Search Results for "computations"
Sort By:
Showing 13 open source projects for "computations"
View related business solutions-
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
-
Outgrown Windows Task Scheduler?Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
-
1
Oscar.jl
A comprehensive open source computer algebra system for computations
...In principle it can be installed and used like any other Julia package; doing so will take a couple of minutes. A comprehensive open source computer algebra system for computations in algebra, geometry, and number theory. -
2
ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
...ParallelStencil relies on the native kernel programming capabilities of CUDA.jl and AMDGPU.jl and on Base.Threads for high-performance computations on GPUs and CPUs, respectively. It is seamlessly interoperable with ImplicitGlobalGrid.jl, which renders the distributed parallelization of stencil-based GPU and CPU apps. -
3
Dagger.jl
A framework for out-of-core and parallel execution
Dagger.jl is a framework for out-of-core and parallel computing in Julia that allows users to construct and execute dynamic task graphs. It is designed for large-scale, distributed, and memory-efficient computations. Dagger supports lazy evaluation and scheduling across multiple threads or machines, enabling high-performance workflows for data processing, scientific computing, and machine learning. -
4
ProgressMeter.jl
Progress meter for long-running computations
ProgressMeter.jl is a lightweight Julia package that provides customizable progress bars for long-running loops and computations. It allows developers to track the progress of tasks with real-time visual feedback in the terminal, making it easier to monitor performance, debug slow operations, or report computational progress in user-facing applications. -
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.
-
5
LazySets.jl
Scalable Symbolic-Numeric Set Computations
LazySets.jl is a Julia package for calculus with convex sets. The aim is to provide a scalable library for solving complex set-based problems, such as those encountered in differential inclusions or reachability analysis techniques in the domain of formal verification. Typically, one is confronted with a set-based recurrence with a given initial set and/or input sets, and for visualization purposes, the final result has to be obtained through an adequate projection onto low dimensions. This... -
6
Finch.jl
Sparse tensors in Julia and more
Finch is a cutting-edge Julia-to-Julia compiler specially designed for optimizing loop nests over sparse or structured multidimensional arrays. Finch empowers users to write conventional for loops which are transformed behind-the-scenes into fast sparse code. -
7
Hecke.jl
Computational algebraic number theory
Hecke is a software package for algebraic number theory maintained by Claus Fieker, Tommy Hofmann and Carlo Sircana. It is written in julia and is based on the computer algebra packages Nemo and AbstractAlgebra. Hecke is part of the OSCAR project and the development is supported by the Deutsche Forschungsgemeinschaft DFG within the Collaborative Research Center TRR 195. -
8
PartitionedArrays.jl
Vectors and sparse matrices partitioned into pieces
This package provides distributed (a.k.a. partitioned) vectors and sparse matrices in Julia. See the documentation for further details. -
9
GraphNeuralNetworks.jl
Graph Neural Networks in Julia
GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl. -
Free and Open Source HR SoftwareGive your HR team the tools they need to streamline administrative tasks, support employees, and make informed decisions with the OrangeHRM free and open source HR software.
-
10
StaticArrays.jl
Statically sized arrays for Julia
StaticArrays.jl is a Julia package that provides statically sized arrays with fast, stack-allocated memory storage and optimized performance for small array computations. It is particularly useful in numerical computing where small fixed-size matrices or vectors are used frequently, such as in robotics, physics simulations, or linear algebra. StaticArrays eliminate dynamic memory allocation overhead and enable compile-time optimizations for performance close to hand-written loops. -
11
ModelingToolkit.jl
Modeling framework for automatically parallelized scientific ML
ModelingToolkit.jl is a modeling language for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. It then mixes ideas from symbolic computational algebra systems with causal and acausal equation-based modeling frameworks to give an extendable and parallel modeling system. It allows for users to give a high-level description of a model for symbolic preprocessing to analyze and enhance the model. Automatic symbolic transformations, such as... -
12
ITensors.jl
A Julia library for efficient tensor computations and tensor network
ITensors.jl is a high-performance Julia library for tensor network calculations, primarily used in quantum physics and computational science. It enables efficient manipulation of large, structured tensors with named indices and provides an intuitive interface for implementing algorithms like DMRG (Density Matrix Renormalization Group), TEBD (Time-Evolving Block Decimation), and more. ITensors.jl leverages Julia’s multiple dispatch and performance features to simplify the development of... -
13
JUDI.jl
Julia Devito inversion
...The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation.
- Previous
- You're on page 1
- Next
