Gamers aren’t primarily spending time or money for the art and neither was NVIDIA. I will grant that the hardware improvements did make the visual aspects more lifelike and detailed and that allowed for increased artistic range, but production costs generally increased accordingly.

Of course graphics hardware was also used for more creative purposes including desktop publishing, special effects for TV, and digital art, so you will find some people in those communities vaguely wishing for something better, but artistic creation, even for commercial purpose, was never the market driver of 3D acceleration. Games were. The hardware was designed for gamers first, game programmers second, game artists a distant third, and for nobody else.

The closest thing to an "art computer" around that time was the Amiga which targeted the design/audio/video production markets.

Early 90s: SGI invented OpenGL to make realtime 3D graphics practical, initially for CAD/CAM and other scientific/engineering pursuits, and started shipping expensive workstations with 3d accelerated graphics. Some game artists used these workstations to prerender 3d graphics for game consoles. Note that 2D CAD/CAM accelerators had already been in market for nearly a decade, as had game consoles with varying degrees of 2D acceleration.

Mid-90s: Arcades and consoles starting using SGI chips and/or chip designs to render 3d games in real time. 3DFx, founded by ex-SGI engineers, created the Voodoo accelerator to bring the technology down market to the PC for PC games, which was a rapidly growing market.

Late 90s: NVIDIA entered the already existing and growing market for OpenGL accelerators for 3D PC gaming. This was a fast-follow technical play. They competed with 3DFx on performance and won after 3DFx fell behind and made serious strategy mistakes.

Later 90s: NVIDIA created the “GPU” branding to draw attention to their addition of hardware texture and lighting support, which 3DFX didn’t have. Really this was more of an incremental improvement in gaming capability.

Early 00s: NVIDIA nearly lost their lead to ATI with the switch to the shader model and DirectX 9, and had to redesign their architecture. ATI is now part of AMD and continues to compete with NVIDIA.

Mid 00s: NVIDIA releases CUDA, which adapts shaders to general purpose computation, completing the circle in a sense and making NVIDIA GPUs more useful for scientific work like the original SGI workstations. This later enabled the crypto boom and now generative AI.

Of course, along the way, OpenGL and GPUs have been used a lot for art, including art in games, but at no point did anybody say "hey, a lot of artists are trying to make 3D art, we should make graphics hardware for artists". Graphics hardware was made to render games faster with higher fidelity.

Comments URL: https://news.ycombinator.com/item?id=29979849

Points: 4

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=29979845

Points: 4

# Comments: 0

1. Schemaless/document/schema-on-need databases like Fauna don't mandate the application breakage on every change that SQL does

2. It's hard to reason about if its not transparent, but it can be transparent now, see below

3. Fauna is a temporal database, which acts like version control on your stored procedures, so you can easily check and revert any change

4. Fauna is serverless and horizontally scalable without consistency/latency impact

5. This was definitely a problem when you were occupying precious CPU cores on a vertically scaled RDBMS with business logic, but compute in Fauna or in serverless lambdas scales horizontally indefinitely

Comments URL: https://news.ycombinator.com/item?id=29093896

Points: 73

# Comments: 42

What other methods does the community use for measuring distributed latency?

Comments URL: https://news.ycombinator.com/item?id=28594664

Points: 42

# Comments: 8

Comments URL: https://news.ycombinator.com/item?id=28384536

Points: 2

# Comments: 0

Map the index additions and their timestamps onto the documents themselves and you can retrieve the entire state of each record that the ip wrote at the time that it wrote it. If you want to know specifically what that ip changed, then diff it with the previous record, for example, to filter down to updates that only changed the email address.

The GemStone transaction docs describe a scheme that would work properly on a single machine, but don't discuss anything about distributed coordination across servers or failure modes. The installation instructions don't discuss setting up a cluster. The marketing docs discuss using thousands of VMs (clients) and scaling the dataset to "hundreds of gigabytes" based on disk storage instead of memory which is not what I would expect from a distributed system. Various benchmarks and user comments refer to using a single server for GemStone.

I will update the post to clarify that we are discussing distributed document databases only. It's easy to do anything you want on a single machine.

The point is, no distributed database with a naive two-phase lock is truly transactional.

These are exactly the kind of "transactions" you get when you try to implement everything at the application level rather than the database level. Couchbase transactions (in the article) are the same. And it's not that different from Vitess cross-shard transactions either, which are not isolated (https://vitess.io/docs/reference/features/two-phase-commit/). Tandem SQL used the same scheme as well I believe.

Prior to Spanner, there were no production databases that offered ACID transactions across distributed, disjoint shards.