Search Results for "ram speed benchmark"
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8 projects for "ram speed benchmark" with 2 filters applied:
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Vibes don’t ship, Retool doesVibe coding tools create cool demos, but Retool helps you build software your company can actually use. Generate internal apps that connect directly to your data—deployed in your cloud with enterprise security from day one. Build dashboards, admin panels, and workflows with granular permissions already in place. Stop prototyping and ship on a platform that actually passes security review.
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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JMH Gradle Plugin
Integrates the JMH benchmarking framework with Gradle
...The plugin is especially useful in projects where regression in execution speed or memory use must be carefully monitored. -
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Laravel Valet
A more enjoyable local development experience for Mac
...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. -
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Apache Sedona
Cluster computing framework for processing large-scale geospatial data
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... -
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StructuralEquationModels.jl
A fast and flexible Structural Equation Modelling Framework
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 /... -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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GoNB
GoNB, a Go Notebook Kernel for Jupyter
Go is a compiled language, but with very fast compilation, that allows one to use it in a REPL (Read-Eval-Print-Loop) fashion, by inserting a "Compile" step in the middle of the loop -- so it's a Read-Compile-Run-Print-Loop — while still feeling very interactive. GoNB leverages that compilation speed to implement a full-featured (at least it's getting there) Jupyter notebook kernel. As a side benefit it works with packages that use CGO — although it won't parse C code in the cells, so it... -
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UVU
UVU is an extremely fast and lightweight test runner for Node.js
UVU is a lightweight, ultra-fast JavaScript test runner for Node.js and the browser created by Luke Edwards (lukeed). Its primary goal is to minimize overhead: startup time, memory usage, and complexity are all kept very low so that developers can write and run tests with minimal friction. The tool supports modern features like async/await and native ES Modules, making it suitable for modern codebases. It also supports running in browser environments, which makes it versatile for frontend... -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ... -
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SBI - Small Bytecode Interpreter
SBI - A Small Bytecode Intepreter to run programs on an AVR from SD
...SBI is a generic library (platform indipendent) that lets you run programs from a stream (es. File). The programs are a sort of bytecode, and they are interpretated at the moment. So you don't have the better speed, but for now this is the only way to run programs from RAM (or SD) on Harvard architectures.
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