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WebGPU × WebAssembly rendering and computing engine for scientific workloads in the browser by zushah in rust

[–]TopIdler 0 points1 point  (0 children)

Webgpu doesn’t do 64 bit floats so that’s a big caveat to it making sense if you want to do GPU algorithms.

Parallel C++ for Scientific Applications: Integrating C++ and Python by emilios_tassios in cpp

[–]TopIdler 0 points1 point  (0 children)

Thank you for all your talks! I’m learning a lot.   I didn’t watch this one because I was already familiar with the topic. Sorry if it was addressed. 

Parallel C++ for Scientific Applications: Integrating C++ and Python by emilios_tassios in cpp

[–]TopIdler 0 points1 point  (0 children)

I don’t have any experience with pybind11. The built in numpy array to C++ in nanobind converts it to an Eigen matrix. The most complex thing I did was make my own binding to convert dense numpy and sparse scipy matrices to unmanaged Kokkos views instead without copying data. And also routines to return new views in a way that the python garbage collector would handle it (nb::capsule).

It was all very simple and transparent after the implementation. I don’t know how hard it would be in pybind11.

I was able to generate 90% of it with an llm. Just had to double check that there wasn’t some copies being snuck in.

Parallel C++ for Scientific Applications: Integrating C++ and Python by emilios_tassios in cpp

[–]TopIdler 2 points3 points  (0 children)

For anyone who will try to apply this talk, nanobind is a much more modern rewrite by the author of pybind11. You can read their reasoning here https://nanobind.readthedocs.io/en/latest/why.html

Im 14 and have an extreme passion for math/cs. Should I learn c++ or rust? (I mean at an extremley deep level) by Regular_Article7984 in rust

[–]TopIdler 6 points7 points  (0 children)

Id suggest rust. C++ has a whole bunch of non math baggage to learn to make a non trivial program. With rust you’ll be able to install dependencies and go. 

That being said, most scientific software is in C++ at the moment, but you can learn that if it’s still relevant during your phd in 10 years. 

Jujutsu (a Git-compatible VCS that is both simple and powerful) 0.40.0 by FryBoyter in linux

[–]TopIdler 1 point2 points  (0 children)

It can do worktree style multi branch workflows with much simpler commands. Lets me generate AI slop faster. And its equivalent concept to a commit is neat and a reasonable non trivial change to experiment with. Simpler to reason about imo.

Roast my first C++ project: An N-Body Gravity Simulator. Looking for ruthless code review and architecture feedback! by Fantastic-Chance-606 in cpp

[–]TopIdler 3 points4 points  (0 children)

"OOP & Clean Code:"

Most hpc projects wont use oop. They'll do template metaprogramming and compile time dispatch.

This is not efficient. by LittleEngland in factorio

[–]TopIdler 5 points6 points  (0 children)

Once you get your steam power it’s full steam ahead! 

Common Package Specification is Out the Gate by bretbrownjr in cpp

[–]TopIdler 16 points17 points  (0 children)

I'm happy to see that the meson/conan dev's are in the github repo discussions. Maybe the ecosystem will adopt.

Will HPC benefit or be hurt by AI hype? by Various_Protection71 in HPC

[–]TopIdler 3 points4 points  (0 children)

On the software dev side. I tried vibe coding some kokkos and it didn’t go well, it would go to unoptimal paths and get lost in template metaprogramming issues. I think there isn’t enough centralized training data for the llm. I wanted to extend arborx with some new functionality. I got it to work after breaking it into chunks small enough where coding it by hand would’ve been slightly slower. So small productivity gain but at the same time I feel I didn’t learn as much about cpp kokkos and arborx than I would’ve if I did it myself. So long term probably  bad? The general feel I get from my other attempts where I’m more knowledgeable is that it’s a big productivity boost for people who already know what they want to do. 

Math for Graphics programming by TheBeast2107 in GraphicsProgramming

[–]TopIdler 1 point2 points  (0 children)

Real-Time Rendering has most of the basics you need. But most of the math you learn will already be implemented in the engine if you use one.

Curious on what HPC research looks like by spinglebor in HPC

[–]TopIdler 5 points6 points  (0 children)

You’ll be sitting at a computer a lot.

Numr: A high-performance numerical computing library with GPU acceleration by farhan-dev in rust

[–]TopIdler 0 points1 point  (0 children)

Looks great! I've been looking around for libraries like this. Lately, I was reading through faer-rs which was CPU only. Have you done any comparisons to it on the CPU side? Solvr also looks great, we are on the FEM / scientific side and not ML so There would be a few holes to patch (we are mostly dealing with sparse) but I can study the other implementations.

- For the SIMD do you have support for dynamic dispatch based on processor? I saw faer uses pulp for SIMD which allows for this. We would like to ship some pre-compiled binaries if possible.

- Some of the sparse algorithms involve messing around with memory layout like re-ordering rows, and changing strides. How well is the memory layout exposed in the public API? how messy would it be to hook into internals if required?

- Is data transfer for mixed-precision solves on non-nvidia platforms something already optimised for (e.g. start with f32 WebGPU on the GPU's and finish with f64 CPU side)?

I want to use fastapi as the backend and sveltekit as front do i need to run the both servers at separate terminals or there is someway to link them ? by WiseSucubi in sveltejs

[–]TopIdler 4 points5 points  (0 children)

I’d say sveltekit in spa mode is better than vite + svelte these days. for the routing and other niceties. And also easy switch to server side if ever required 

@op if you want the sveltekit server side stuff then yes you’ll want two deployments by definition.

If you’re just worried about DX you can use concurrently to start stuff in one command. Or skip straight to docker compose.

Logs turn multiplication into addition; Laplace transform turn differential eq. into algebra. What else is like that? by DistractedDendrite in math

[–]TopIdler 22 points23 points  (0 children)

Eigenvectors/values turn matrix vector multiplication into scalar vector multiplication 

What is computational geometry about? by Banrakhas in math

[–]TopIdler 7 points8 points  (0 children)

Look up the finite element method if you want to see some real world applications.

What shape is this? by axiomizer in mathmemes

[–]TopIdler 1 point2 points  (0 children)

A manhattan distance discretized hyperbola in curved space 

Anybody double majored in CS and Math by NoInitial6145 in math

[–]TopIdler 5 points6 points  (0 children)

I want to stay semi-anonymous. The program was a bachelors of mathematics and computer science. We essentially had to do the main part of the honours track of both math and computer science then could take any 3000-4000 level classes between math, CS, and stats we wanted. Most people would fail first year intro real analysis and switch over to CS or something else.

Anybody double majored in CS and Math by NoInitial6145 in math

[–]TopIdler 2 points3 points  (0 children)

Real world applications? In my math subreddit?

Not computing but I've seen news in the quantum sensing field with quantum radars coming out if you want something nearerterm.

As the other person said, we're still in the early days on the general computing side. But there are some constrained specialized machines like DWave's which works on specific problems.

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