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This page is from the beta release of the Data-Oriented Design book. There are errors, spelling and factual, and this page is only kept for purposes of maintaining old links.


The future of hardware is almost certainly increasingly parallel processing with all the related problems that entails; from power consumption, through deserialising our processing, to distribution of jobs across more than just a few local cores.

Supercomputers have been vector machines for decades, and any advances in graphics cards comes with an increase in the number of cores that run transforms. Mobile phones have 4 cores and even some microcontrollers have multi-core layouts17.1 Stream processing hardware is showing to be more capable of increasing throughput per generation than the general purpose computing architecture employed in the design of CPUs and micro-controllers. It's shaping up that the coming generations of hardware are going to be a battles of the parallel processing beasts17.2, larger core counts and more numerous pipes. Memory always seems to be at a premium, so again, we'll probably be trying to push next gen games out without having enough space in which to work. One area that has not been pushed around much has been offloading processing outside the local machine. Even though there has been plenty of work done with very high latency architectures such as Beowulf clusters or grid computing, the idea that they could be useful for games is still in its infancy due to the virtually real-time nature of processing for games. It could be a while off, but we may see it come one day, and it's better not to let it sneak up on us. Once we need it, we will need a language that handles offloading to external processing like any other threaded task. Erlang is one such language. Erlang is functional (with a tendency for immutable state which helps very much with concurrency) and is highly process oriented with a very simple migration path from single-machine multi-threaded to remote processing or grid compute style distributed processing and a large number of options for fault tolerance. Node-js offers some of the same parallelism and a much shorter learning time as it is based on Javascript. Functional languages would seem to dominate, but OpenCL isn't purely functional, and C++AMP only requires you consider how to amplify your code with parallelism, which might not be enough for how many cores we really end up. Whatever language we do end up using to leverage the power of parallel processing, once it's ubiquitous, we'd better be sure we're not still thinking serial.

But parallel processing isn't the only thing on the horizon. Along with many cores comes the beginnings of a new issue that we're only starting to see traces of outside supercomputing. The issue of power per watt. In mobile gaming, though we're striving to make a game that works, one thing that developers aren't regularly doing that will affect sales in the years to come, is keeping the power consumption down on purpose. The mobile device users will put up with us eking out the last of the performance for only so long. There is a bigger thing at stake than being the best graphical performance and fastest AI code, there is also the need to have our applications be small enough to be kept on the device, and also not eat up enough battery that the users drop the application like a hot potato. Data-oriented design can address this issue, but only if we add it to the list of considerations when developing a game. As with parallelism, the language we use impacts the possibilities. If we continue to move towards more high level languages such as C# and Java, then we will need smarter compilers to reduce the overhead, but if we develop in a language made to support tasking, such as the process kernels in OpenCL or the lightweight threads of Erlang, then we may find new hardware changes to match the language much the same way C++ and object-oriented design changed the way CPUs were designed.

Parallel processing

In short, parallel processing increases the number of things done at the same time. Towards an infinite number of CPUs the amount of processing available is infinite, but the amount of processing you can use is limited by two factors.

The first limiting factor is the amount of work that needs to be done. If you have a perfectly parallel algorithms, then you are limited by the number of units of work you have to do.

For example, if a graphics card had an infinite number of cores (and we'll keep using infinite as it is the only good measure for future values of N), then the maximum number of cores that it can use to render a single polygon would be measured in how many tasks there are to do. If the polygon was fully covering a standard 1080P HD screen with 1920 x 1080 pixels, then the highest feasible number of cores to throw at the task of pixel shading the poly to the back buffer would be 2,073,600, and thus the highest throughput you can possibly get from the machine would rely on how fast one single core could transform the request to render, into a final change of value at the pixel. This example is slightly broken because the time taken to set up that many cores in a normal rendering setup would probably cost more time than just splitting the task into larger chunks, and if you use MSAA you can call on even more cores, but eventually you'll even run out of samples to do. The point being, if there are not enough jobs, then the cores will be under utilised.

A lack of jobs is hard to find in a graphics card, which is part of the reason they have been growing in power so rapidly over the last few years (2008-2013), and show no signs of significantly slowing down in their GFLOPS growth. There are always more pixels to render, always more vertices to transform, always more textures to lookup. The bottleneck in a graphics card is still the number of cores, and that's why it's relatively easy to increase the power of them compared to general purpose chips like CPUs.

In addition to the amount of jobs the cores have to do, the relative similarity of the jobs makes it easier to move towards a job-board style approach to job dispatch. In every parallel architecture so far, the instructions to run per compute core are decided specifically per core. In the future, an implicit job system may make a difference by instigating the concept of a public read-only job spec, and having set up the compute cores to look out for jobs and apply their own variant information on them. A good example might be that a graphics card may set all the compute cores to run the same algorithm, but given some constants about where they should get their data such as offsets into the stream and different output rectangles of the display.

This second limiting factor is dependency. We touched on it with the argument for pixel shaders, the set up time is a dependency. The number of cores able to be utilised is serial up until the first step of set up is finished, that is, the call to render primitive. Once the render call is done, we can bifurcate our way to parallelism, but even then, we're wasting a lot of power because of dependence on previous steps. One way to avoid this in hardware is have many cores wait for tasks to do by assuming they will be in charge of some particular part of the transform. We see it in SIMD processing where the CPU issues one instruction to multiply a vector, and each sub core carries out its own multiply, knowing that even though it was told that a multiply for the whole vector was issued, it could be sure that it was the core involved in multiplying element n of that vector. This is very useful and possibly why future optimisations of hardware can work towards a runtime configurable hardware layout that runs specified computations on demand, but without explicit instruction. Instead, as soon as any data enters into the transformation arena, it begins the configured task without asking for, or needing information on how to interpret the data. This type of stream processing may be best suited to runtime configurable hardware.

Ahmdal's Law is based on two constants, the time that a process must be serial and the time that a process can be parallel. In the world of infinite core computing we must continually strive for the lowest possible serial latency in our development. That means we must find out what is critical, what can be done without prior information, what can be done in preparation, what can be not done until the very last moment. All these elements of processing add up to a full product, and without considering what the output data is and how we get there, we could continually return to the state where we are optimising for code, and not for what is of ultimate importance, the experience due to realisation of output data in a timely fashion.

Distributed computing

When you think of distributed computing, normally you think of farms of computers spread over multiple locations, running long lifetime processes on massive amounts of data. You think of on demand load balancing systems providing more compute where necessary on the grid. All this talk of large scale is just another step out from our CPU cores. Another layer that we can move to when the possibility presents itself. One day, maybe, we will have invisible grid computing for the consumer. The idea of spreading the compute load out from the physical device was first presented commercially with the CellBE, the idea that adding more compute to an existing hardware instance could be done without explicitly linking hardware through a proprietary interface. There were articles talking of how your TV could increase the quality of your gaming experience by allowing the console to offload some of the processing onto the TVs processor. This has not come to pass, but the common core, the idea of doing more work by adding more machines, that has not gone away and is in common use in most development companies, whether by use of the free to use build accelerators such as the one included in the Sony developer tools, or proprietary software such as Xoreax's IncrediBuild. Adding more compute to processes that can safely be very high latency is definitely one way of using grid engines, but another alternative is to use grid engine's to provide answers to questions not yet asked. If you know that a type of question is common during your processing, then offloading the question process so it processes all known variants of the question means you can get back the answer to all the possible questions before it's even asked.

For example, If you know that your scene is going to animate, and you want to do a ray cast from some entities to other entities, but don't know which, you can task the grid engine with advancing the animation and doing all the ray casts. Then, once you are ready, read from the ray casts that you decided that you did need after whatever calculations you needed to do. That's a lot of wasted processing power, but it reduces latency. When you're thinking about the future, it is only sensible to think about latency as the future contains an infinite number of processing cores.

Using this one example, you can ramp it back to current generation hardware by offloading processes to being large scale ray casting, and during the main thread calculation stages, cull tasks from the grid engine like thread that is managing the ray casts. This means that you can get started on tasks, but only waste cycles when you don't know enough to not.

With very high speed network adapters, very high bandwidth network connections to the internet, grid computing inside games might become more commonplace than expected. What gameplay elements this amount of processing power can open up is beyond our vision, but once it is here, we can expect remote processing through something akin to stored procedures to become a staple part of the game developer tool-kit.

Runtime hardware configuration

We cannot know what the future brings, and some assumptions we have about CPUs might be broken. One such assumption might be that our hardware is fixed in one form once it has been fabricated. Both field programmable gate arrays and complex programmable logic arrays allow for change during their lifetime, and some announcements have been made about mainstream hardware adding FPGAs to their arsenal17.3which may open the door to FPGA add-on cards much the same way we saw the take-off of graphics cards once they got a foot in the door.

FPGAs present a new and interesting problem to programmers, specifically to the programmers that may be inventing new plans for others to consume. One way this could pan out is by re-orienting the mindset of the average programmer into that of a flow based or stream processing developer. Being able to take game data and manipulate it with highly efficient, and low latency modules dynamically loaded onto FPGAs could be the final nail in the coffin for any language that links code with data.

Approaches to development similar to that imposed on us by the shader model and the process of getting data into the right layout for the shaders might have been good training, readying us for the coming days of compute power only really being available if we order it for later. With dynamic partial reconfiguration, we would have the same benefits seen in being able to switch shaders mid render. That is, the ability to utilise an FPGA much smaller than would be necessary if we were to attempt to cram all the potential processing modules onto it at once. We've been here before. This extremely high latency before being able to compute, followed by very fast processing once the computational framework is ready, is very similar to programming with shaders, so much so that we might not take that long bringing our existing engines up to speed.

The CellBE was an attempt at this way of working, but was overlooked by many as just a strange piece of hardware. The core, an underpowered CPU that would look after feeding all the high power SPUs was assumed to be a general purpose CPU. This was an unfortunate side effect of too many years developing for out-of-order CPUs, and a deep investment in random memory access programming methods such as object-oriented design and interpreted languages. We don't know what other CPUs may come along in the future, but we can attempt to use a data-oriented approach and not try to make a CPU work the way we want to work, but work with it to make the best out of what we have.

Data centric development in hardware design

Once software solutions concentrate on transforming data, what changes can we expect from hardware vendors? Is that a silly question? Would a change of programming paradigm really affect the people who create hardware?

What should we promote and demote in order to get the most from our transistors?

move away from serial thinking more functional programming, where there are no side-effects, leads to solutions for infinite numbers of cores

Whatever the future brings with respect to hardware and processing configurations, there are certain assumptions we can make.

next up previous contents
Next: About this document ... Up: Data-Oriented Design Previous: Hardware   Contents Beta release of Data-Oriented Design :
Expect errors, spelling and factual. Expect out of date data, or missing stuff. Expect to be bored stiff in some sections, and rushed in others, but most of all, please send any feedback on any of these and any other things that you spot, to

Richard Fabian 2013-06-25