Table of contents
Numba
Numba gives you the power to speed up your applications with high performance functions written...
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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.
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.
We have collected here some useful links to help you find out if Numba is good.
Check the traffic stats of Numba on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Numba on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Numba's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Numba on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Numba on Reddit. This can help you find out how popualr the product is and what people think about it.
Also you can use projects like numba https://numba.pydata.org/. - Source: Hacker News / about 1 month ago
>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 / almost 2 years ago
I believe it is using Numba which converts to machine code. https://numba.pydata.org/. - Source: Hacker News / over 2 years ago
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 / almost 3 years ago
Or you use numba [1]. Then you can use a subset of plain Python. [1] https://numba.pydata.org/. - Source: Hacker News / almost 3 years ago
Simulations are, at least in my experience, numbaโs [0] wheelhouse. [0]: https://numba.pydata.org/. - Source: Hacker News / almost 3 years ago
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees. Source: about 3 years ago
A super-fast backtesting engine built in NumPy and accelerated with Numba. - Source: dev.to / about 3 years ago
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does... Source: about 3 years ago
Just for reference, * Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11." * Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles. * Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... Makes writing C... - Source: Hacker News / about 3 years ago
For the benefit of future readers: https://numba.pydata.org/. Source: about 3 years ago
You should profile your consumer to check the bottlenecks. You can use the excellent py-spy(written in Rust). IMO a few usage of Numba there and there should solve your performance issues. Source: about 3 years ago
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file. Source: about 3 years ago
There are also just-in-time compilers available for some Python features, that compile those parts to machine code. That includes Numba (usable as a library within CPython) and Pypy (an alternative Python implementation that includes a JIT compiler to improve performance). Thereโs also Cython, which is a superset of Python that allows more directly interfacing with C and C++ functions, and compiling the resulting... Source: over 3 years ago
This was a submission from u/bpecsek and shows that lisp with sbcl can do quite well on bench-marking. But keep in mind that these sort of benchmarks can't tell you much about real world applications. Moreover if you are really concerned about niche performance you need to start thinking about compilers. Heck with an appropriate compiler even python can go wrooom. Source: over 3 years ago
- if no purpose built libraries are faster, use numba (http://numba.pydata.org/) to speed up your code. Optionally you can also use Taichi (https://www.taichi-lang.org/) instead of numba. Source: over 3 years ago
The heavy part in backtest is calculations. And they are done in pandas which is partially written in c. Also, we can use numba: https://numba.pydata.org/. Source: over 3 years ago
The participants are also shown in the cover image, which are : Numba, JAX, Tensorflow, Triton. - Source: dev.to / over 3 years ago
Our project is dominated by I/O, and the bits that are CPU bound can be optimized using tools like numba (we do some serious math in parts of the code base). We actually did a performance comparison between Rust and Python for computation, and we ended up having comparable performance. Rust was 2-3x faster, but when we turned on parallelization, Python ended up being 2-3x faster than single-threaded Rust, and the... Source: over 3 years ago
You can also check out the Numba Python package that can sometimes help with some things: https://numba.pydata.org/. For example, I use it to implement a Numpy ufunc in the benchmark to generate a bounded random walk which would would be quite slow to do otherwise in pure Python for 10M rows:... Source: over 3 years ago
B. Just-in-time compilers can take advantage of specialized instructions on CPUs that static compilers traditionally used by C cannot and as such there are situations where python is more performant than C. Source: over 3 years ago
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