Raspberrypi

MPICH Raspberry Pi Cluster The earliest project to run MPICH on a Raspberry Pi cluster would be Raspberry Pi at Southampton, in which professor Simon Cox and his son together to build an MPI cluster consisting of 64 Raspberry Constructs and Legos. This project is already 10 years old, and at that time the raspberry pi 1 B+ was used, and due to the limitations of the performance of the 1st generation, the whole cluster could only be used for MPI learning, which is still under-utilized in high-performance computing. However, now raspberry pi 4 is still very weak compared to Intel servers, but it still has great potential to run MPICH related computing projects compared to the 1st generation. First of all, power consumption, 64 raspberry pi 4 CPUs running at full load consume about 400 watts, and secondly, the ARM ecosystem is now very well developed compared to a decade ago, and a lot of programs have been ported to the ARM64 platform, with a variety of tools at your fingertips. ...

Problem The system of raspberry pi zero w is currently raspberry pi official os image is armv6hf debian 11, and ROS2 official support only ubuntu debian aarch64, not support arm32, so to use ROS2 on raspberry pi zero w must be built from source. So the problem is that if you compile directly on zero w, obviously because of the hardware (cpu: 1 core, memory: 512M), the compilation will be extremely slow, I also tried to use armhf cross-compiler toolchain to compile the ROS source code on aarch64 system, but there will be a problem of missing system libraries. At the same time, I also consider the consistency of the subsequent development environment and zero w’s original system environment, I decided to use zero w’s native debian 11 armhf system as rootfs, make a docker image, and then run on aarch64 systems (such as raspberry pi 4, jetson agx), so that the compiled ros2 can be run on zero w directly. This way, the compiled ros2 can be run on zero w, and the runtime and compilation environments are consistent, which will be very convenient for subsequent tuning and debugging. ...

Coral ecology and technology References: Coral AI Introducing the entire ecosystem of google coral edge TPU computing. SoM for production environment and PCIe Accelerator. Four pocket-sized development boards for development and testing. Peripheral sensors including environmental detection module as well as camera, Wi-Fi/PoE expansion board. Cloud TPU / Edge TPU Introduction Technology Coral Edge TPU is a dedicated chip developed by Google to accelerate neural network inference in edge devices and run specially optimized neural network models while maintaining low power consumption. ...

I designed a rudimentary k8s persistentvolumeclaims solution myself, using Moosefs on the backend as a distributed data store. The principle is based on the kubernetes events mechanism, when a create pvc message is received, the backend control application will create the corresponding pv on moosefs and bind the corresponding pvc, in order to facilitate the application to move between nodes, I mount the moosefs file system to each worker node, here a problem arises: how to keep the moosefs master high availability! ...

This is the architecture of the content management platform I currently use, the front end of the two google comput engine for connecting to the global Internet and hosting static sites, the back-end cluster all running on my owner hosted ARM64 system platform, the Cloudflared tunnel is running inside of kubernetes cluster, when the user requests invisibleart, the request will be sent to the tunnel through the cloudflare edge network, then cloudflared will send the request to the target address wordpress engine, the backend database is used vitess mysql cluster, the early use of tidb I found that compatibility problems with wordpress, and finally I decided to use vitess, in the backup, expansion, and disaster recovery, vitess is still very reliable. ...