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  5. Numba vs PyPy

Numba vs PyPy

OverviewComparisonAlternatives

Overview

PyPy
PyPy
Stacks15
Followers35
Votes0
Numba
Numba
Stacks20
Followers44
Votes0
GitHub Stars0
Forks0

Numba vs PyPy: What are the differences?

### Introduction
When comparing Numba and PyPy, it is essential to understand the key differences between the two in terms of performance optimization for Python code.

### 1. Just-In-Time Compilation:
Numba uses just-in-time compilation to translate Python functions to machine code at runtime, resulting in improved performance. On the other hand, PyPy utilizes a Just-In-Time compiler with a focus on optimizing Python interpreters, leading to enhanced execution speed of Python programs.

### 2. Supported Python Features:
Numba supports a wide range of Python features and libraries, making it versatile for various applications. In contrast, PyPy has broader support for the Python language itself, allowing for compatibility with different Python packages and modules.

### 3. Type Inference and Specialized Functions:
Numba specializes in type inference based on function inputs to generate efficient machine code, particularly beneficial for numerical computations. PyPy, on the other hand, focuses on providing a general-purpose optimization approach rather than specializing in certain types or functions.

### 4. Garbage Collection:
Numba does not include its garbage collection mechanism, relying on Python's default garbage collection implementation. PyPy, however, offers a more advanced garbage collector that can improve memory management efficiency for Python programs.

### 5. Community Support and Development:
Numba has a strong community of users and developers that actively contribute to its enhancement and provide support for users. PyPy also benefits from community-driven development but has a more extensive history and established user base compared to Numba.

### 6. Integration with Existing Codebases:
Numba seamlessly integrates with existing Python codebases, allowing for easy adoption and optimization of specific functions. PyPy, while offering compatibility with existing Python code, may require additional adjustments or considerations when integrating with complex systems.

In Summary, Numba excels in specialized function optimization and numerical computations, whereas PyPy focuses on broader Python interpreter optimization and compatibility with various Python packages.

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Detailed Comparison

PyPy
PyPy
Numba
Numba

It is a very compliant implementation of the Python language, featuring a JIT compiler. It runs code about 7 times faster than CPython.

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.

JIT compiler; real GC; low memory usage; easy interfacing with C
On-the-fly code generation; Native code generation for the CPU (default) and GPU hardware; Integration with the Python scientific software stack
Statistics
GitHub Stars
-
GitHub Stars
0
GitHub Forks
-
GitHub Forks
0
Stacks
15
Stacks
20
Followers
35
Followers
44
Votes
0
Votes
0
Integrations
IPython
IPython
Django
Django
Flask
Flask
PyCharm
PyCharm
C++
C++
TensorFlow
TensorFlow
Python
Python
GraphPipe
GraphPipe
Ludwig
Ludwig

What are some alternatives to PyPy, Numba?

JavaScript

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

Python

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.

PHP

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

Golang

Golang

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.

HTML5

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

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