Parallel Computing with Python on a Raspberry Pi Cluster || OpenMPI and mpi4py install
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- เผยแพร่เมื่อ 27 ก.ย. 2024
- Today we are making a mini supercomputer! Not really, but sort-of, we are building a 4-node raspberry pi cluster. On this cluster we will be installing a package called OpenMPI which is a Message Passing Interface (MPI) critical for Parallel Computing. An MPI allows a complicated problem to be split into different processes and computed on separate processors, perhaps even on different nodes of a cluster.
In this tutorial, we will go through how to install OpenMPI, Python packages from source, and how we can use OpenMPI with Python using a module called mpi4py. This will enable us to use all nodes on our cluster to approximate pi ~3.14… We will be following three tutorials from Garrett Mills on Medium for installation on SLURM and enabling parallel computing using openMPI.
Building a raspberry pi cluster (Garrett Mills, 2018): / building-a-raspberry-p...
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The presantation of the topic is excellent from your side complying with educational and lecturer rules... congradulation ... I follow you
Do you think "GNU parallel" could help? I mean write a python script with only one input, and use GNU parallel to run ,say 10 inputs
You should check out the Julia programming language: it is natively much faster than python in many cases and is as easy to write. There are also a lot of packages for statistical inference and model building, and many of them are written in Julia rather than c, so you can modify them for your own needs better than packages like numpy in python.
Are you using Julia right now? Never heard of it, what sort of things do you use it for?
@@jamesr3368 I have been using Julia a lot lately. I use it for prototyping ideas like I might in python, but it has some optimizations out of the box that let me create "structs" which are like python types while still being able to run things very fast (on the same order of magnitude of speed as with numpy). The big advantage is that numpy doesn't let you create your own dtypes while Julia works as fast with the structs you make as with others. This feature makes it very nice to code things like physics simulations with a simple language that still runs very fast. There's an MIT lecture on TH-cam called "high performance in dynamic languages" that explains exactly how it is so fast. I'm currently using it to reproduce some of the things shown on this channel on stuff like efficient frontiers, but I also want to use it for physics simulations and dynamical inference and a handful of other ideas I have in the works.
Hey ASX can you make some videos about quantitative analysis?
Can you mine Bitcoin on it?
😂