Well, do you have dedicated JSON hardware?
Please no, don’t subsidize anything Java-Script. It will only make it less efficient.
Render the json as polygons?
It’s time someone wrote a JSON shader.
That just results in an image of JSON Bourne.
Would you rather have 100,000 kg of tasty supreme pizza, or 200 kg of steaming manure?
Choose wisely.
Careful, the 100,000 kg of pizza will turn into manure.
I figure I can probably convert about 10 kg into manure before it autoconverts into compost. Which is maybe even a worse problem.
Maybe it’s time we invent JPUs (json processing units) to equalize the playing field.
The best I can do is an ML model running on an NPU that parses JSON in subtly wrong and impossible to debug ways
Until then, we have simdjson https://github.com/simdjson/simdjson
Let it be known that heat death is not the last event in the universe
Everybody gangsta still we invent hardware accelerated JSON parsing
https://ieeexplore.ieee.org/document/9912040 “Hardware Accelerator for JSON Parsing, Querying and Schema Validation” “we can parse and query JSON data at 106 Gbps”
I’m so impressed that this is a thing
106 Gbps
They get to this result on 0.6 MB of data (paper, page 5)
They even say:
Moreover, there is no need to evaluate our design with datasets larger than the ones we have used; we achieve steady state performance with our datasets
This requires an explanation. I do see the need - if you promise 100Gbps you need to process at least a few Tbs.
Imagine you have a car powered by a nuclear reactor with enough fuel to last 100 years and a stable output of energy. Then you put it on a 5 mile road that is comprised of the same 250 small segments in various configurations, but you know for a fact that starts and ends at the same elevation. You also know that this car gains exactly as much performance going downhill as it loses going uphill.
You set the car driving and determine that, it takes 15 minutes to travel 5 miles. You reconfigure the road, same rules, and do it again. Same result, 15 minutes. You do this again and again and again and always get 15 minutes.
Do you need to test the car on a 20 mile road of the same configuration to know that it goes 20mph?
JSON is a text-based, uncompressed format. It has very strict rules and a limited number of data types and structures. Further, it cannot contain computational logic on it’s own. The contents can interpreted after being read to extract logic, but the JSON itself cannot change it’s own computational complexity. As such, it’s simple to express every possible form and complexity a JSON object can take within just 0.6 MB of data. And once they know they can process that file in however-the-fuck-many microseconds, they can extrapolate to Gbps from there
Based on your analogue they drive the car for 7.5 inches (614.4 Kb by 63360 inches by 20 divided by 103179878.4 Kb) and promise based on that that car travels 20mph which might be true, yes, but the scale disproportion is too considerable to not require tests. This is not maths, this is a real physical device - how would it would behave on larger real data remains to be seen.
Except we know what the lifecycle of physical storage is, it’s rate of performance decay (virtually none for solid state until failure), and that the computers performing the operations have consistent performance for the same operations over time. And again, while for a car such a small amount can’t be reasonably extrapolated, for a computer processing an extremely simple format like JSON, when it is designed to handle FAR more difficult tasks on the GPU involving billions of floating point operations, it is absolutely, without a doubt enough.
You don’t have to believe me if you don’t want but I’m very confident in my understanding of JSON’s complexity relative to typical GPU workloads, computational analysis, computer hardware durability lifecycles, and software testing principles and best practices. 🤷
That’s why le mans exist, to show that 100m races with muscle cars are a farce
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The obvious solution is parsing jsons with gpus? maybe not…
Given it is a CPU is limiting the parsing of the file, I wonder how a GPU-based editor like Zed would handle it.
Been wanting to test out the editor ever since it was partially open sourced but I am too lazy to get around doing it
As far as my understanding goes, Zed uses the GPU only for rendering things on screen. And from what I’ve heard, most editors do that. I don’t understand why Zed uses that as a key marketing point
To appeal to people who don’t really understand how stuff works but think GPU is AI and fast
That’s not how this works, GPUs are fast because the kind of work they do is embarrassingly parallel and they have hundreds of cores. Loading a json file is not something that can be trivially parallelized. Also, zed use the gpu for rendering, not reading files.
Rockstar making GTA online be like: “Computer, here is a 512mb json file please download it from the server and then do nothing with it”
Someone just needs to make a GPU-accelerated JSON decoder
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Notepad
CPU vs GPU tasks I suppose.
GPU, render my 4.2 MB json file!
I’m afraid I can’t do that, Dave
there are simd accelerated json decoders
every day we stray further from god
Don’t worry, they still make extensive use of regexes.
I didn’t think any JSON parsers used regex given how simple the grammar is… but I’ve seen some horrors, so I shouldn’t rule it out.
I have the same problem with XML too. Notepad++ has a plugin that can format a 50MB XML file in a few seconds. But my current client won’t allow plugins installed. So I have to use VS Code, which chokes on anything bigger than what I could do myself manually if I was determined.
Time to train an LLM to format XML and hope for the best
Do we need a “don’t parse xml with LLM” copypasta?
L arge L regex M odel
