Saturday, 11 February 2012

R & D - It keeps you honest!

I've recently been working in an R&D group, developing various features that are quite experimental and wouldn't normally be scheduled as part of a project. This is intended to be "practical" R&D - we aim to develop features that have a chance of making it into the game, as opposed to features that are blue sky and would never actually be shipped.

This kind of development approach subtly shifts your approach to developing features. Briefly, you become very focused on doing what is necessary to get the feature into the game. You focus on making sure the feature is on-budget. You make sure that you consult with affected parties. You work out the consequences of incorporating the system and seek to mitigate any problems. You suddenly find yourself becoming a salesman, trying to establish a coalition of support for the feature. Your feature really has to prove itself worthwhile so that management will entertain the perceived risk of incorporating it.

Strangely, you don't always find yourself doing these things during the development of "standard" features. Because they are planned as part of the development effort, their inclusion in the title is automatically assured - pending no major disasters. You don't have to sell them. These features don't need to earn their place. Consequently, it can be very easy to find yourself "going down a rabbit hole" with features like this - exploring functionality that is interesting, but not necessarily focused on shipping. Perhaps these features are ultimately more risk-intensive, as they won't be quite so critically examined as the R&D features.

I tend to find that the whole "posture" you adopt during the development of an R&D feature that is not automatically included in the game is ultimately much better from the perspective of careful software development. It does at least feel more like the kind of process intended by the creators of various software development methodologies. It feels very much along the lines of the approach advocated by Michael Saladino in his "Why Red Lining Your Team Leads to Ineffectiveness".

Perhaps it's not for everyone. Perhaps it's not for every team. Perhaps some may find it quite a pressured, stressful kind of approach. But I really quite like it. It certainly feels a much more considered, deliberate approach. It seems that developing features in branches, that have to prove their worth for integration, sets in motion a slightly changed, more focused set of processes than may be typical.

Thursday, 9 February 2012

Sparse Voxel Octrees in Haskell

It's been a long time coming (daughters birth impedes Haskell coding!), but I've finally implemented and uploaded support for Sparse Voxel Octrees in my Haskell Renderer.
It's quite an interesting algorithm to implement in something like Haskell. It's interesting because it really questions the balance of strict vs lazy evaluation in Haskell. Which is better - strict construction and strict traversal, with the platform optimisations that may bring, or lazy construction-as-you-traverse?
In an imperative language, you'd run a complete process to construct the tree, and then traverse the completed tree for each intersection query. You have to pay the total construction cost plus the traversal cost. Since you'd be constructing and storing the whole tree, you need to invest significant effort in data compression. On the other hand, the direct data representation would permit you to make a number of optimisations aimed at evaluation efficiency.
With Haskell, the lazy evaluation thunks can do some of that work for you. Instead of trying to crunch the whole tree down to be as small as possible, you may choose instead to rely on the Haskell runtime to just-in-time construct the relevant portions of it. You would only pay the cost of construction for the parts of the tree you traverse. Would this sum be lower than a strict construction and traversal?

Alternatively, In Haskell, you can still go for the strict option and implement it much as you would with C.

What's interesting is that it introduces this challenging, general question of what offers the most efficiency: A domain-specific, efficiently implemented clever algorithm, or a somewhat more generic algorithm, less specifically optimised but instead relying on lazy evaluation and the higher-order optimisations that may automatically permit?

I'm not sure what the answer is to this question right now. I suspect there is a cross over point that is very specific to the data set in question and therefore your particular application.


My current Haskell SVO code is on github, in my Haskell Renderer:

I'm going to fix up the various remaining TODOs and then pursue a more optimised, C-like version along the lines of the various SIMD-like SVO implementations that have recently emerged.
Implementation Notes

This was ultimately quite an elegant algorithm to implement in Haskell. Much of the algorithm is built off simple geometric tests. These are quite compact and elegant to represent in a functional language like Haskell. For instance, a ray-box test is simply:

boundingBoxIntersectRay :: AABB -> Ray -> Maybe (Double, Double)
boundingBoxIntersectRay (bounds0, bounds1) (Ray rayOrg _ invRayDir rayLen)
     | tmax > tmin = Nothing
     | tmin > 0 && tmin < rayLen = Just (tmin, tmax `Prelude.min` rayLen)
     | tmax > 0 && tmax < rayLen = Just (tmax, tmax)
     | otherwise = Nothing
     (tmin, tmax) = foldr1 (\(a0, b0) (a1, b1) -> (a0 `Prelude.max` a1, b0 `Prelude.min` b1)) (map intercepts [vecX, vecY, vecZ])

intercepts f = let x0 = (f bounds0 - f rayOrg) * f invRayDir
                   x1 = (f bounds1 - f rayOrg) * f invRayDir
               in (x0 `Prelude.min` x1, x0 `Prelude.max` x1)

Here we simply define an operation to calculate the slab intercepts of the box, and then repeat that operation over X, Y and Z, then fold to reduce the results. We're offering a description of the algorithm to Haskell in high level primitives, permitting optimisation and reduction - as opposed to clumsily trying to indirectly tell a C++ compiler what you'd really like it to do. It's surprising how often this sort of thing proves faster in Haskell. For example, using the "any" or "all" functions has proven faster than a set of || or && clauses for me in the past. I could really imagine that a thunk-less version of Haskell, free of the irritation of considering C++ aliasing might produce some very efficient code.

The traversal code is a simple recursive traversal, with few (as yet) clever tricks. It terminates either at the leaves, at the maximum traversal depth or when the feature becomes too small when projected to screen space (though this calculation needs further elaboration). It therefore gives a simple, automatic LOD control.
And so, finally, here it is, our SVO lego-sphere:

Thursday, 2 February 2012

Peak Abstraction

An interesting phenomena I've observed over my years as a programmer is the occurrence of "Peak Abstraction" within a young programmer - and their subsequent improvement.

There is a common pattern. It typically occurs after 3-4 years of professional practice. A programmer is growing in his confidence. He begins to tackle more complex, challenging problems. And, he reflects those problems with complex, challenging data structures. He has not yet learnt the fine art of simplicity.

Every member of every data structure is a pointer or a reference. There are no simple data types. Everything becomes a "value dictionary", a "graph-based visitor" or a "multi-dimensional ring buffer". Disassembly of his code reveals it to be little more than an infinite sequence of lwz, lwz, lwz, lwz and more lwz. Perhaps he/she develops a hobbyist interest in Java. They may develop an editor that connects boxes with lines - such editors are great for filling with lots and lots of abstraction! Often, she/he may favour Boost; perhaps he even becomes your office's Boost Guy. Even simple plain old data types such as integers are suddenly held within a smart-pointer container and reference counted. And with a pimpl to retrieve their value.

Those around them become increasingly desperate. They can see the code becoming inflated, inefficient, unmaintainable. Spending time reading this individual's code is like spending time exposed to a radioactive isotope. You can only tolerate a few moments before exhibiting signs of sickness. You attempt to modify their code. 478 pages of template errors dissuade you from considering this foolish course of action again.

And then it happens. The programmer realises the error of their ways. They realise that they are adding neither design value nor computational value. They realise they are adding overhead and maintenance trouble. Perhaps they undergo their first encounter with profiling software. Said software may disclose the full terror of the D$ dirty bomb they have unleashed upon the code. Perhaps they are "introduced" to the profiler by an irate lead in some sort of shotgun-wedding affair. Whatever, the programmer realises that solving complex problems does not require needlessly complex data structures. Such structures impress nobody. People are impressed by simple, elegant solutions. And thus the recovery begins. Data structures become simple, expressive, and maintainable. Data cache misses dip under 20ms/frame for the first time. Co-workers enjoy modifying their code. The compiler begins to emit instructions such as "add" or "mul". Sadly the compiler may be too fond of outputting "jump" instructions at this stage... The programmer themself becomes wary of others who are pre-Peak Abstraction.

One even wonders if, like it's namesake "Peak Oil", there may be a global Peak Abstraction. Perhaps the practising body of programmers may all undergo Peak Abstraction in the next 5-10 years and we will all left never having to refactor some template monstrosity.

But then, where's the fun in that? Don't programmers just love to be indignant about others' code?