Search Results for "parallel computing" - Page 2
Sort By:
Showing 84 open source projects for "parallel computing"
View related business solutions-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
1
Equalizer is the standard middleware to create parallel OpenGL-based applications. Please visit https://github.com/Eyescale for current development information.
-
2
-
3
HadoopDB is a hybrid of parallel database and MapReduce technologies. It approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce systems.
-
4
Simdist lets you harness the power of cluster computing without any knowledge of parallel libraries such as MPI, and with no restrictions on programming language. Primarily targeted at evolutionary computing and similar master-slave configurations.
-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
5
GridSim allows modeling and simulation of entities in parallel and distributed computing systems such as users, applications, resources, and resource brokers/schedulers for design and evaluation of scheduling algorithms. http://www.gridbus.org/gridsim
-
6
RT-BOINC stands for a Real-Time BOINC. It was designed for managing highly-interactive, short-term, and massively-parallel real-time applications. We implemented RT-BOINC on top of the recent BOINC server source codes.
-
7
Distributed Parallel Programming for Python! This package builds on traditional Python by enabling users to write distributed, parallel programs based on MPI message passing primitives. General python objects can be messaged between processors. Ru
-
8
Roomy is a programming language extension for writing parallel disk-based applications. All details of parallelism and disk I/O are hidden within the Roomy library.
-
9
PyMW is a Python module for parallel master-worker computing in a variety of environments. With the PyMW module, users can write a single program that scales from multicore machines to global computing platforms.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
10
BSPonMPI is an implementation of the BSPlib standard on top of MPI. Both MPI and BSPlib are API's of communication routines meant for parallel computing, but BSPlib is easier to learn and its performance easier to predict.
-
11
Parallelizable is a tiny framework in complement for java.util.conccurent api. Parallelizable is an abstract class to extend for making asynchronous parallel execution on a synchronous task. Very usefull if task often calls a very long process.
-
12
Numerical package Parallel EXECution. Using a numerical package like Octave, 'npexec' distribute computation of a math formula among various network elements, collecting result automatically. ->y=npexec('fft(x.^m)', 'm=-8:8','x=ARG');
-
13
Meerkat is a distributed programming environment. It consists of a virtual machine which is suited to parallel processing. The data model is based on the concept of actors, although it is much more permissive than the traditional description.
-
14
The Application Agent (AA) is a software providing the platform and mechanism for the distributed/parallel applications to interact and negotiate with the underlying grid resource infrastructure such as Condor and SGE on one-to-one and dynamic basis.
-
15
LIME (Less-is-More) is parallel/concurrent programming environment based on C. Internally, it uses XML technology to describe tasks and their dependencies. Externally, it offers the ANSI C99 programming as well as a set of visually-oriented interfaces.
-
16
NetWorkSpaces, developed by Scientific Computing Associates Inc., provides a framework to coordinate programs written in R. It allows users to write parallel programs in R easily.
-
17
Pydusa is a package for parallel programming using Python. It contains a module for doing MPI programming in Python. We have added parallel solver packages such as Parallel SuperLU for solving sparse linear systems.
-
18
Python Integrated Parallel Programming EnviRonment (PIPPER), Python pre-parser that is designed to manage a pipeline, written in Python. It enables automated parallelization of loops. Think of it like OpenMP for Python, but it works in a computer cluster
-
19
Open FMM ( http://www.openfmm.net ) is and open source EM software project. It consist of a fast parallel two-dimensional TM/TE solver called Nero2d.
-
20
The CodeTime platform covers every aspect of parallel software from authoring, through distribution, to run-time. Its goals are: high programmer productivity; write once, run high performance anywhere; and wide acceptance.
-
21
PPIV is a parallel software application used in Particle Image Velocimetry (PIV) analysis. The software will compute the vector field for one image pair or multiple image pairs. PPIV can run on a single desktop machine or in a cluster environment.
-
22
A data parallel scientific programming model. Compiles efficiently to different platforms like distributed memory (MPI), shared memory multi-processor (pthreads), Cell BE processor, Nvidia Cuda, SIMD vectorization (SSE, Altivec), and sequential C++ code.
-
23
This project aims to create a method able to determine the most frequent word phrases in a large source of text data (>5 Gb) using the computational power of multiple processors.
-
24
-
25
Simple Distributed Job Management Tools facilitates parallel, distributed execution of simple commands, on a network of UNIX-like machines. Components include a job dependency/exclusivity language and load-balanced remote execution facility.
