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Julia

379
500
+ 1
117
Numba

10
32
+ 1
0
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Pros of Julia
Pros of Numba
  • 18
    Designed for parallelism and distributed computation
  • 17
    Fast Performance and Easy Experimentation
  • 14
    Free and Open Source
  • 13
    Multiple Dispatch
  • 12
    Calling C functions directly
  • 12
    Lisp-like Macros
  • 11
    Dynamic Type System
  • 8
    Powerful Shell-like Capabilities
  • 4
    REPL
  • 4
    Jupyter notebook integration
  • 2
    String handling
  • 2
    Emojis as variable names
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    Cons of Julia
    Cons of Numba
    • 5
      Immature library management system
    • 3
      Slow program start
    • 3
      Poor backwards compatibility
    • 2
      JIT compiler is very slow
    • 2
      Bad tooling
    • 2
      No static compilation
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      - No public GitHub repository available -

      What is Julia?

      Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

      What is Numba?

      It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.

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      What companies use Julia?
      What companies use Numba?
      See which teams inside your own company are using Julia or Numba.
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      What tools integrate with Julia?
      What tools integrate with Numba?

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      What are some alternatives to Julia and Numba?
      Python
      Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
      R Language
      R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
      MATLAB
      Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
      Rust
      Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
      Go
      Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.
      See all alternatives