages and dependencies based on a fancy new external build tool of the week.

The potential cost of such a choice is huge because chances are that now many of your programmers aren't very familiar with this system, it's bringing no measurable benefits but now you've obfuscated an important part of your pipeline. Yet, it's very unlikely that such decision will be derided.

- Sometimes issues are even subtler, because they involve non-obvious trade-offs. A fairly hard-coded system might be painful in terms of malleability, maybe doing changes in this subsystem requires every time editing lots of source files even for trivial operations.

We really don't like that, so we replace this system with a more generic, data-driven one which allows to do everything live, doesn't even require to recompile code anymore. But say that such system was fairly "cold", and the changes were actually infrequent. Suppose also that the new system takes a fair amount more code and now our entire build is slower. We ended up optimizing a workflow that was infrequent but on the down side we slowed down the daily routine of all our programmers on the team...

- Let's say you use a class where you could have used a simple function. Maybe you integrate a library, where you could have written a hundred lines of code. You use a templated container library where you could have used a standard array or ad-hoc solutions. You were careless and now your system is becoming more and more coupled at build-time due to type dependencies. 

It's maybe a bit slower in runtime than it could be or it makes more dynamic allocations than it should, or it's slow in debug builds, and it makes your build time longer while being quite obscure when you actually have to step in this library code.

This is a very concrete example, happens often yet chances are that none of this will be recognized as a design problem, and we often see complex tools built on top over-engineered designs to "help" solving their issues. So now you might use "unity builds" and distributed builds to try to remedy the build time issues. You might start using complex memory allocators and memory debuggers to track down what's causing fragmentation and so on and so forth. 

Over-engineering invites more over-engineering. There is this idea that a complex system can be made simpler by building more on top of it, which is not very realistic.

Specialization and constraints

I don't have a universal methodology for evaluating return on investment, once the costs and benefits of a given choice are understood. And I think there isn't in general one because this metric is very context sensitive. What I like to invite engineers to do is to think about the problem, be acutely aware of it.

One of the principles I think is useful as a guidance is that we operate with a finite working set: we can't pay attention to many things at the same time, we have to find constraints that help us achieve our objectives. In other words, our objectives guide how we should specialize our project.

For example, in my job I often deal with numerical algorithms, visualization, and data exploration. I might code very similar things in very different environments and very different styles depending on the need. If I'm exploring an idea, I might use Mathematica or Processing. 

In these environments, I really know little about the details of memory allocations and the subtleties of code optimization. And I don't -want- to know. Even just being aware of them would be a distraction, as I would naturally gravitate towards coding efficient algorithms instead of just solving the problem at hand.

Often times my Mathematica code actually leaks memory. I couldn't care less when running an exploratory task overnight a workstation with 92 gb of ram. The environment completely shields me from these concerns and this is perfect, it allows me to focus on what matters, in that context. I write some very high-level code, and somehow magic happens.

Sometimes I have to then port these experiments to production C++ code. In that environment, my goals are completely different. Performance is so important to us that I don't want any magic, I want anything that is even remotely expensive to be evident in the location where it happens. If there was some magic that worked decently fast most of the times, you can be sure that the problems it creates would be lost until there are so many locations where that happens that the entire product falls apart.

I don't believe that you can create systems that are extremely wide, where you have both extremely high-level concerns and extremely low-level ones, jack-of-all-trades. Constraints and specialization are key to software engineering (and not only), they allow us to focus on what matters, keeping the important concerns in our working set and to perform local reasoning on code.

All levels

Another aspect of over-engineering is that it doesn't just affect minute code design decisions or even just coding. In general, we have a tendency to do things without proper awareness, I think, of what problems solve for us and what problem they create. Instead, we're often guided either by a certain aesthetic or certain ideals of what's good.

Code sharing for example and de-duplication. Standards and libraries. There are certain things that sometimes we consider intrinsically good, even when we have a history of failures from which we should learn. 

For engineering, sharing in particular is something that comes with an incredible cost but that is almost always considered a virtue per se, even by teams which have experience actually paying the price in terms of integration costs, of productivity costs, of code-bloat and so on, it came to be just considered "natural".

"Don't reinvent the wheel" is very true and sound. But "the wheel" to me means "cold", infrastructural code that is not subject to iteration, that doesn't need specialization for a given project. 

Thinking that sharing and standardization is always a win is like thinking that throwing more people at a problem is always a win, or that making some code multithreaded is always a win, regardless of how much synchronization it requires and how much harder it makes the development process...

In a videogame company, for example, it's certainly silly to have ten different math libraries for ten different projects. But it might very well not be silly to have ten different renderers. Or even twenty for what matters, rendering is part of the creative process, it's part of what we want to specialize, to craft to a given art-direction, given project scope and so on.


Context doesn't matter only on a technical level, but also, or perhaps even more, on a human level. Software engineering is a soft science!

I've been persuaded of this having worked for a few different projects in a few different companies. Sometimes you see a company using the same strategy for similar projects, only to achieve very different results. Some other times on the other hands, similar results are obtained by different products in different companies by employing radically different, almost opposite strategies. Why is that?

Because people matter more than technology. And this is perhaps the thing that we, as software engineers, are trained the least to recognize. People matter more than technology.

A team of veterans does not work the same as a team that has or needs a lot of turnover. In the game industry, in some teams innovation is spearheaded by engineers, in some others, it's pushed by artists or technical artists.

A given company might want to focus all their investment in a few, very high profile products, where innovation and quality matters a lot. Another might operate by producing more products and trying to see what works, and in that realm maybe keeping costs down matters more.

Even the mantras of sharing and avoiding duplication are not absolute. In some cases, duplication actually allows for better results, e.g. having a separate environment for experimentation than final production. In some cases sharing stifles creativity, and has upkeep costs that overall are higher than the benefits.

It's impossible to talk about engineering without knowing costs, benefits, and context. There is almost never a universally good solution. Problems are specific and local.

Engineering is about solving concrete problems in a specific context, not jumping carelessly on the latest bandwagon.

Our industry I feel, still has lots to learns.

21 October, 2016

Cinematography, finally!

I made some screen grabs from the recently released Red Dead Redemption 2 announcement trailer.

These were quite hastily and heavily processed (sorry) in an attempt to make the compression artifacts from the video show less.

Basically these are an average of 5-6 frames, a poor man's temporal anti-aliasing let's say (and the general blurring helps not to focus on the general balance of the image instead of being distracted by small details), and tons of film grain to hide blocking artifacts.

I am in love. Tasteful, amazing frames that don't look like something that was just arbitrarily color-graded to make it seem somewhat cool last minute like a bad Instagram filter.

Enjoy. Ok, let me spend a couple more words...

Some made this to be a technical comparison between titles. In a way it is, but it's not a comparison on resolution and framerate, or amount of leaves rendered, texture definition and so on.

In all these accounts I'm sure there are plenty of engines that do great, even better than RDR2, especially engines made to scale to PC.

Thing is, who cares? This shouldn't matter anymore. We're past the point where merely draw more pixels or more triangles is where technology is at. We're not writing tri-fillers, we're making images. Or at least, we should be.

Technology should be transparent. Great rendering should not show, great rendering happens when you can't point your finger and name a given feature, or effect implementation. We are means to and end, the end is what matters. And it is a matter of technology, or research and of optimization. But a different one.

It is absolutely easier to push a million blades of grass on screen than to make a boring, empty room that truly achieves a given visual goal.

It is absolutely easier to make a virtual texturing system that allows for almost one-to-one texel to pixel apparent resolution than to accurately measure and reproduce a complex material.

It is absolutely easier to make a hand optimized, cycle-tight uber post-effect pipeline than to write artists tools for actual rapid iteration and authoring of color variants in a scene.

I'm not claiming that this particular game does any of these things right. Maybe it's just great art direction. Maybe it's achieved by lots of artists iterating on the worst engine and tools possible. Or maybe it's years of R&D, it's very hard to tell, even more from a teaser-trailer (albeit it totally looks legit real-time on the platform). But that's the point, great rendering doesn't show, it just leaves great images.

And I hope that  more and more games (RDR is certainly not the only one) and art departments and engineers do realize that, the shift we're seeing. Rendering hasn't "peaked" for sure, but we need to shift our focus more and more towards the "high level".

We solve concrete problems, technology otherwise is useless. And, yes, that means that you can't do good tech without artists because you won't ever have great images without great art. Working in isolation is pointless.

And now... Enjoy!

The first Red Dead Redemption also holds up quite well all things considered... These are respectively from the introduction cinematic and one of the first missions:

And as "bonus images", some screens I've found from the recent Battlefield 1 and The Witcher 3.

01 October, 2016

More (silly) tone-mapping ideas

As a follow-up to my previous post I'll show here two more tone-mapping tricks. They are both "silly" in a way, they were done quickly and mostly while waiting for more important things to build, so they are far from "production quality".

They both are somewhat inspired by photography (but make no effort at all to simulate photographic effects, rather, they take inspiration from observing how certain things work), and I believe they could all be part (including the ideas of the previous post) of a single tone-mapping operator. 
All dynamic range compression algorithms have trade-offs and artifacts, so I believe it's not unwise to layer various methods each doing just a bit of compression, to achieve an overall larger reduction.

"Adaptive ND-Filter"

The idea of doing this test came from a discussion with my Italian friend Manuele Bonanno, as we were chatting about Bart's TM post (which I linked in the previous article).

In landscape photography is not uncommon to use graduated neutral density filters. A ND filter reduces all wavelengths of light equally, darkening the image without shifting colors, and can be used for particular effects like very long exposures or to be able to use wide lens apertures in daylight. 
A graduated ND filter does the same but using a gradient, and it's used typically to darken the sky (top half) relative to the other elements of the image.

A GND filter - from Tiffen Filters marketing material

The interesting thing to notice is that even if in photography these filters are made with a fixed gradient, in practice the effect is smooth enough that it can make a big difference on the final image without being noticeable. 
Our vision system is not good at detecting very low-frequency gradients in images, so after a given spatial frequency we can "cheat" liberally without being able to see artifacts.

How to apply this in computer graphics is rather obvious: let's just blur everything a lot and use that as a base for a localized tone-mapping operator (in the test below, I just scale the exposure by the blurred version of the image a bit, then apply global TM as usual).

Doing local tone-mapping with a gaussian blur average usually yields pretty strong haloing artifacts (and a lot of photographic HDR toning software actually is plagued by such halos), and my previous article tried to show an alternative that is both stupidly cheap and that can't result in haloing (as it's not using neighboring operators at all).

Without ND
With ND (save the images and A/B them by alternating the two)

The "insight" here is that you can also prevent haloing if you use gaussian blur, but you blur enough not to be at frequencies where haloing shows. And, of course, if you have a good quality bloom/veil effect, you can re-use the image pyramids from that almost directly. Just remember to apply a -neutral- (grayscale) ND!

The gaussian filter approximated with image pyramids

Used tastefully and in a temporally stable way I think it can work, and indeed my colleague Michal confirmed that he know of titles doing something similar. 
I did a quick test using COD:AW and the effect indeed works really well, actually even better indoors than outdoors often, reducing the amount of "auto-exposure" needed (which we don't push very far typically anyways).

Film Grain

What? Film grain is not about dynamic range, right? It's usually an artistic effect, at best it can be seen as a dithering method that can help avoiding Mach band artifacts. Right?

A detail from a film photo by Trent Parke

Well, not really. If you look at an actual photo you'll notice that grains mostly show in midtones, or rather, that pure whites and pure blacks either saturate grains or have no grains exposed, and thus appear as solid regions.

Thresholded version

This means that if we look at a thresholded version of a good film scan we can see a spatial dithering in the threshold. 
We have some black areas that have black grains but are not fully saturated with black, and then we have "blacker than black" areas where the same black grains appear spatially clustered to create an entirely saturated region (and the same happens in the highlights as well). 

This suggests that we can use dithering patterns not only to get more precision in the midtones, but also to extend a bit the range of highlights and shadows. If a dither pattern is symmetric and equally distributed around zero (as it should be), sometimes it will add luminosity and sometimes it will subtract it. 
So if an area is white, but not whiter-than-white, the times the pattern subtracts it will create darker pixels. In areas that are constantly whiter than the intensity of the dither, even after subtraction, we will still end up with a full white, and we will see saturated areas. 

The image above is a quick test of the theory. On the left, it uses just the simple, old good Reinhard tone-mapping (with black and white levels used to get contrast). The image on the right uses the exact same tone-mapping but also adds som RGB film grain. 
If you look at the shadows and the highlights you can notice that there is a bit more detail visible in the film-grain version (e.g. count the number of visible beams in the glass ceiling).

This is just a quick test, I tweaked the film grain actually to be less intense in the midtones (around 0.5) than near the extremes (0 and 1) to be able to push it a bit further without being too intense over the entire image. It can certainly be done in much better ways, but as far as silly experiments go, I'm rather happy. 
If you really wanted to be cheating, you could even just add grain in the shadows, which would definitely be a hack as you're adding energy but hey... it's not that we don't routinely do that (e.g. bloom/veil usually doesn't redistribute energy, it just adds it...)

P.s. real film grain is actually not easy to emulate (and I'm not trying to, here). This is the only attempt I know of.