Common state-of-the-art simulations employ hundreds of thousands CPU cores on high-performance computing (HPC) systems to solve big societal challenges. In this context, the scalability and the simulation kernel performance are key to efficient multi-core computations. ▶
In this article, we outline the setup and configuration of a basic “headless” cluster with the end goal of running parallel programs based on message passing, using the Message Passing Interface (MPI) parallel programming model in particular. ▶
The Docker team has developed a clustering and scheduling tool for Docker containers, called swarm. This article describes how one can create an ODROID-HC1 based swarm cluster. This cluster fits in a 19″ rack. ▶
Remember Part 1 of this guide? We set up a Bitcoin full node with Lightning from scratch, went to quite some length to secure our box, and started testing Bitcoin on testnet. ▶
ARM cores are rapidly being adopted by the scientific Datacenter community because ARM devices compute faster in floating-point math intensive operations, for a fraction of the energy costs, and have an architectural roadmap of even more performance per watt to come. There is also a corresponding growth in interest in HPC (High Performance Computing) and its uses in broader domains from the ARM developer community. ▶
In my quest for a true 64-bit quad-core SBC with each core close to 2 Ghz and with at least 4GB of RAM, I came upon this newly released beauty called ODROID-N2 Let's do a cluster with it! ▶
In the past few years, the topics of big data and data science have grown into mainstream prominence across countless industries. No longer are high tech companies in Silicon Valley the sole purveyors of topics like Hadoop, logistic regression, and machine learning. Being familiar with big data technologies is becoming ▶
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