mratsim / Arraymancer
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
Why this job: matched by what the repo talks about: gpu, device, cuda, cpu
What it does
- Basic math operations generalized to tensors (sin, cos, ...)
- Matrix algebra primitives. Matrix-Matrix, Matrix-Vector multiplication.
- Easy and efficient slicing including with ranges and steps.
- No need to worry about "vectorized" operations.
- Broadcasting support. Unlike Numpy it is explicit, you just need to use +. instead of +.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 11 days ago
- Stars
- 1,417
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.7.0
- Released 5 years ago
- Built with
- Nim
- 98% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 56% closed
- 159 still open
- Pull requests
- 303 merged
- 7 waiting
- Releases
- 10
- 0 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Nim 98%
- Python 2%
- Julia <1%
- Ruby <1%
Topics
- tensor
- nim
- multidimensional-arrays
- cuda
- deep-learning
- machine-learning
- cudnn
- high-performance-computing
- gpu-computing
- matrix-library
- neural-networks
- parallel-computing
Project record
- JobLocal LLMs
- StatusListed
- Commits1,009
- Forks102
- Watching35
- Stars in 28 days+4
- CreatedApr 13, 2017
- First seen by usNov 7, 2026
- CheckedNov 8, 2026