Search Results for "ram speed benchmark"

...Then, using DnsMasq, Valet proxies all requests on the *.test domain to point to sites installed on your local machine. In other words, a blazing-fast Laravel development environment that uses roughly 7 MB of RAM. Valet isn't a complete replacement for Vagrant or Homestead, but provides a great alternative if you want flexible basics, prefer extreme speed, or are working on a machine with a limited amount of RAM.

Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive...

This is a package for Structural Equation Modeling in development. It is written for extensibility, that is, you can easily define your own objective functions and other parts of the model. At the same time, it is (very) fast. We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof. As a user, you can easily define custom loss functions. For those, you can decide to provide analytical gradients or use finite difference approximation /...

An Express-inspired web framework written in Go. Fiber is a Go web framework built on top of Fasthttp, the fastest HTTP engine for Go. It's designed to ease things up for fast development with zero memory allocation and performance in mind. Setting up routes for your application has never been so easy! The Express-like route definitions are easy to understand and work with. Serve your static HTML, CSS, and JavaScript files with ease by defining static routes. You can also serve the contents...

Approximate Reasoning trades completeness and soundness for increasing speed. The focus of this project is to develop scalable reasoning techniques for OWL ontologies.