StatsAPI.jl
A statistics-focused namespace for packages to share functions (by JuliaStats)
KernelAbstractions.jl
Heterogeneous programming in Julia (by JuliaGPU)
| StatsAPI.jl | KernelAbstractions.jl | |
|---|---|---|
| 1 | 4 | |
| 17 | 334 | |
| - | 2.4% | |
| 2.6 | 7.9 | |
| 5 months ago | 2 days ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
StatsAPI.jl
Posts with mentions or reviews of StatsAPI.jl.
We have used some of these posts to build our list of alternatives
and similar projects.
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Question: How to make method globally accessible (for interface/protocols/polymorphic types) and avoid "per-module" extension?
This is the usual way of doing things in Julia, and is why there are packages like StatsAPI.jl.
KernelAbstractions.jl
Posts with mentions or reviews of KernelAbstractions.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-12.
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Why is AMD leaving ML to nVidia?
For myself, I use Julia to write my own software (that is run on AMD supercomputer) on Fedora system, using 6800XT. For my experience, everything worked nicely. To install you need to install rocm-opencl package with dnf, AMD Julia package (AMDGPU.jl), add yourself to video group and you are good to go. Also, Julia's KernelAbstractions.jl is a good to have, when writing portable code.
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Generic GPU Kernels
>Higher level abstractions
like these?
https://github.com/JuliaGPU/KernelAbstractions.jl
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Cuda.jl v3.3: union types, debug info, graph APIs
For kernel programming, https://github.com/JuliaGPU/KernelAbstractions.jl (shortened to KA) is what the JuliaGPU team has been developing as a unified programming interface for GPUs of any flavor. It's not significantly different from the (basically identical) interfaces exposed by CUDA.jl and AMDGPU.jl, so it's easy to transition to. I think the event system in KA is also far superior to CUDA's native synchronization system, since it allows one to easily express graphs of dependencies between kernels and data transfers.