Browse free open source Distributed Computing software and projects below. Use the toggles on the left to filter open source Distributed Computing software by OS, license, language, programming language, and project status.
High-speed, 3D-friendly, TightVNC-compatible remote desktop software
PTP IEEE 1588 stack for Linux
X Window System Server for Windows
A robust student and learning management system offering a holistic approach to education. Classter is a modular, cloud-based management solution that provides all key features required for the effective administration of any academic institution: K12, University and Academies!
Classter is a global pioneer in the field of Education Technology, offering an all-in-one Cloud-based SaaS that bundles: Student Information System (SIS), School Management System (SMS) & Learning Management System (LMS). The platform offers an end-to-end and modular information management solution that can be used by any educational institution. Fully integrated with Office 365, Google G-Suite and other 3rd party systems: from ERP and government databases to SMS services and BI tools. More than 200 academic institutions from all educational stages trust Classter to bring their people, operations and data together in one place.JupyterLab computational environment
Distributed tracing system to gather timing data
Scalable, distributed monitoring system for high-performance computing
CGRU: Afanasy render farm manager and RULES project tracker.
Distro Penetrasing Live System Burn to USB Flash Disk & Run.
Community superset free distribution of gridengine/SGE batch system
Distributed and Parallel Computing with/for Python.
Legacy Release only. Get latest Edition here: http://www.openqrm.com.
A durable Datalog implementation adaptable for distribution
Open source distributed computing software is a type of computer application used to perform large-scale tasks, such as massive number crunching or complex computations. By utilizing multiple computers connected through the internet, distributed computing projects can achieve results faster than single systems. This type of software has been available for decades and continues to evolve with advances in technology.
The open source model is based on allowing users to freely access, modify and share its code as they see fit. This makes it an attractive option for organizations that need powerful tools without expense or restrictions. Open source projects are often developed collaboratively by a community of volunteers who work up from basic building blocks. These components can provide many advantages over traditional software packages including flexibility, scalability and cost savings.
For example, Hadoop is one of the most widely adopted open source distributed computing platforms today. It consists of several modules which enable efficient storage and parallel processing of large amounts of data across clusters of computers (nodes). It is supported by major cloud providers such as Google Cloud Platform and Amazon Web Services, which offer managed Hadoop services that make it easier to deploy large data processing jobs quickly at little cost.
Overall, there are numerous benefits associated with open source distributed computing software: flexibility in terms of design and development; scalability across physical boundaries; cost savings due to the availability of free tools; freedom from restrictive licensing; and collaboration between developers worldwide resulting in more feature-rich applications that benefit everyone involved.
Open source distributed computing software generally does not require any cost, as it can be downloaded and used for free. There are several popular open source packages available, such as Apache Hadoop, Apache Storm and Apache Spark. These can typically be downloaded from the web without any charge or licensing fees. Some of these programs might also include additional support services or extended features that may require an additional fee, but in general, most users will have access to the core functionality just by downloading the software for free.
The main benefit of using open-source distributed computing is that you don't need to pay expensive software license fees or maintenance costs since it is released under an open-source license. This allows developers and businesses to save money while still benefiting from high performance computing capabilities they would normally not get with proprietary systems. Additionally, since the codebase is made publicly available, it enables experienced coders to contribute their own efforts in improving upon existing solutions or developing new ones that better meet their needs.
Overall, distributed computing software can provide a great deal of power and flexibility when properly implemented — regardless if it's an open source package or a proprietary one — but choosing an open source solution can lead to significant savings in terms of development time and resource requirements along with potential cost savings when compared to other commercial solutions.
Open source distributed computing software can integrate with a wide variety of software types. For example, many development frameworks that are used to build applications, such as languages like Python and JavaScript, are able to connect easily to open source distributed computing software. Additionally, operating systems such as Linux and macOS are compatible with this type of software. Open source databases like MongoDB or web servers like Apache Tomcat also provide integration capabilities so they can be used in conjunction with distributed computing applications. Finally, there is also potential for integration among specific cloud services, such as Google Cloud Platform and Microsoft Azure, which could enable the deployment of large-scale open source distributed computing projects.
Getting started with open source distributed computing software is a relatively straightforward process. First, you'll need to download the software from an online source such as Github or SourceForge. Then, you'll need to install and configure the software on your computer or server. Depending on the complexity of the software, this could take anywhere from a few minutes to an entire day.
Once installed, you can start exploring what the application does by playing around in its graphical user interface (GUI). This will give you a great feel for how it works and what features it offers. If there aren't any GUI-based options available with your chosen software package then some configuration files may have to be manually edited in order to get things working correctly.
Next, you should begin familiarizing yourself with all of its capabilities by reading tutorials, documentation and blogs related to the application. You should also read up on any API's or scripting language interfaces that are available so that you can better integrate your existing systems with the new application - this will allow for greater flexibility and scalability over time.
Finally, once all of these steps are complete, it's time to begin using your new distributed computing system. To do this, simply create jobs and assign them resources (either physical or virtual) according to their various requirements: whether it’s CPU processing power required , RAM needed for specific tasks or network bandwidth needed for data transmission . Once submitted, these jobs can then run in parallel across multiple nodes which helps speed up computation times significantly - creating massive efficiency gains compared to running tasks serially on a single machine. Additionally , from here , users can monitor job performance , reallocate resources if needed , throttle speeds if necessary , evaluate results and draw conclusions about their overall setup .