https://github.com/quantumbfs/juliamatrix
A tutorial for matrix computation in Julia Language
https://github.com/quantumbfs/juliamatrix
Last synced: about 1 year ago
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A tutorial for matrix computation in Julia Language
- Host: GitHub
- URL: https://github.com/quantumbfs/juliamatrix
- Owner: QuantumBFS
- Created: 2018-07-27T16:32:41.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-08-12T10:35:31.000Z (almost 8 years ago)
- Last Synced: 2025-03-27T23:32:59.891Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 136 KB
- Stars: 4
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Tutorial for high performance matrix computations, in Julia
## Table of Contents
#### Performance Tips
* How to `@time` and `@benchmark` a piece of code
* Use `@simd`, `@inbounds` and `@inline` to speed up codes
* Use `StaticArrays.jl` for small matrices of fixed size to avoid allocation
* Improve type stability
#### Matrices
* Use `LinearMaps.jl` to view a linear function as a matrix
* fast `eigen solver` and `expmv`
* Sparse Matrices
* CSC Sparse Matrix
* General Permutation Matrix
* Identity Matrix
* Tensor Operations
## Preparations
* install latest [julia](https://julialang.org/)
* install required packages, open a julia REPL, type
* Pkg.add("BenchmarkTools")
* Pkg.add("TensorOperations")
* Pkg.add("Yao")
* Pkg.add("LinearMaps")
* Pkg.add("IJulia")
* Pkg.clone("https://github.com/GiggleLiu/Expokit.jl.git")
* type `jupyter notebook`, and open the notebook "notebooks/juliamatrix.ipynb"