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Category: Technical

Demoscene is not dead

Demoscene is not dead

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Andreas from Insomniac Games made a Amiga 500 demo in 2019 as part of this work with The Black Lotus demo group. He presented not only the Eon Amiga 500 demo, but tons of great technical information about the 4 years it took to develop it.

Old demo scene programmers hold amazing amounts of wisdom. When solving the core pieces of logic, I found this is true (but when doing larger, complete system development, these don’t work)

Work backwards from desired outcome to discover your constraints. Don’t just brute force. Instead, ask, what must be in place for us to get the peak performance from the key component we’re dependent on (render, disk load, etc). Then work from that constraint.

Do everything you can at compile time, not run time. Pre-compute tons of things – even the build-up of the data structures in memory. Just run it and then save and reload that blob automatically.

Over-generalizing early is a trap many devs fall into. Solve the problem in front of you. Trust that you can delete the code and do something else if it sucks. It’s cheaper and faster than trying to anticipate things ahead of time. Do the simplest thing that will work and if it sucks come back and delete it.

If you end up with a small runtime table/code that doesn’t require runtime checks because you can prove it can’t go wrong, you’re doing something right.

When developing, the actual Amiga is super slow and limited. They took an Amiga emulator and hacked it up so they could debug on it instead. Using calltraps to trigger the emulator, they added memory protection, fast forward, trigger debug, loading symbols, cycle accurate profiling, single step, high-resolution timers, etc. Also allows perfect input playback.

Modern threading and consumer/producer components (disk loading, data transfer, decompressors, etc) often just throw things in buffers and YOLO. There’s no clear backpressure to show you where you’re wasting time/space. Running on this kind of hardware/simulator shows you how much time the design is wasting by poorly and inefficiently designed algorithms/constraints.

Presented at Handmade Cities event in Seattle at: https://handmadecities.com/

Making a game in Assembly

Making a game in Assembly

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When teaching myself to program as a kid, my first language was type-in BASIC programs. After that, I made the very un-orthodox choice to learn assembly. I wrote a small database, a TSR (Terminate and stay resident program), and a couple other small creations.

Looks like GreatCorn did one better by writing his own game in x86 assembly.

RESound: Interactive Sound Rendering for Dynamic Virtual Environments

RESound: Interactive Sound Rendering for Dynamic Virtual Environments

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About 15 years ago, people noticed that rendering virtual scenes with ray tracing was a lot like how sound propagates through an environment. Light rays travel through open spaces, hit objects and then reflect, refract, and bend. Sound waves follow many of the same principles.

What if you use the same ray casting methods to simulate sound traveling through an environment? Instead of standard hacks on sound to make something sound like it’s in a tiled bathroom or a big orchestra hall, you could accurately simulate it – reducing artist time. Simply play the sound and let the algorithm figure out how it should sound.

Not sure what other research has happened since. It was too computationally expensive for real time back then, but it was a cool idea and maybe we have the compute for it with today’s GPU’s.

Paper: https://gamma.cs.unc.edu/Sound/RESound

Doordash Principal Engineer on Microservices

Doordash Principal Engineer on Microservices

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A reasonable good, simple discussion on the pros/cons of monoliths and microservices.

Some interesting comments:

  • Conway’s law – the structure of a system reflects the structure of the organization that makes it.
  • Microservices have their issues because they are a technical solution to an organizational problem – trying to solve when a team gets too big.

Here are the links referenced:

The gritty world of retro game analysis

The gritty world of retro game analysis

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The world has gotten very familiar to retro hardware re-creations, game emulation, re-releases, speed runs, creating new games for old platforms, as well as new exploits, tools, and discoveries. The nitty gritty work of doing all of this, however, is a labor of love. For those that dig into the binary, there’s tricky copyright concerns that need to be managed, only scraps of information about old hardware and software, highly optimized/tricky code that is tough to read, and almost no financial gain – except for commercial re-releases.

Made Up of Wires walks us through a live bit of decompiling of the PS1 classic: Castlevania: Symphony of the Night to give you a taste of the work involved in this kind of work. Not really that different than any other reverse engineering but surprisingly accessible as these old games were relatively small and simple.

Make your own video card!

Make your own video card!

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Ben Eater decided to build his own VGA video card. Well, technically it’s more of a display adapter/controller since the card doesn’t provide any rendering or accelerate the image buffer generation portion – but it’s still a pretty fun watch.

This is pretty much how computer graphics started. Someone built a display controller. Then others added some helper hardware to speed up the buffer fills, then blitting, then rendering, AI upscaling/noise reduction, and now full on AI rendering. What a wild technology ride – but it was this early stuff that really got me excited about technology. You could create and build all of this kind of amazing stuff yourself.

Programming for the Larrabee/Xeon Phi

Programming for the Larrabee/Xeon Phi

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Back in the day, I worked on this little project called Larrabee – which later turned into the Intel Xeon Phi coprocessor. It was an ambitious and exciting platform. It consisted of a ton of 512 bit wide instructions to operate like a lot of streaming GPU architectures, yet was fully general purpose x86.

It turned out that getting performance out of this hardware was difficult. In order to get the full potential of the hardware, you simply had to utilize the vector units. Without that, it is like writing a single threaded app on a 8 core system. Single SIMD lane operation just wasn’t going to cut it as was written about in 2017 International Journal of Parallel Programming article:

“Our results show that, although the Xeon Phi delivers a relatively good speedup in comparison with a shared-memory architecture in terms of scalability, the relatively low computing power of its computational units when specific vectorization and SIMD instructions are not fully exploited makes this first generation of Xeon Phi architectures not competitive”

Using the Xeon Phi Platform to Run Speculatively Parallelized Codes

The paper, and the host of others linked on the page as references, are a good read and gives some hints why fixed-function GPUs have an advantage when it comes to raw streaming throughput. Hint: cache and data flow behavior is as, if not more, important as utilizing vectorization in such architectures.