Numba VS PyPy

Compare Numba VS PyPy and see what are their differences

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Numba

Numba gives you the power to speed up your applications with high performance functions written...

PyPy

PyPy is a fast, compliant alternative implementation of the Python language (2.7.1).

Landing page //
2019-09-05

Landing page //
2023-10-15

Numba

Website: numba.pydata.org
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PyPy

Website: pypy.org
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Numba features and specs

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 of Numba

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.

PyPy features and specs

Performance
PyPy is known for its superior execution speed and performance, often outperforming the standard CPython interpreter for many workloads thanks to its Just-in-Time (JIT) compilation strategy.
Compatibility
PyPy aims to be compatible with standard Python, so many programs and libraries that run on CPython should work on PyPy without or with minimal changes.
Memory Efficiency
Due to its garbage collection mechanism, PyPy often results in lower memory usage as compared to CPython, which can be beneficial for memory-intensive applications.
Concurrency
PyPy provides better support for concurrency, including potentially avoiding some of the Global Interpreter Lock (GIL) performance issues present in CPython.

Possible disadvantages of PyPy

Compatibility Limitations
Although PyPy aims to be compatible with Python, not all extensions and libraries available for CPython work flawlessly with PyPy, particularly those relying on C extensions.
Startup Time
PyPy has a slower startup time than CPython due to the JIT compilation overhead, which could be a downside for scripts primarily dealing with short-lived processes.
Larger Memory Footprint
While PyPy can be more memory efficient in the long term, the JIT compilation process can result in a larger initial memory footprint which could affect applications with limited memory resources.
Platform Support
PyPy might not support all platforms or the latest Python features immediately, potentially causing issues for users relying on cutting-edge Python developments or specific system architectures.

Analysis of Numba

Overall verdict

Numba is considered good, especially if your work involves numerical computations that can take advantage of its just-in-time compilation. Its ability to speed up Python code while allowing you to remain within the Python ecosystem makes it a valuable tool for performance optimization in computationally demanding applications.

Why this product is good

Numba is a just-in-time compiler for Python that is particularly effective for numerical and scientific computing. It translates Python functions to optimized machine code at runtime using the LLVM compiler infrastructure. This can significantly accelerate execution speed, especially for operations that involve loops and computationally intensive tasks. It's an attractive option for developers looking for performance optimization without having to write C or C++ code. Numba is also easy to integrate with other popular scientific computing libraries such as NumPy.

Recommended for

Data scientists and engineers working with large datasets.
Developers involved in scientific computing and numerical analysis.
Researchers needing to optimize algorithms for speed without leaving Python.
Educational purposes for those learning about compiling and performance acceleration.

Numba videos

+ Add

The Criminal History of RondoNumbaNine

PyPy videos

+ Add

PyPy - the hero we all deserve. - Amit Ripshtos - PyCon Israel 2019

Category Popularity

0-100% (relative to Numba and PyPy)

PyPy

Website Builder

74 74%

Website Builder

26% 26

Website Design

78 78%

Website Design

22% 22

Programming Language

72 72%

Programming Language

28% 28

CMS

71 71%

CMS

29% 29

User comments

Share your experience with using Numba and PyPy. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Numba seems to be a lot more popular than PyPy. While we know about 93 links to Numba, we've tracked only 8 mentions of PyPy. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Numba mentions (93)

I Use Nim Instead of Python for Data Processing
>Not type safe That's the point. Look up what duck typing means in Python. Your program is meant to throw exceptions if you pass in data that doesn't look and act how it needs to. This means that in Python you don't need to do defensive programming. It's not like in C where you spend many hundreds of lines safe-guarding buffer lengths, memory allocation, return codes, static type sizes, and so on. That means that... - Source: Hacker News / 9 months ago
Gravitational Collapse of Spongebob
I believe it is using Numba which converts to machine code. https://numba.pydata.org/. - Source: Hacker News / about 1 year ago
Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code. - Source: dev.to / over 1 year ago
Mojo: The usability of Python with the performance of C
Or you use numba [1]. Then you can use a subset of plain Python. [1] https://numba.pydata.org/. - Source: Hacker News / over 1 year ago
Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse. [0]: https://numba.pydata.org/. - Source: Hacker News / almost 2 years ago

PyPy mentions (8)

Pydrofoil: Accelerating Sail-based instruction set simulators
Gains than using either compiler alone. This uses the PyPy JIT framework to speed up a RISC-V simulator. https://pypy.org/ https://github.com/pydrofoil/pydrofoil Pydrofoil: A fast RISC-V emulator generated from the Sail model, using PyPy's JIT. - Source: Hacker News / about 1 month ago
One Billion Nested Loop Iterations
"On average, PyPy is 4.4 times faster than CPython 3.7." https://pypy.org/. - Source: Hacker News / 6 months ago
Ask HN: Are my HPC professors right? Is Python worthless compared to C?
If you're going the pure Python route, don't forget to try PyPy[1], an alternative JITed implementation of the language. A seriously underrated project, IMHO. Most time it speeds up execution by a factor of 2x-4x, but improvements of about two orders of magnitude are not unheard of. See for example [2]. Numeric, long-running code shoud suit PyPy optimizations well. [1] https://pypy.org/ [2]... - Source: Hacker News / 7 months ago
Yes, Ruby is fast, but…
Python: My Python-foo is limited, so I only ported the last problem (a simple while loop) and ran it with PyPy. It takes a bit less of time:. - Source: dev.to / about 1 year ago
Python 3.12: A Game-Changer in Performance and Efficiency
If you r looking for performance with almost fully supported C Extensions , pypy.org for you , 20x faster than cpython still. Source: about 2 years ago