Numba VS Tiny C Compiler

• Performance
  Numba can significantly increase the speed of execution for numerically intensive Python code by compiling Python functions to optimized machine code using LLVM.
• Ease of Use
  Numba is user-friendly and requires minimal code changes. Often, just applying a decorator to functions is enough to gain performance benefits.
• Integration with NumPy
  Numba works well with NumPy, allowing users to compile functions that utilize NumPy arrays efficiently.
• JIT Compilation
  It supports Just-In-Time (JIT) compilation, enabling functions to be compiled at runtime, which allows for optimizations based on actual usage.
• GPGPU Acceleration
  Numba offers support for GPU acceleration, which can further enhance performance by offloading tasks to NVIDIA GPUs using CUDA.

Possible disadvantages

• Limited Python Feature Support
  Numba does not support all Python features and standard library modules, which can limit its applicability for certain functions or applications.
• Compilation Overhead
  The initial compilation of functions can add overhead, which might negate performance gains for small or simple tasks.
• Debugging Difficulty
  Debugging Numba-compiled code can be challenging due to the compiled nature of the code, which may obscure typical Python error messages.
• Complex Code Compatibility
  More complex Python constructs, such as classes and closures, are not fully supported, requiring workarounds or alternative solutions.
• Dependency on LLVM
  Numba heavily relies on the LLVM library for compilation, which can complicate installation and increase dependency size.

• Fast Compilation
  Tiny C Compiler (TCC) is known for its incredibly fast compilation speed, which makes it ideal for quick compilations and testing.
• Small Size
  TCC has a very small footprint compared to other compilers, making it easy to include in applications and use in environments with limited resources.
• C99 Support
  TCC provides support for the C99 standard, allowing the use of newer C language features.
• Dynamic Code Generation
  TCC can compile and execute code dynamically, which can be useful for scripting or embedded contexts.
• Simplified Licensing
  Under the GNU Lesser General Public License (LGPL), TCC can be more easily used in various projects, including proprietary ones, compared to compilers with more restrictive licenses.

Possible disadvantages

• Limited Optimization
  TCC does not perform extensive optimization, which can result in less efficient executable code compared to compilers like GCC or Clang.
• Incomplete C Standard Library
  TCC's standard C library implementation is not as complete as those of more established compilers, which might lead to compatibility issues.
• Lack of Detailed Documentation
  Users may find the available documentation lacking in detail, which can hinder learning and debugging for complex projects.
• Limited Platform Support
  TCC is primarily designed for smaller-scale applications and lacks some platform-specific and cross-compilation capabilities.
• Fewer Community Resources
  Compared to major compilers like GCC or Clang, TCC has a smaller user community, which can mean fewer tutorials, forums, and third-party support tools.

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• Python's experimental JIT to be removed

  You’re in luck: Numba exists [0]. It doesn’t work for everything, but it can be a massive speed-up in some areas. 0: https://numba.pydata.org/. - Source: Hacker News / 12 days ago

• Mojo 1.0 Is Here

  Julia is actually quite nice for this. If you prefer a python-like approach consider Triton from openai, numba (https://numba.pydata.org/) or CuTe DSL from Nvidia. - Source: Hacker News / 3 months ago

• Python JIT project was asked to pause development

  Also you can use projects like numba https://numba.pydata.org/. - Source: Hacker News / 5 months ago

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• What every compiler writer should know about programmers (Anton Ertl, 2015) [pdf]

  Some of those already exist, e.g. https://bellard.org/tcc/ However, they're not in widespread use. I would be curious to learn if there's any data/non-anecdotal information as to why. Is it momentum/inertia of GCC/LLVM/MSVC? Are... - Source: Hacker News / 9 months ago

• Git: Introduce Rust and announce that it will become mandatorty

  In theory you should be able to use TCC to build git currently [1] [2]. If you have a lightweight system or you're building something experimental, it's a lot easier to get TCC up and running over GCC. I note that it supports arm, arm64,... - Source: Hacker News / about 1 year ago

• Weird Lexical Syntax

  > I'm not sure who wants to be able to syntax highlight C at 35 MB per second, but I am now able to do so Fast, but tcc *compiles* C to binary code at 29 MB/s on a really old computer: https://bellard.org/tcc/#speed. - Source: Hacker News / about 2 years ago

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