An introduction to Julia
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This document introduces Julia, an open-source programming language for technical computing with features like high performance, multiple dispatch, and ease of use. It has over 500 packages and 700 contributors. Julia bridges the gap between computer science and computational science by allowing for both high-level programming and high performance via just-in-time compilation. Its type system helps express scientific computations and improves performance. Other features include object-oriented programming with multiple dispatch, native parallelism, and domain-specific languages like JuMP for optimization. Julia has comparable or better performance than other numerical computing languages.
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An introduction to Julia
- 1. A introduction to Jiahao Chen MIT CSAIL Alan EdelmanJeff Bezanson Stefan Karpinski Viral B. Shah Andreas Noack Jake Bolewski
- 2. 720 contributors + package writers
- 3. https://github.com/jiahao/ijulia-notebooks The world of 557 packages, 720 authors 345 contributors to julia repo 5,239 stargazers 436 watchers
- 4. What’s the big deal about Julia ? A high level language with C-like speed julialang.org/benchmarks
- 5. What’s the big deal about Julia ? Normalized average lines of code (shortest = 0, longest = 1) Execution time (C = 1) https://groups.google.com/d/msg/julia-users/BYRAeQJuvTw/O7VK7-vp1EEJ fast and expressive
- 7. It bridges the divide between computer science and computational science What’s the big deal about Julia ? data abstraction performance What if you didn’t have to choose between data abstraction and performance?
- 8. Take home message Types helps users express scientific computations and helps the compiler specialize code for performance
- 9. methods objects Object-oriented programming with classes What can I do with/to a thing? top up pay fare lose buy classes are more fundamental than methods top up pay fare lose buy pay fare lose buy
- 10. OOP with classes multi-methods What can I do with/to a thing? top up pay fare lose buy generic function objectsmethods multimethods relationships between objects and functions
- 11. Multi-methods for linear algebra What can I do with/to a thing? compute spectral factorization compute singular values compute singular values and vectors compute eigenvalues generic function objectsmethods Methods can take advantage of special matrix structures eigvals eigfact svdvals svdfact Matrix SymTridiagonal Bidiagonal
- 14. easy to call external C functions, e.g. CLAPACK sstev, dstev…
- 15. So how does this help us with linear algebra? Multi-method dispatch with generic fallbacks Matrix operations on general rings textbook algorithm
- 16. So how does this help us with linear algebra? Multi-method dispatch with generic fallbacks Matrix operations on general rings
- 18. JuMP: a domain specific language Iain Dunning Miles Lubin MIT Operations Research
- 21. In summary, Types helps users express scientific computations and helps the compiler specialize code for performance Other advanced features for performance: code generation, native parallel computing, … try it today! juliabox.org JuliaCon - June 24-28 at MIT juliacon.org
- 24. Pure-‐Julia FFT performance2 4 8 16 32 64 128 256 512 1024 2048 4096 8192 16384 32768 65536 131072 2621440 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 speed(mflops) intel-mkl-dfti in-place intel-mkl-dfti out-of-place fftw3 out-of-place fftw3 in-place fftw3-no-simd out-of-place fftw3-no-simd in-place dfftpack emayer julia bloodworth cross cwplib esrfft double-precision complex, 1d transforms powers of two already comparable to FFTPACK [ actually was even a bit better; some recent inlining regressions in Julia snapshots ] Steven Johnson, 2014-06 Presented at NAIS Codegen workshop