#numba — Public Fediverse posts

The config args give a pretty good summary of how I built my coin toss app (see 2/2).

job_config (n, runs, Kb, Kbt, Lb, Kf, Kft, Lf, rK, nitro, turbo, summary, new_csv, cold_restart, multi_engine, cpu_sys, cpu_mp)

Almost done coding multi-processing.

A trillion coin tosses in under two hours on a 13-y-o laptop is about to become reality.

Hand-coded all the way.

Coin toss trends at scale look a lot like life.

1/2

#it #technology #science #math #linux #python #numba

The config args give a pretty good summary of how I built my coin toss app (see 2/2).

job_config (n, runs, Kb, Kbt, Lb, Kf, Kft, Lf, rK, nitro, turbo, summary, new_csv, cold_restart, multi_engine, cpu_sys, cpu_mp)

Almost done coding multi-processing.

A trillion coin tosses in under two hours on a 13-y-o laptop is about to become reality.

Hand-coded all the way.

Coin toss trends at scale look a lot like life.

1/2

#it #technology #science #math #linux #python #numba

Почему Python + Numba обгоняет C? Эксперимент с алгоритмом прогонки

Все знают, что C быстрее Python. Но мы провели эксперимент с алгоритмом прогонки и обнаружили, что Numba (JIT-компилятор для Python) обгоняет наивный C на 20–25%. Разбираемся, почему так происходит, и сравниваем точность float32/float64.

https://habr.com/ru/articles/1012122/

#Python #C #Numba #NumPy #производительность #алгоритмы #численные_методы #оптимизация #прогонка

https://blog.gslin.org/archives/2026/03/19/12941/%e5%be%88%e5%a4%9a-python-%e5%8a%a0%e9%80%9f%e7%9a%84%e6%96%b9%e6%b3%95/

很多 Python 加速的方法

#c #code #codon #compiler #cpython #cython #graalpy #jax #mojo #mypyc #native #numba #numpy #performance #pyo3 #pypy #python #rust #speed #taichi

https://blog.gslin.org/archives/2026/03/19/12941/%e5%be%88%e5%a4%9a-python-%e5%8a%a0%e9%80%9f%e7%9a%84%e6%96%b9%e6%b3%95/

很多 Python 加速的方法

#c #code #codon #compiler #cpython #cython #graalpy #jax #mojo #mypyc #native #numba #numpy #performance #pyo3 #pypy #python #rust #speed #taichi

I just released v1.1.0 of Complexitty, my little #Mandelbrot explorer for the #terminal. This release is all about an optional method of making it faster.

#python #programming #textual #numba

https://blog.davep.org/2026/02/28/complexitty-1-1-0.html

I just released v1.1.0 of Complexitty, my little #Mandelbrot explorer for the #terminal. This release is all about an optional method of making it faster.

#python #programming #textual #numba

https://blog.davep.org/2026/02/28/complexitty-1-1-0.html

結局Pythonは遅いのか？を“最小ベンチ”で決着：プロファイル→ベクトル化→Numba
https://qiita.com/Seine_A_Shintani/items/fef850878d8c89bad47a?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #Python #numpy #パフォーマンス #Numba #プロファイリング

Achieve incredible computing capabilities with our #GPU Programming workshops. We will teach you the basics knowledge of #Numba and #CuPy required to start a journey in the world of GPUs. Registration is open for GPU Programming workshop!
https://www.esciencecenter.nl/event/gpu-programming-6/

Achieve incredible computing capabilities with our #GPU Programming workshops. We will teach you the basics knowledge of #Numba and #CuPy required to start a journey in the world of GPUs. Registration is open for GPU Programming workshop!
https://www.esciencecenter.nl/event/gpu-programming-6/

Currently playing with adding optional #Numba support to Complexitty; my #Mandelbrot plotter for the #terminal. The speedup is okay.

Given this zoom and position, on my M2 Mac mini, 0.8 seconds. With Numba: 0.2 seconds.

I should give it a spin on my M2 Pro mini.

Without Numba on my 2019 Intel MacBook Pro the same spot takes about 2 seconds.

#Python #programming

Currently playing with adding optional #Numba support to Complexitty; my #Mandelbrot plotter for the #terminal. The speedup is okay.

Given this zoom and position, on my M2 Mac mini, 0.8 seconds. With Numba: 0.2 seconds.

I should give it a spin on my M2 Pro mini.

Without Numba on my 2019 Intel MacBook Pro the same spot takes about 2 seconds.

#Python #programming

A few years ago, the #ROCm backend for @numba was removed. #TIL that AMD is now actively developing a HIP backend!

https://github.com/ROCm/numba-hip

#python #numpy #numba #gpu #amd

Python: Schneller als man denkt

Beginn einer Artikelserie zum Thema schneller Python Code. Eine Einführung mit einer Auflistung verschiedener Strategien die verglichen werden.

#Python #Cython #Numpy #Numba #Linux

https://gnulinux.ch/python-schneller-als-man-denkt

Python: Schneller als man denkt

Beginn einer Artikelserie zum Thema schneller Python Code. Eine Einführung mit einer Auflistung verschiedener Strategien die verglichen werden.

#Python #Cython #Numpy #Numba #Linux

https://gnulinux.ch/python-schneller-als-man-denkt

Бэктестинг торговых стратегий на Python с помощью Numba. Когда перевод расчетов на GPU действительно оправдан?

Бэктестинг — ключевой процесс в алгоритмической торговле. Он позволяет проверить стратегию на исторических данных, прежде чем запускать её в реальной торговле. Однако, чем больше данных и сложнее логика стратегии, тем дольше времени занимают вычисления. Особенно если стратегия анализирует тиковые данные и требуется протестировать множество комбинаций гиперпараметров стратегии, время вычислений может расти экспоненциально. В этой статье мы разберем, как реализовать бэктестинг на чистом Python, посмотрим сколько времени могут занимать вычисления, а также попробуем найти разные способы оптимизации. Python, как известно — это интерпретируемый язык, что означает, что код выполняется построчно во время исполнения программы, а не компилируется в машинный код заранее, как это происходит, например, в C или C++. Это делает разработку быстрее и удобнее, так как можно сразу видеть результаты выполнения кода и легко отлаживать программы. Но этот же факт, в свою очередь, приводит к тому, что Python заметно уступает в скорости более низкоуровневым языкам. К тому же Python использует динамическую типизацию, что требует дополнительных проверок и снижает производительность и если данных очень много, это может приводить к значительным сложностям, связанным с увеличением времени вычислений. Как же использовать ту легкость и скорость разработки Python и при этом сохранить адекватное время вычислений на больших объемах данных? В этой статье мы увидим, насколько перенос вычислений на GPU может увеличить производительность вычислений.

https://habr.com/ru/articles/893748/

#python #cuda #numba #gpu #backtesting #производительность

Coming soon 🥁 #python #numba #rust

Coming soon 🥁 #python #numba #rust

Made a python library called #pyrebel which implements the idea of abstraction of data. The idea is explained in https://github.com/ps-nithin/pyrebel/blob/main/intro-r2.pdf. The program runs on an #nvidia #gpu and uses #numba library for using #cuda with #python . I have demo programs for image abstraction and edge detection at https://github.com/ps-nithin/pyrebel

Thanks,

@nen while still on Python, have you tried scientific computation speed up things like #Numba ? Also, I once saw a #Cython talk and I was almost convinced to give it a go (but I'm too lazy) :D

@nen while still on Python, have you tried scientific computation speed up things like #Numba ? Also, I once saw a #Cython talk and I was almost convinced to give it a go (but I'm too lazy) :D

🚀 Parallel Python Made Easy! 🐍

We're hosting a hands-on tutorial on PyOMP, a system bringing OpenMP parallelism to Python! By combining OpenMP directives (as strings) with Numba's JIT compiler, PyOMP taps into LLVM's OpenMP support, delivering C-like performance in Python's simplicity.

Our participants are mastering this game-changing tool to supercharge their workflows.

Stay tuned for updates!

#Python #OpenMP #PyOMP #ParallelComputing #Numba #HPC

For whatever is worth, a couple of comparisons for numerical tasks involving #python (all varieties, base language, #numba, #numpy) , #rstats, and (you read it correctly) #perl chrisarg.github.io/Killing-It-w... #perl has an autothreading library, #pdl that's 🔥 chrisarg.github.io/Killing-It-w...

The Quest for Performance Part...

I am doing https://github.com/srush/GPU-Puzzles/

My solution for puzzle 9 "Pooling" is

def pool_test(cuda):
    def call(out, a, size) -> None:
        shared = cuda.shared.array(TPB, numba.float32)
        i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
        local_i = cuda.threadIdx.x

        mysum = 0
        for j in range(max(0,i-2), i+1):
            mysum += a[j]
        out[i] = mysum

    return call

But I doubt that this is the solution, as I don't utilize the shared memory...

#programming #python #numba

Learn GPU Programming With Simple Puzzles https://hackaday.com/2024/09/25/learn-gpu-programming-with-simple-puzzles/ #googlecollaboratory #SoftwareHacks #programming #NUMBA #CUDA #gpu

Learn GPU Programming With Simple Puzzles https://hackaday.com/2024/09/25/learn-gpu-programming-with-simple-puzzles/ #googlecollaboratory #SoftwareHacks #programming #NUMBA #CUDA #gpu

Learn GPU Programming With Simple Puzzles - Have you wanted to get into GPU programming with CUDA but found the usual textbook... - https://hackaday.com/2024/09/25/learn-gpu-programming-with-simple-puzzles/ #googlecollaboratory #softwarehacks #programming #numba #cuda #gpu

Learn GPU Programming With Simple Puzzles - Have you wanted to get into GPU programming with CUDA but found the usual textbook... - https://hackaday.com/2024/09/25/learn-gpu-programming-with-simple-puzzles/ #googlecollaboratory #softwarehacks #programming #numba #cuda #gpu

Reminder: @numba is an *excellent* accelerator for numeric computations in Python. Also, issues with Python's stability are greatly exaggerated. Case in point: I just ran my 8yo n-body benchmarks without modification:

https://github.com/jni/nbody-numba

and they were just 10% slower than the fastest C code — and that's including all the Python launch time and JIT warmup!

#numba #Python

Reminder: @numba is an *excellent* accelerator for numeric computations in Python. Also, issues with Python's stability are greatly exaggerated. Case in point: I just ran my 8yo n-body benchmarks without modification:

https://github.com/jni/nbody-numba

and they were just 10% slower than the fastest C code — and that's including all the Python launch time and JIT warmup!

#numba #Python

Trying to get https://github.com/pythonspeed/profila working on macOS.

Good news: I've gotten debugger-based sampling using lldb, which means it's usable on macOS.

Bad news: lldb is ludicrously slow. It takes 100ms to take a sample. And that's not time the program is running, that's just lldb. So if you spend 50% time in lldb, and 50% time actually running, that's 5 Hz sampling rate, not exactly the best rate for profiling.

(For comparison, I'm easily getting 50Hz from gdb).

#python #numba

Trying to get https://github.com/pythonspeed/profila working on macOS.

Good news: I've gotten debugger-based sampling using lldb, which means it's usable on macOS.

Bad news: lldb is ludicrously slow. It takes 100ms to take a sample. And that's not time the program is running, that's just lldb. So if you spend 50% time in lldb, and 50% time actually running, that's 5 Hz sampling rate, not exactly the best rate for profiling.

(For comparison, I'm easily getting 50Hz from gdb).

#python #numba

WARNING: Upgrading to Ubuntu 24.04 LTS (Noble Numbat) now risks system crashes, so hold off. Fixes coming soon.
https://linuxiac.com/do-not-try-to-upgrade-to-ubuntu-24-04-at-this-time/

#ubuntu #numba

WARNING: Upgrading to Ubuntu 24.04 LTS (Noble Numbat) now risks system crashes, so hold off. Fixes coming soon.
https://linuxiac.com/do-not-try-to-upgrade-to-ubuntu-24-04-at-this-time/

#ubuntu #numba

I've just been introduced to #numba and... is this as revolutionary as it seems?

#python #programming

https://numba.readthedocs.io/en/stable/user/5minguide.html

I've just been introduced to #numba and... is this as revolutionary as it seems?

#python #programming

https://numba.readthedocs.io/en/stable/user/5minguide.html

#PyQtGraph 0.13.5 is out! While not our largest release, it has something for everyone! Note, this release is the last to support #Python
3.9 and #NumPy 1.22.

First, ImageItem got another substantial performance boost, especially if you're using #numba but there is a significant boost for #NumPy users as well.

A ColorMapMenu was added to ColorBarItem, allowing for users to be able to change color maps interactively instead of programmatically.

#DataViz #PyQt #Qt

Calling all pythonistas who are interested in executing code on the GPU (but haven't had an interest in learning CUDA C), come to our April MadPy gathering to learn about what @numba can do for you!

We'll be meeting at the downtown Madison Public Library. Attendance is free and open to all. Free pizza and beverages will also be provided 🍕 🥤

Looking forward to seeing everybody!

https://madpy.com/meetups/2024/4/11/20240411-gpu-programming-in-python/

#MadisonWI #MachineLearning #UWMadison #GPU #Numba #Python #CUDA

We've been looking forward to a great talk from Joe Farchione on #GPU computing in #Python. It's now on our calendar for April 11th, and you should make sure it's on yours!

Learn how to unlock the power of parallelism without the hassle of learning #CUDA

Newcomers & beginners welcome! This event is free and open to the public

https://www.meetup.com/madison-python/events/299615775

#Madison #MadisonWI #UWMadison #Wisconsin #DataScience #AI #MachineLearning #numba

Just announced: "GPU Programming in Python"

Our speaker, Joe Farchione, will show us how to harness the power of parallelism with #GPU computing. We'll learn how #Python & #numba help us skip the pain of learning #CUDA #C.

Newcomers & beginners welcome! This event is free and open to the public

https://www.meetup.com/madison-python/events/298119328/

P.S: Sponsor us! Please reach out if you or your employer is interested in covering pizza & beverages for our group

#Madison #MadisonWI #UWMadison #Wisconsin #DataScience

So I need to write some #NumericalPython code that needs to run on large arrays and loads of times. I thought I'd test #numba, #jax and #pytorch versus a simple #numpy version. The system where I run this doesn't have GPUs, only CPUs. The results show that numba is the "winner". I expected jax and torch to be up there with it, but there's quite a bit of a difference. My code is here: https://gist.github.com/jgomezdans/6560e2971904794d91298f741acbbfc7 Any comments welcomed!

The best days are when you can learn something new! Today, the @eScienceCenter is filled with participants wanting to gain first-hand experience in learning how to use #Python and #CUDA to program #GPUs! They'll even become familiar with libraries such as #CuPy and #NUMBA to accelerate Python code.

Achieve incredible computing capabilities with our #GPU Programming #Workshops. We will teach you the basic knowledge of #Numba and CuPy required to start a journey in the world of GPUs. Registration closes 30 October. ✍️ Sign up now!
https://www.esciencecenter.nl/event/gpu-programming-3/

I read the FAQ, and they answer a lot of “why not X” questions (make CPython faster, use Julia, etc), but comparison to #numba is a glaring omission. Doubtless they can’t be ignorant of numba, so it’s probably because it doesn’t look good: numba is wicked fast and the API is super easy.

Anyway, the Anaconda engineering team has a great comparison here: https://engineering.anaconda.com/2023/09/numba-mojo.html

2/3

So at least with #python on my computer, log is faster than log10 by a factor of two. Similar results obtained from #Numpy and #numba . Wat?

#programming