Setting up a shell environment on a remote server can be a tedious process, especially when dealing with multiple dependencies. This one also can be used for your local machine.
This guide walks you through installing micromamba, setting up a River environment, and configuring useful tools like goofys to work with cloud storage (S3) and zsh (amazing shell)
When working on a remote High-Performance Computing (HPC) cluster or a cloud server, accessing development tools locally can be challenging.
One effective approach is to use an SSH tunnel to securely access a galaxy server-a web platform as if it were running on your local machine.
Modern computers are designed to handle multitasking, enabling you to run multiple programs simultaneously. But have you ever wondered how computers manage this complexity?
In programming, Python is one of the most popular languages, and it supports multitasking through multiple processes and threads. However, Python has a unique feature that might lead to inefficient usage if not understood properly. Let’s dive in and explore.
Figure 1: A system monitor displaying process IDs (PIDs), user ownership, and resource consumption (e.g., memory and CPU).
SLURM (Simple Linux Utility for Resource Management) is a powerful open-source workload manager commonly used in high-performance computing (HPC) environments. While it is typically deployed across multi-node clusters, setting up SLURM on a single node can be highly beneficial for testing, development, or running lightweight workloads. This guide will help you understand the fundamental concepts of how a scheduler operates and provide a step-by-step walkthrough to configure SLURM with full functionality on a single-node setup.
Welcome to RiverXData, a data platform designed for scalable and efficient data analysis. Built on top of SLURM, RiverXData brings the power of high-performance computing (HPC) to a user-friendly web-based interface, enabling researchers, engineers, and data scientists to run complex computational tasks with ease.