tiny-cuda-nn VS RecursiveFactorization

Compare tiny-cuda-nn vs RecursiveFactorization and see what are their differences.

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tiny-cuda-nn RecursiveFactorization
9 3
3,567 -
2.6% -
5.4 -
15 days ago -
C++
GNU General Public License v3.0 or later -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

tiny-cuda-nn

Posts with mentions or reviews of tiny-cuda-nn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-11.

RecursiveFactorization

Posts with mentions or reviews of RecursiveFactorization. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-01.
  • Can Fortran survive another 15 years?
    7 projects | news.ycombinator.com | 1 May 2023
    What about the other benchmarks on the same site? https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Bio/BCR/ BCR takes about a hundred seconds and is pretty indicative of systems biological models, coming from 1122 ODEs with 24388 terms that describe a stiff chemical reaction network modeling the BCR signaling network from Barua et al. Or the discrete diffusion models https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Jumps/Dif... which are the justification behind the claims in https://www.biorxiv.org/content/10.1101/2022.07.30.502135v1 that the O(1) scaling methods scale better than O(log n) scaling for large enough models? I mean.

    > If you use special routines (BLAS/LAPACK, ...), use them everywhere as the respective community does.

    It tests with and with BLAS/LAPACK (which isn't always helpful, which of course you'd see from the benchmarks if you read them). One of the key differences of course though is that there are some pure Julia tools like https://github.com/JuliaLinearAlgebra/RecursiveFactorization... which outperform the respective OpenBLAS/MKL equivalent in many scenarios, and that's one noted factor for the performance boost (and is not trivial to wrap into the interface of the other solvers, so it's not done). There are other benchmarks showing that it's not apples to apples and is instead conservative in many cases, for example https://github.com/SciML/SciPyDiffEq.jl#measuring-overhead showing the SciPyDiffEq handling with the Julia JIT optimizations gives a lower overhead than direct SciPy+Numba, so we use the lower overhead numbers in https://docs.sciml.ai/SciMLBenchmarksOutput/stable/MultiLang....

    > you must compile/write whole programs in each of the respective languages to enable full compiler/interpreter optimizations

    You do realize that a .so has lower overhead to call from a JIT compiled language than from a static compiled language like C because you can optimize away some of the bindings at the runtime right? https://github.com/dyu/ffi-overhead is a measurement of that, and you see LuaJIT and Julia as faster than C and Fortran here. This shouldn't be surprising because it's pretty clear how that works?

    I mean yes, someone can always ask for more benchmarks, but now we have a site that's auto updating tons and tons of ODE benchmarks with ODE systems ranging from size 2 to the thousands, with as many things as we can wrap in as many scenarios as we can wrap. And we don't even "win" all of our benchmarks because unlike for you, these benchmarks aren't for winning but for tracking development (somehow for Hacker News folks they ignore the utility part and go straight to language wars...).

    If you have a concrete change you think can improve the benchmarks, then please share it at https://github.com/SciML/SciMLBenchmarks.jl. We'll be happy to make and maintain another.

  • Yann Lecun: ML would have advanced if other lang had been adopted versus Python
    9 projects | news.ycombinator.com | 22 Feb 2023
  • Small Neural networks in Julia 5x faster than PyTorch
    8 projects | news.ycombinator.com | 14 Apr 2022
    Ask them to download Julia and try it, and file an issue if it is not fast enough. We try to have the latest available.

    See for example: https://github.com/JuliaLinearAlgebra/RecursiveFactorization...

What are some alternatives?

When comparing tiny-cuda-nn and RecursiveFactorization you can also consider the following projects:

instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more

diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

blis - BLAS-like Library Instantiation Software Framework

LeNetTorch - PyTorch implementation of LeNet for fitting MNIST for benchmarking.

KiteSimulators.jl - Simulators for kite power systems

juliaup - Julia installer and version multiplexer

vectorflow

RecursiveFactorization.jl

StochasticAD.jl - Research package for automatic differentiation of programs containing discrete randomness.

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