AI Face Changer Video

AI Face Changer Video — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Georges Giralt PhD Award

The Georges Giralt PhD Award is a European scientific prize for extraordinary contributions to robotics. It is awarded yearly at the European Robotics Forum by euRobotics AISBL, a non-profit organisation based in Brussels with the objective of turning robotics beneficial for Europe’s economy and society. Georges Giralt received his PhD in 1958, from Paul Sabatier University, in the domain of electrical machines, and soon afterwards became a pioneer in robotics, in Europe and worldwide. He was especially instrumental in bringing in scientific foundations and methodology when the domain was still young, and a loose coupling of mechanical and electrical engineering, adopting the early results of automatic control. The high reputation of the Georges Giralt PhD Award is based on the prominent role of the awarding institution euRobotics. With more than 250 member organisations, euRobotics represents the academic and industrial robotics community in Europe. Moreover, it provides the European robotics community with a legal entity to engage in a public/private partnership with the European Commission. The award is covered by various media. Entitled for participation in the Georges Giralt PhD Award are all robotics-related dissertations which have been successfully defended at a European university. The US-American counterpart is the Dick Volz Award. == Award winners == 2026: Antonio González Morgado 2025: Erfan Shahriari 2024: Manuel Keppler 2023: Antonio Andriella, Ribin Balachandran 2022: Antonio Loquercio, Michael Lutter 2021: Giuseppe Averta, Bernd Henze 2020: Cosimo Della Santina 2019: Grazioso Stanislao, Teodor Tomic 2018: Frank Bonnet, Daniel Leidner 2017: Johannes Englsberger 2016: Alexander Dietrich, Mark Müller 2015: Jörg Stückler 2014: Manuel Catalano, Fabien Expert, Rainer Jaekel 2013: Jens Kober 2012: Sami Haddadin 2011: Mario Pratts 2010: Ludovic Righetti 2009: Alejandro-Dizan Vasquez-Govea 2008: Cyrill Stachniss, Eduardo Rocon 2007: Pierre Lamon 2006: Martijn Wisse 2005: Juan Andrade Cetto 2004: Gilles Duchemin 2003: Ralf Koeppe 2002: Gianluca Antonelli, Jens-Steffen Gutmann
Read more →
GCube system

gCube is an open source software system specifically designed and developed to enact the building and operation of a Data Infrastructure providing their users with a rich array of services suitable for supporting the co-creation of Virtual Research Environments and promoting the implementation of open science workflows and practices. It is at the heart of the D4Science Data Infrastructure. == Overview == It is primarily organised in a number of web service called to offer functionality supporting the phases of knowledge production and sharing. In addition, it consists of a set of software libraries supporting service development, service-to-service integration, and service capabilities extension, and a set of portlets dedicated to realise user interface constituents facilitating the exploitation of one or more services. It is designed and conceived to enact system of systems. In fact, its gCube services rely on standards and mediators to interact with other services as well as are made available by standard and APIs to make it possible for clients to use them. For instance, the DataMiner service implements the Web Processing Service protocol to facilitate clients to execute processes. The set of components dealing with Identity and Access Management rely on Keycloak and federates other IDMs thus making the overall Authentication and the Authorization management compliant with open standards such as OAuth2, User-Managed Access (UMA), and OpenID Connect (OIDC)protocols. The Catalogue relies on DCAT, OAI-PMH, and Catalogue Service for the Web to collect contents from other catalogues and data sources and offers its content by DCAT, OAI-PMH, and a proprietary REST API (gCat REST API). Its Continuous Integration/Continuous Delivery pipeline implemented by Jenkins represents an innovative approach to software delivering conceived to be scalable and easy to maintain and upgrade at a minimal cost. == History == gCube has been developed in the context of the D4Science initiative with the support of several EU projects.
Read more →
Trello

Trello is a web-based, kanban-style list-making application developed by Atlassian. Created in 2011 by Fog Creek Software, it was spun out to form the basis of a separate company in New York City in 2014 and sold to Atlassian in January 2017. == History == The name Trello is derived from the word trellis, which had been a code name for the project at its early stages. Trello was released at a TechCrunch event by Fog Creek founder Joel Spolsky. In September 2011 Wired magazine named the application one of "The 7 Coolest Startups You Haven't Heard of Yet". Lifehacker said "it makes project collaboration simple and kind of enjoyable". In 2014, it raised US$10.3 million in funding from Index Ventures and Spark Capital. Prior to its acquisition, Trello had sold 22% of its shares to investors, with the remaining shares held by founders Michael Pryor and Joel Spolsky. In May 2016, Trello claimed it had more than 1.1 million daily active users and 14 million total signups. In May 2015, Trello expanded internationally with localized interfaces for Brazil, Germany, and Spain. In 2016 Trello launched the Power-Up platform, allowing 3rd party developers to build and distribute extensions known as Power-Ups to Trello. Initial integrations included Zendesk, SurveyMonkey and Giphy. By January 2022 there were a total of 247 power-ups listed in the Power-Up directory. On 9 January 2017, Atlassian announced its intent to acquire Trello for $425 million. The transaction was made with $360 million in cash and $65 million in shares and options. In December 2018, Trello announced its acquisition of Butler, a company that developed a leading power-up for automating tasks within a Trello board. Trello announced 35 million users in March 2019 and 50 million users in October 2019. In 2020 Craig Jones, then cybersecurity operations director at Sophos, found that the company exposed the personally identifiable information (PII) data of its users, exposed through public Trello boards; the researcher first tweeted about this issue in the year 2018. On 16 January 2024 Trello suffered a data breach containing over 15 million unique email addresses, names and usernames, when the data was posted on a popular hacking forum. The data was obtained by enumerating a publicly accessible resource using email addresses from previous breach corpuses; it was then added on 22 January 2024 to the famous website collecting data breaches "Have I Been Pwned?". == Uses == Users can create task boards with different columns and move the tasks between them. Typically columns include task statuses such as To Do, In Progress, Done. The tool can be used for personal and business purposes including real estate management, software project management, school bulletin boards, lesson planning, accounting, web design, gaming, and law office case management. == Architecture == According to a Fog Creek blog post in January 2012, the client was a thin web layer which downloads the main app, written in CoffeeScript and compiled to minified JavaScript, using Backbone.js, HTML5 .pushState(), and the Mustache templating language. The server was built on top of MongoDB, Node.js and a modified version of Socket.io. == Reception == On 26 January 2017, PC Magazine gave Trello a 3.5 / 5 rating, calling it "flexible" and saying that "you can get rather creative", while noting that "it may require some experimentation to figure out how to best use it for your team and the workload you manage."
Read more →
Coghead

Coghead was a web application company based out of Redwood City, California. The company offered a web-based service for building and hosting custom online database applications. Applications were built around custom data collections and were typically designed to facilitate management of, and collaboration on, business data. Examples of Coghead's "gallery" applications include project management, simple Customer relationship management, bug tracking and extreme programming. Coghead's service was available through a limited-access beta program before "going live" for free trial accounts in April, 2007. Coghead launched a paid subscription plans in June, 2007. On February 19, 2009, Coghead announced that its intellectual property assets (its 'service') had been acquired by SAP AG (NYSE:SAP). == Product == Coghead's product was a fully hosted environment for building, accessing, and maintaining applications and the associated business data. Like other so-called "Web 2.0" companies, Coghead built its product around the idea of "software as a service". The product was intended to allow users to design a range of applications from scratch using only a drag and drop, WYSIWYG user interface, with very limited scripting or coding (if any) required. Coghead also offered its paid subscribers the ability to develop and publish "Coglets," web forms that allowed site visitors to view data in, or submit data into, the host's Coghead database. On February 19, 2009, Coghead announced that SAP AG had acquired the Coghead service through an asset purchase. The SAP asset purchase closed in the 1st Quarter 2009. Immediately upon closing the asset purchase, the public-facing service was taken off-line by SAP as they prepared to integrate the Coghead code with other SAP assets. This forced many of Coghead's customers to find alternative solutions.
Read more →
Anaconda (Python distribution)

Anaconda is an open source data science and artificial intelligence distribution platform for the Python programming language. Developed by Anaconda, Inc., an American company founded in 2012, the platform is used to develop and manage data science and AI projects. In 2024, Anaconda Inc. has about 300 employees and 45 million users. == History == Co-founded in Austin, Texas in 2012 as Continuum Analytics by Peter Wang and Travis Oliphant, Anaconda Inc. operates from the United States and Europe. Anaconda Inc. developed Conda, a cross-platform, language-agnostic binary package manager. It also launched PyData community workshops and the Jupyter Cloud Notebook service (Wakari.io). In 2013, it received funding from DARPA. In 2015, the company had two million users including 200 of the Fortune 500 companies and raised $24 million in a Series A funding round led by General Catalyst and BuildGroup. Anaconda secured an additional $30 million in funding in 2021. Continuum Analytics rebranded as Anaconda in 2017. That year, it announced the release of Anaconda Enterprise 5, an integration with Microsoft Azure, and had over 13 million users by year's end. In 2022, it released Anaconda Business; new integrations with Snowflake and others; and the open-source PyScript. It also acquired PythonAnywhere, while Anaconda's user base exceeded 30 million in 2022. In 2023, Anaconda released Python in Excel, a new integration with Microsoft Excel, and launched PyScript.com. The company made a series of investments in AI during 2024. That February, Anaconda partnered with IBM to import its repository of Python packages into Watsonx, IBM's generative AI platform. The same year, Anaconda joined IBM's AI Alliance and released an integration with Teradata and Lenovo. In 2024, Anaconda's user base reached 45 million users and Barry Libert was named company CEO, after serving on Anaconda's board of directors. He was succeeded as CEO in October 2025 by David DeSanto, who also became a company director. In May 2025, the company introduced the first unified AI platform for Open Source, Anaconda AI Platform, a central control for AI workflows that enables customization in Python-based enterprise AI development. That July, after reaching over $150 million in a Series C funding round, Anaconda was evaluated at about $1.5 billion. == Overview == Anaconda distribution comes with over 300 packages automatically installed, and over 7,500 additional open-source packages can be installed from the Anaconda repository as well as the Conda package and virtual environment manager. It also includes a GUI, Anaconda Navigator, as a graphical alternative to the command-line interface (CLI). Conda was developed to address dependency conflicts native to the pip package manager, which would automatically install any dependent Python packages without checking for conflicts with previously installed packages (until its version 20.3, which later implemented consistent dependency resolution). The Conda package manager's historical differentiation analyzed and resolved these installation conflicts. Anaconda is a distribution of the Python programming language (and previously also R) for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment. Anaconda distribution includes data-science packages suitable for Windows, Linux, and macOS. Other company products include Anaconda Free, and subscription-based Starter, Business and Enterprise. Anaconda's business tier offers Package Security Manager. Package versions in Anaconda are managed by the package management system Conda, which was spun out as a separate open-source package as useful both independently and for applications other than Python. There is also a small, bootstrap version of Anaconda called Miniconda, which includes only Conda, Python, the packages they depend on, and a small number of other packages. Open source packages can be individually installed from the Anaconda repository, Anaconda Cloud (anaconda.org), or the user's own private repository or mirror, using the conda install command. Anaconda, Inc. compiles and builds the packages available in the Anaconda repository itself, and provides binaries for Windows 32/64 bit, Linux 64 bit and MacOS 64-bit (Intel, Apple Silicon). Anything available on PyPI may be installed into a Conda environment using pip, and Conda will keep track of what it has installed and what pip has installed. Custom packages can be made using the conda build command, and can be shared with others by uploading them to Anaconda Cloud, PyPI or other repositories. The default installation of Anaconda2 includes Python 2.7 and Anaconda3 includes Python 3.7. However, it is possible to create new environments that include any version of Python packaged with Conda. === Anaconda Navigator === Anaconda Navigator is a desktop graphical user interface (GUI) included in Anaconda distribution that allows users to launch applications and manage Conda packages, environments and channels without using command-line commands. Navigator can search for packages on Anaconda Cloud or in a local Anaconda Repository, install them in an environment, run the packages and update them. It is available for Windows, macOS and Linux. The following applications are available by default in Navigator: JupyterLab Jupyter Notebook QtConsole Spyder Glue Orange RStudio Visual Studio Code === Conda === Conda is an open source, cross-platform, language-agnostic package manager and environment management system that installs, runs, and updates packages and their dependencies. It was created for Python programs, but it can package and distribute software for any language, including multi-language projects. The Conda package and environment manager is included in all versions of Anaconda, Miniconda, and Anaconda Repository. == Anaconda.org == Anaconda Cloud is a package management service by Anaconda where users can find, access, store and share public and private notebooks, environments, and Conda and PyPI packages. Cloud hosts useful Python packages, notebooks and environments for a wide variety of applications. Users do not need to log in or to have a Cloud account, to search for public packages, download and install them. Users can build new Conda packages using Conda-build and then use the Anaconda Client CLI to upload packages to Anaconda.org. Notebooks users can be aided with writing and debugging code with Anaconda's AI Assistant.
Read more →
Foodsi

Foodsi is a Polish mobile application that connects customers with restaurants, convenience stores, bakeries and cafes that have a surplus of food, allowing its users to buy the surplus at a reduced price. The service launched in 2019 in Warsaw and has expanded to other major cities in Poland. In 2023, a new feature was introduced in the app, allowing users to buy packages not only with self-pickup but also with delivery. The products range has also been expanded to include unsold magazines, cosmetics or plants. == History == The company was created in 2019 in Poland by Mateusz Kowalczyk and Jakub Fryszczyn. During studies in their home country and abroad, when they made a living working in restaurants and bakeries, they recognized the problem and the scale of food waste. They launched the application by themselves, having previously raised PLN 100,000 on their own for the purpose. Initially, Foodsi was an Android-only app, but over time, an IOS version was developed. In 2022, the startup raised PLN 6 million in a seed round from VC companies including CofounderZone and Status Starter, as well as private investors such as founders of Pyszne.pl. As of December 2023, it claimed more than 5000 businesses, serving over 1,5 million users, have saved nearly 3 million bags of food. == Purpose == Foodsi aims to significantly reduce food waste, which contributes to the Sustainable Development Goals. The application bridges the gap between the customers who are looking for shopping deals and the companies that want to reduce surplus products but are unable to sell them at a normal price. This allows the customers to buy unsold products for as little as 30% of the normal price. The company claims that every 4 out of 5 packages are sold on average. As of 2019 Foodsi employed more than 30 people. By 2024 it was more than 50. For now, Foodsi operates in major Polish cities such as Warsaw, Kraków, Trójmiasto, Wrocław, Poznań etc. However, in the upcoming years, Foodsi plans to expand to other countries. == Use == To start selling surplus, a company must leave Foodsi its contact information to register in the system. Registration in the app is completely free of charge. Then, companies offer available packages anticipating what won’t be sold and post them in the app along with the price so that users can buy them and pick them up. Companies can put their packages in the app at any time during the day. Users can pick up packages from bakeries, grocery stores, restaurants, but also florists and beauty stores. Foodsi charges a small commission on each package from the cooperating companies. If a user wants to start ordering packages from Foodsi, he or she needs to install the app on their mobile phone (Android or IOS) and register an account. The app displays a list of restaurants and other venues available in a specific region set by the user's location. Customers can see the price, address, distance and time range for package pickup. Packages are usually in the form of so-called 'surprise-packages', meaning that customers do not know specifically what kind of food/product will be inside. Some restaurants offer a choice of different package sizes. Prices are up to 70% lower than those of the original products. Customers have to show up at the restaurant to pick up the package using their phone at a time specified in the app. == Awards == Auler All-Stars 2025 - 3rd place Deloitte Technology Fast 50 - 2025 Central Europe Executive Club - Innowacja Roku: Żywność i Rolnictwo - Wyróżnienie (2025) Stena Circular Economy Award - Lider Gospodarki Obiegu Zamkniętego (2025) - wyróżnienie w kategorii start-up wdrażający GOZ na rynku polskim 255th place in the international poll FoodTech 500 2025 Finalist for the EY Entrepreneur Of The Year™ 2025 Wpływowi 2024 - Laureat w kategorii “Zrównoważony rozwój” Supplier of the Year 2024 - XXII Food & Business Forum Supplier of the Year 2024 - VII Sweets & Coffee Forum Innovative Leader 2024 - Leader in Food / Food-Tech Category - Executive Summit “Orzeł Innowacji - Start-up z potencjałem Polska-Świat” (Rzeczpospolita, 2024) 102nd place in the international poll FoodTech 500 2024 Auler 2023 Startup of the Year 2023 according to money.pl Start(up) w zrównoważoną przyszłość Kongresu Kompas ESG 2023 Marka Godna Zaufania according to My Company Polska 2023 184th place in the international poll FoodTech 500 2023 In 2023, Foodsi co-founder Mateusz Kowalczyk was recognized by Forbes magazine and included in its "30 before 30" list.
Read more →
List of ARM Cortex-M development tools

This is a list of development tools for 32-bit ARM Cortex-M-based microcontrollers, which consists of Cortex-M0, Cortex-M0+, Cortex-M1, Cortex-M3, Cortex-M4, Cortex-M7, Cortex-M23, Cortex-M33, Cortex-M35P, Cortex-M52, Cortex-M55, and Cortex-M85 cores. == Development toolchains == IDE, compiler, linker, debugger, flashing (in alphabetical order): Ac6 System Workbench for STM32 (based on Eclipse and the GNU GCC toolchain with direct support for all ST-provided evaluation boards, Eval, Discovery and Nucleo, debug with ST-LINK) ARM Development Studio 5 by ARM Ltd. Atmel Studio by Atmel (based on Visual Studio and GNU GCC Toolchain) Code Composer Studio by Texas Instruments CoIDE by CooCox (note - website dead since 2018) Crossware Development Suite for ARM by Crossware CrossWorks for ARM by Rowley Dave by Infineon. For XMC processors only. Includes project wizard, detailed register decoding and a code library still under development. DRT by SOMNIUM Technologies. Based on GCC toolchain and proprietary linker technology. Available as a plugin for Atmel Studio and an Eclipse-based IDE. EmBitz (formerly Em::Blocks) – free, fast (non-eclipse) IDE for ST-LINK (live data updates), OpenOCD, including GNU Tools for ARM and project wizards for ST, Atmel, EnergyMicro etc. Embeetle IDE - free, fast (non-eclipse) IDE. Works both on Linux and Windows. emIDE by emide – free Visual Studio Style IDE including GNU Tools for ARM GNU ARM Eclipse – A family of Eclipse CDT extensions and tools for GNU ARM development GNU Tools (aka GCC) for ARM Embedded Processors by ARM Ltd – free GCC for bare metal IAR Embedded Workbench for ARM by IAR Systems ICC by ImageCraft Keil MDK-ARM by Keil LPCXpresso by NXP (formerly Red Suite by Code Red Technologies) MikroC by mikroe – mikroC MULTI by Green Hills Software, for all Arm 7, 9, Cortex-M, Cortex-R, Cortex-A Ride and RKit for ARM by Raisonance SEGGER Embedded Studio for ARM by Segger. SEGGER Ozone by Segger. STM32CubeIDE by STMicroelectronics - Combines STCubeMX with TrueSTUDIO into a single Eclipse style package Sourcery CodeBench by Mentor Graphics TASKING VX-Toolset by Altium TrueSTUDIO by Atollic Visual Studio by Microsoft as IDE, with GNU Tools as compiler/linker – e.g. supported by VisualGDB VXM Design's Buildroot toolchain for Cortex. It integrates GNU toolchain, Nuttx, filesystem and debugger/flasher in one build. winIDEA/winIDEAOpen by iSYSTEM YAGARTO – free GCC (no longer supported) Code::Blocks (EPS edition) (debug with ST-LINK no GDB and no OpenOCD required) IDE for Arduino ARM boards Arduino – IDE for Atmel SAM3X (Arduino Due) Energia – Arduino IDE for Texas Instruments Tiva and CC3200 Notes: == Debugging tools == JTAG and/or SWD debug interface host adapters (in alphabetical order): Black Magic Probe by 1BitSquared. CMSIS-DAP by Mbed. Crossconnect by Rowley Associates. DSTREAM by ARM Holdings Green Hills Probe and SuperTrace Probe by Green Hills Software. iTAG by iSYSTEM. I-jet by IAR Systems. Jaguar by Crossware. J-Link by Segger Supports JTAG and SWD. Supports ARM7, ARM9, ARM11, Cortex-A, Cortex-M, Cortex-R, Renesas RX, Microchip PIC32. Eclipse plug-in available. Supports GDB, RDI, Ozone debuggers. J-Trace by Segger. Supports JTAG, SWD, and ETM trace on Cortex-M. JTAGjet by Signum. LPC-LINK by Embedded Artists (for NXP) This is only embedded on NXP LPCXpresso development boards. LPC-LINK 2 by NXP. This device can be reconfigured to support 3 different protocols: J-LINK by Segger, CMSIS-DAP by ARM, Redlink by Code Red. Multilink debug probes, Cyclone in-system programming/debugging interfaces, and a GDB Server plug-in for Eclipse-based ARM IDEs by PEmicro. OpenOCD open source GDB server supports a variety of JTAG probes OpenOCD Eclipse plug-in available in GNU ARM Eclipse Plug-ins. AK-OPENJTAG by Artekit (Open JTAG-compatible). AK-LINK by Artekit. PEEDI by RONETIX Debug Probe by Raspberry Pi. RLink by Raisonance. ST-LINK/V2 by STMicroelectronics The ST-LINK/V2 debugger embedded on STM32 Nucleo and Discovery development boards can be converted to SEGGER J-LINK protocol. TRACE32 Debugger and ETM/ITM Trace by Lauterbach. ULINK by Keil. Debugging tools and/or debugging plug-ins (in alphabetical order): Memfault Error Analysis for post mortem debugging Percepio Tracealyzer, RTOS trace visualizer (with Eclipse plugin). Segger SystemView, RTOS trace visualizer. == Real-time operating systems == Commonly referred to as RTOS: == C/C++ software libraries == The following are free C/C++ libraries: ARM Cortex libraries: Cortex Microcontroller Software Interface Standard (CMSIS) libopencm3 (formerly called libopenstm32) libmaple for STM32F1 chips LPCOpen for NXP LPC chips Alternate C standard libraries: Bionic libc, dietlibc, EGLIBC, glibc, klibc, musl, Newlib, uClibc FAT file system libraries: EFSL, FatFs, Petit FatFs Fixed-point math libraries: libfixmath, fixedptc, FPMLib Encryption libraries: Comparison of TLS implementations wolfSSL == Non-C/C++ computer languages and software libraries ==
Read more →
EyeOS

eyeOS was a web desktop for cloud computing, whose main purpose is to enable collaboration and communication among users. It is mainly written in PHP, XML, and JavaScript. It is a private-cloud application platform with a web-based desktop interface. eyeOS delivers a whole desktop from the cloud with file management, personal management information tools, and collaborative tools, with the integration of the client's applications. == History == The first publicly available eyeOS version was released on August 1, 2005, as eyeOS 0.6.0 in Olesa de Montserrat, Barcelona (Spain). A worldwide community of developers soon took part in the project and helped improve it by translating, testing, and developing it. After two years of development, the eyeOS Team published eyeOS 1.0 on June 4, 2007. Compared with previous versions, eyeOS 1.0 introduced a complete reorganization of the code and some new web technologies, like eyeSoft, a portage-based web software installation system. Moreover, eyeOS also included the eyeOS Toolkit, a set of libraries allowing easy and fast development of new web applications. With the release of eyeOS 1.1 on July 2, 2007, eyeOS changed its license and migrated from GNU GPL Version 2 to Version 3. Version 1.2 was released just a month after the 1.1 version and integrated full compatibility with Microsoft Word files. eyeOS 1.5 Gala was released on January 15, 2008. This version was the first to support both Microsoft Office and OpenOffice.org file formats for documents, presentations, and spreadsheets. With this version, eyeOS also gained the ability to import and export documents in both formats using server-side scripting. eyeOS 1.6 was released on April 25, 2008, and included many improvements such as synchronization with local computers, drag and drop, a mobile version, and more. eyeOS 1.8 Lars was released on January 7, 2009, and featured a completely rewritten file manager and a new sound API to develop media-rich applications. Later, on April 1, 2009, 1.8.5 was released with a new default theme and some rewritten apps, such as the Word Processor and the Address Book. On July 13, 2009, 1.8.6 was released with an interface for the iPhone and a new version of eyeMail with support for POP3 and IMAP. eyeOS 1.9 was released on December 29, 2009. It was followed up with the 1.9.0.1 release with minor fixes on February 18, 2010. These releases were the last of the "classic desktop" interfaces. A major re-work was completed in March 2010, now called eyeOS 2.x. However, a small group of eyeOS developers still maintain the code within the eyeOS forum, where support is provided, but the eyeOS group itself has stopped active 1.x development. It is now available as the On-eye project on GitHub. Active development was halted on 1.x as of February 3, 2010. eyeOS 2.0 release took place on March 3, 2010. This was a total restructure of the operating system. The 2.x stable is the new series of eyeOS, which is in active development and will replace 1.x as stable in a few months. It includes live collaboration and more social capabilities than eyeOS 1.x. eyeOS then released 2.2.0.0 on July 28, 2010. On December 14, 2010, a working group inside the eyeOS open-source development community began the structure development and further upgrade of eyeOS 1.9.x. The group's main goal is to continue the work eyeOS has stopped on 1.9.x. eyeOS released 2.5 on May 17, 2011. This was the last release under an open source license. It is available on SourceForge for download under another project called eyeOS 2.5 Open Source Version. On April 1, 2014, Telefónica announced their acquisition of eyeOS. eyeOS would maintain its headquarters in the Catalonia, Spain, where their staff would continue to work but now as part of Telefónica. After its integration into Telefónica, eyeOS would continue to function as an independent subsidiary under CEO Michel Kisfaludi. == Structure and API == For developers, EyeOS provides the eyeOS Toolkit, a set of libraries and functions to develop applications for eyeOS. Using the integrated Portage-based eyeSoft system, one can create their own repository for eyeOS and distribute applications through it. Each core part of the desktop is its own application, using JavaScript to send server commands as the user interacts. As actions are performed using AJAX (such as launching an application), it sends event information to the server. The server then sends back tasks for the client to do in XML format, such as drawing a widget. On the server, eyeOS uses XML files to store information. This makes it simple for a user to set up on the server, as it requires zero configuration other than the account information for the first user, making it simple to deploy. To avoid bottlenecks that flat files present, each user's information and settings are stored in different files, preventing resource starvation from occurring, though this in turn may create issues in high volume user environments due to host operating system open file descriptor limits. == Professional edition == A Professional Edition of eyeOS was launched on September 15, 2011, as an operating system for businesses. It uses a new version number and was released under version 1.0 instead of continuing with the next version number in the open source project. The Professional Edition retains the web desktop interface used by the open source version while targeting enterprise users. A host of new features designed for enterprises, like file sharing and synchronization (called eyeSync), Active Directory/LDAP connectivity, system-wide administration controls, and a local file execution tool called eyeRun were introduced. A new suite of Web Apps (a mail client, calendar, instant messaging, and collaboration tools) was also introduced, specific to the enterprise edition for the web desktop. With eyeOS Professional Edition 1.1, a to-do task manager tool, Citrix XenApp integration, and a Facebook like 'wall' for collaboration were introduced. == Awards == 2007 – Received the Softpedia's Pick award. 2007 – Finalist at SourceForge's 2007 Community Choice Awards at the "Best Project" category. The winner for that category was 7-Zip. 2007 – Won the Yahoo! Spain Web Revelation award in the Technology category. 2008 – Finalist for the Webware 100 awards by CNET, under the "Browsing" category. 2008 – Finalist at the SourceForge's 2008 Community Choice Awards at the "Most Likely to Change the World" category. The winner for that category was Linux. 2009 – Selected Project of the Month (August 2009) by SourceForge. 2009 – BMW Innovation Award. 2010 – Winner of Accelera (Ernst & Young). 2010 – Asturias & Girona Spanish Prince award “IMPULSA”. 2011 – Winner of MIT's TR35 award as Innovator of the Year in Spain. == Community == eyeOS community is formed with the eyeOS forums, which reached 10,000 members on April 4, 2008; the eyeOS wiki; and the eyeOS Application Communities, available at the eyeOS-Apps website, hosted and provided by openDesktop.org as well as Softpedia.
Read more →
Human–robot interaction

Human–robot interaction (HRI) is the study of interactions between humans and robots. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language processing, design, psychology and philosophy. A subfield known as physical human–robot interaction (pHRI) has tended to focus on device design to enable people to safely interact with robotic systems. == Origins == Human–robot interaction has been a topic of both science fiction and academic speculation even before any robots existed. Because much of active HRI development depends on natural language processing, many aspects of HRI are continuations of human communications, a field of research which is much older than robotics. The origin of HRI as a discrete problem was stated by 20th-century author Isaac Asimov in 1941, in his novel I, Robot. Asimov coined Three Laws of Robotics, namely: A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. These three laws provide an overview of the goals engineers and researchers hold for safety in the HRI field, although the fields of robot ethics and machine ethics are more complex than these three principles. However, generally human–robot interaction prioritizes the safety of humans that interact with potentially dangerous robotics equipment. Solutions to this problem range from the philosophical approach of treating robots as ethical agents (individuals with moral agency), to the practical approach of creating safety zones. These safety zones use technologies such as lidar to detect human presence or physical barriers to protect humans by preventing any contact between machine and operator. Although initially robots in the human–robot interaction field required some human intervention to function, research has expanded this to the extent that fully autonomous systems are now far more common than in the early 2000s. Autonomous systems include from simultaneous localization and mapping systems which provide intelligent robot movement to natural-language processing and natural-language generation systems which allow for natural, human-esque interaction which meet well-defined psychological benchmarks. Anthropomorphic robots (machines which imitate human body structure) are better described by the biomimetics field, but overlap with HRI in many research applications. Examples of robots which demonstrate this trend include Willow Garage's PR2 robot, the NASA Robonaut, and Honda ASIMO. However, robots in the human–robot interaction field are not limited to human-like robots: Paro and Kismet are both robots designed to elicit emotional response from humans, and so fall into the category of human–robot interaction. Goals in HRI range from industrial manufacturing through Cobots, medical technology through rehabilitation, autism intervention, and elder care devices, entertainment, human augmentation, and human convenience. Future research therefore covers a wide range of fields, much of which focuses on assistive robotics, robot-assisted search-and-rescue, and space exploration. == The goal of friendly human–robot interactions == Robots are artificial agents with capacities of perception and action in the physical world often referred by researchers as workspace. Their use has been generalized in factories but nowadays they tend to be found in the most technologically advanced societies in such critical domains as search and rescue, military battle, mine and bomb detection, scientific exploration, law enforcement, entertainment and hospital care. These new domains of applications imply a closer interaction with the user, sharing the workspace but also goals in terms of task achievement. The subfield of physical human–robot interaction (pHRI) has largely focused on device design to enable people to safely interact with robotic systems but is increasingly developing algorithmic approaches in an attempt to support fluent and expressive interactions between humans and robotic systems. With the advance in AI, the research is focusing on one part towards the safest physical interaction but also on a socially correct interaction, dependent on cultural criteria. The goal is to build an intuitive, and easy communication with the robot through speech, gestures, and facial expressions. Kerstin Dautenhahn refers to friendly Human–robot interaction as "Robotiquette" defining it as the "social rules for robot behaviour (a 'robotiquette') that is comfortable and acceptable to humans" The robot has to adapt itself to our way of expressing desires and orders and not the contrary. But every day environments such as homes have much more complex social rules than those implied by factories or even military environments. Thus, the robot needs perceiving and understanding capacities to build dynamic models of its surroundings. It needs to categorize objects, recognize and locate humans and further recognize their emotions. The need for dynamic capacities pushes forward every sub-field of robotics. Furthermore, by understanding and perceiving social cues, robots can enable collaborative scenarios with humans. For example, with the rapid rise of personal fabrication machines such as desktop 3D printers, laser cutters, etc., entering our homes, scenarios may arise where robots can collaboratively share control, co-ordinate and achieve tasks together. Industrial robots have already been integrated into industrial assembly lines and are collaboratively working with humans. The social impact of such robots have been studied and has indicated that workers still treat robots and social entities, rely on social cues to understand and work together. On the other end of HRI research the cognitive modelling of the "relationship" between human and the robots benefits the psychologists and robotic researchers the user study are often of interests on both sides. This research endeavours part of human society. For effective human – humanoid robot interaction numerous communication skills and related features should be implemented in the design of such artificial agents/systems. == General HRI research == HRI research spans a wide range of fields, some general to the nature of HRI. === Methods for perceiving humans === Methods for perceiving humans in the environment are based on sensor information. Research on sensing components and software led by Microsoft provide useful results for extracting the human kinematics (see Kinect). An example of older technique is to use colour information for example the fact that for light skinned people the hands are lighter than the clothes worn. In any case a human modelled a priori can then be fitted to the sensor data. The robot builds or has (depending on the level of autonomy the robot has) a 3D mapping of its surroundings to which is assigned the humans locations. Most methods intend to build a 3D model through vision of the environment. The proprioception sensors permit the robot to have information over its own state. This information is relative to a reference. Theories of proxemics may be used to perceive and plan around a person's personal space. A speech recognition system is used to interpret human desires or commands. By combining the information inferred by proprioception, sensor and speech the human position and state (standing, seated). In this matter, natural-language processing is concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural-language data. For instance, neural-network architectures and learning algorithms that can be applied to various natural-language processing tasks including part-of-speech tagging, chunking, named-entity recognition, and semantic role labeling. === Methods for motion planning === Motion planning in dynamic environments is a challenge that can at the moment only be achieved for robots with 3 to 10 degrees of freedom. Humanoid robots or even 2 armed robots, which can have up to 40 degrees of freedom, are unsuited for dynamic environments with today's technology. However lower-dimensional robots can use the potential field method to compute trajectories which avoid collisions with humans. === Cognitive models and theory of mind === Humans exhibit negative social and emotional responses as well as decreased trust toward some robots that closely, but imperfectly, resemble humans; this phenomenon has been termed the "Uncanny Valley". However recent research in telepresence robots has established that mimicking human body postures and expressive gestures has made the robots likeable and engaging in a remote setting. Further, the presence o
Read more →
Traceability

Traceability is the capability to trace something. In some cases, it is interpreted as the ability to verify the history, location, or application of an item by means of documented recorded identification. Other common definitions include the capability (and implementation) of keeping track of a given set or type of information to a given degree, or the ability to chronologically interrelate uniquely identifiable entities in a way that is verifiable. Traceability is applicable to measurement, supply chain, software development, healthcare and security. == Measurement == The term measurement traceability or metrological traceability is used to refer to an unbroken chain of comparisons relating an instrument's measurements to a known standard. Calibration to a traceable standard can be used to determine an instrument's bias, precision, and accuracy. It may also be used to show a chain of custody—from current interpretation of evidence to the actual evidence in a legal context, or history of handling of any information. In many countries, national standards for weights and measures are maintained by a National Metrological Institute (NMI) which provides the highest level of standards for the calibration / measurement traceability infrastructure in that country. Examples of government agencies include the National Physical Laboratory, UK (NPL) the National Institute of Standards and Technology (NIST) in the USA, the Physikalisch-Technische Bundesanstalt (PTB) in Germany, the Instituto Nazionale di Ricerca Metrologica (INRiM) in Italy, and the National Research Council of Canada (NRC). As defined by NIST, "Traceability of measurement requires the establishment of an unbroken chain of comparisons to stated references each with a stated uncertainty." A clock providing traceable time is traceable to a time standard such as Coordinated Universal Time or International Atomic Time. The Global Positioning System is a source of traceable time. === Food processing === In food processing (meat processing, fresh produce processing), the term traceability refers to the recording through means of barcodes or RFID tags and other tracking media, all movement of product and steps within the production process. One of the key reasons this is such a critical point is in instances where an issue of contamination arises, and a recall is required. Where traceability has been closely adhered to, it is possible to identify, by precise date/time and exact location which goods must be recalled, and which are safe, potentially saving millions of dollars in the recall process. Traceability within the food processing industry is also utilised to identify key high production and quality areas of a business, versus those of low return, and where points in the production process may be improved. In food processing software, traceability systems imply the use of a unique piece of data (e.g., order date/time or a serialized sequence number, generally through the use of a barcode / RFID) which can be traced through the entire production flow, linking all sections of the business, including suppliers and future sales through the supply chain. Messages and files at any point in the system can then be audited for correctness and completeness, using the traceability software to find the particular transaction and/or product within the supply chain. In food systems, ISO 22005, as part of the ISO 22000 family of standards, has been developed to define the principles for food traceability and specifies the basic requirements for the design and implementation of a feed and food traceability system. It can be applied by an organization operating at any step in the feed and food chain. The European Union's General Food Law came into force in 2002, making traceability compulsory for food and feed operators and requiring those businesses to implement traceability systems. The EU introduced its Trade Control and Expert System, or TRACES, in April 2004. The system provides a central database to track movement of animals within the EU and from third countries. Australia has its National Livestock Identification System to keep track of livestock from birth to slaughterhouse. India has started taking initiatives for setting up traceability systems at Government and Corporate levels. Grapenet, an initiative by Agriculture and Processed Food Products Export Development Authority (APEDA), Ministry of Commerce, Government of India is an example in this direction. GrapeNet is an internet based traceability software system for monitoring fresh grapes exported from India to the European Union. GrapeNet is a first of its kind initiative in India that has put in place an end-to-end system for monitoring pesticide residue, achieve product standardization and facilitate tracing back from pallets to the farm of the Indian grower, through the various stages of sampling, testing, certification and packing. Grapenet won the National Award (Gold), in the winners announced for the best e-Governance initiatives undertaken in India in 2007. The Directorate Generate Foreign Trade (DGFT), Government of India, through its notification dated 04.02.2009 relating to Amendment in Foreign Trade Policy (RE2008)has mandated that Export to the European Union is permitted subject to registration with APEDA, thereby making Grapenet mandatory for all exports of fresh grapes from India to Europe. Uruguay has also designed a system called "Traceability & Electronic Information System of the Beef Industry". Traceability in food supply can also refer to practices employed by individual companies, including Ritual and Amway's Nutrilite. In the case of Nutrilite's supplements, ingredients are documented and tested throughout farming, processing, and manufacturing to ensure traceability at each stage of production. == Systems and software development == In systems and software development, the term traceability (or requirements traceability) refers to the ability to link product requirements back to stakeholders' rationales and forward to corresponding design artifacts, code, and test cases. Traceability supports numerous software engineering activities such as change impact analysis, compliance verification or traceback of code, regression test selection, and requirements validation. It is usually accomplished in the form of a matrix created for the verification and validation of the project. Unfortunately, the practice of constructing and maintaining a requirements trace matrix (RTM) can be very arduous and over time the traces tend to erode into an inaccurate state unless date/time stamped. Alternate automated approaches for generating traces using information retrieval methods have been developed. The IEEE defines traceability as "(1)The degree to which a relationship can be established between two or more products of the development process, especially products having a predecessor, successor or master-subordinate relationship to one another. For example, the degree to which the requirements and design of a given software component match. See also: consistency. " and "(2) The degree to which each element in a software development product establishes its reason for existing; for example, the degree to which each element in a bubble chart references the requirement that it satisfies." In transaction processing software, traceability implies use of a unique piece of data (e.g., order date/time or a serialized sequence number) which can be traced through the entire software flow of all relevant application programs. Messages and files at any point in the system can then be audited for correctness and completeness, using the traceability key to find the particular transaction. This is also sometimes referred to as the transaction footprint. == Health care == Patient safety during healthcare service plays an important role in preventing delayed recovery or even mortality, by increasing and improving the quality of life of citizens, and is considered an indicator of the quality status of health services Maintaining patient safety is a complex task and involves factors inherent to the environment and human actions. New technologies facilitate the traceability tools of patients and medications. This is particularly relevant for drugs that are considered high risk and cost. Recent research in the healthcare industry emphasizes the significant impact of Blockchain Technology (BCT) on improving the performance of healthcare supply chain management. It highlights BCT's role in enhancing transparency, data immutability, and efficient management, leading to better cooperation among stakeholders and effective risk mitigation in healthcare services. The World Health Organization has recognized the importance of traceability for medical products of human origin (MPHO) and urged member states "to encourage the implementation of globally consistent coding systems to facilitate national and international traceability". == Security and cri
Read more →
OpenIO

OpenIO offered object storage for a wide range of high-performance applications. OpenIO was founded in 2015 by Laurent Denel (CEO), Jean-François Smigielski (CTO) and five other co-founders; it leveraged open source software, developed since 2006, based on a grid technology that enabled dynamic behaviour and supported heterogenous hardware. In October 2017 OpenIO was completed a $5 million funding rounds. In July 2020 OpenIO had been acquired by OVH and withdrawn from the market to become the core technology of OVHcloud object storage offering. == Software == OpenIO is a software-defined object store that supports S3 and can be deployed on-premises, cloud-hosted or at the edge, on any hardware mix. It has been designed from the beginning for performance and cost-efficiency at any scale, and it has been optimized for Big Data, HPC and AI. OpenIO stores objects within a flat structure within a massively distributed directory with indirections, which allows the data query path to be independent of the number of nodes and the performance not to be affected by the growth of capacity. Servers are organized as a grid of nodes massively distributed, where each node takes part in directory and storage services, which ensures that there is no single point of failure and that new nodes are automatically discovered and immediately available without the need to rebalance data. The software is built on top of a technology that ensures optimal data placement based on real-time metrics and allows the addition or removal of storage devices with automatic performance and load impact optimization. For data protection OpenIO has synchronous and asynchronous replication with multiple copies, and an erasure coding implementation based on Reed-Solomon that can be deployed in one data center or geo-distributed or stretched clusters. The software has a feature that catches all events that occur in the cluster and can pass them up in the stack or to applications running on OpenIO nodes. This enables event-driven computing directly into the storage infrastructure. The open source code is available on Github and it is licensed under AGPL3 for server code and LGPL3 for client code. == Performance == OpenIO claimed in 2019 to have reached 1.372 Tbit/s write speed (171 GB/s) on a cluster of 350 physical machines. The benchmark scenario, conducted under production conditions with standard hardware (commodity servers with 7200 rpm HDDs), consisted in backing up a 38 PB Hadoop datalake via the DistCp command. This level of performance marked, according to analysts, the arrival of a new generation of object storage technologies oriented toward high performance and hyper-scalability.
Read more →
JDoodle

JDoodle is a cloud-based online integrated development environment and compiler platform that supports execution of source code in 70+ programming languages including Java, Python, C/C++, PHP, Ruby, Perl, HTML, and more. It provides zero‑setup code for compilation, execution, and sharing via a web browser interface. == Features == Provides real‑time collaboration and code embedding via shareable URLs and APIs Offers an integrated terminal interface supporting database engines such as MySQL and MongoDB. JDroid — AI‑assistant to generate code snippets, optimize code, and assist debugging. == Languages and frameworks supported ==
Read more →
CLAWS (linguistics)

The Constituent Likelihood Automatic Word-tagging System (CLAWS) is a program that performs part-of-speech tagging. It was developed in the 1980s at Lancaster University by the University Centre for Computer Corpus Research on Language. It has an overall accuracy rate of 96–97% with the latest version (CLAWS4) tagging around 100 million words of the British National Corpus. == History == A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS tags like 'noun-plural'. Developed in the early 1980s, CLAWS was built to fill the ever-growing gap created by always-changing POS necessities. Originally created to add part-of-speech tags to the LOB corpus of British English, the CLAWS tagset has since been adapted to other languages as well, including Urdu and Arabic. Since its inception, CLAWS has been hailed for its functionality and adaptability. Still, it is not without flaws, and though it boasts an error-rate of only 1.5% when judged in major categories, CLAWS still remains with c.3.3% ambiguities unresolved. Ambiguity arises in cases such as with the word flies, and whether it should be classified as a noun or a verb. It's these ambiguities that will require the various upgrades and tagsets that CLAWS will endure. == Rules and processing == CLAWS uses a Hidden Markov model to determine the likelihood of sequences of words in anticipating each part-of-speech label. === Sample output === This excerpt from Bram Stoker's Dracula (1897) has been tagged using both the CLAWS C5 and C7 tagsets. This is what a CLAWS output will generally look like, with the most likely part-of-speech tag following each word. == Tagsets == === CLAWS1 tagset === The first tagset developed in CLAWS, CLAWS1 tagset, has 132 word tags. In terms of form and application, C1 tagset is similar to Brown Corpus tags. See Table of tags in C1 tagset here. === CLAWS2 tagset === From 1983 to 1986, updated versions leading to CLAWS2 were part of a larger attempt to deal with aspects such as recognizing sentence breaks, in order to avoid the need for manual pre-processing of a text before the tags were applied, moving instead to optional manual post-editing to adjust the output of the automatic annotation, if needed. The CLAWS2 tagset has 166 word tags. See Table of tags in C2 tagset here. === CLAWS4 tagset === The CLAWS4 was used for the 100-million-word British National Corpus (BNC). A general-purpose grammatical tagger, it is a successor of the CLAWS1 tagger. In tagging the BNC, the many rounds of work that went into CLAWS4 focused on making the CLAWS program independent from the tagsets. For example, the BNC project used two tagset versions: "a main tagset (C5) with 62 tags with which the whole of the corpus has been tagged, and a larger (C7) tagset with 152 tags, which has been used to make a selected 'core' sample corpus of two million words." The latest version of CLAWS4 is offered by UCREL, a research center of Lancaster University. === CLAWS5 tagset === The CLAWS5 tagset, which was used for BNC, has over 60 tags. See Table of tags in C5 tagset here. === CLAWS6 tagset === The CLAWS6 tagset was used for the BNC sampler corpus and the COLT corpus. It has over 160 tags, including 13 determiner subtypes. See Table of tags in C6 tagset here. === CLAWS7 tagset === The standard CLAWS7 tagset is used currently. It is only different in the punctuation tags when compared to the CLAWS6 tagset. See Table of tags in C7 tagset here. === CLAWS8 tagset === CLAWS8 tagset was extended from C7 tagset with further distinctions in the determiner and pronoun categories, as well as 37 new auxiliary tags for forms of be, do, and have. See Table of tags in C8 tagset here
Read more →
Wink Bingo

Wink Bingo is an online bingo website launched in 2008. It is part of Broadway Gaming Ireland DF Limited and is based and licensed in Ireland. == History == Wink Bingo launched in 2008 and under chief executive Eitan Boyd it grew to 60,000 active players within two years. It had an estimated £1.3 million profit in the first 11 months of trading, and by 2009 it had estimated annual revenue of £15 million. In 2009 Wink Bingo was purchased by 888 Holdings Plc, which operates a number of entertainment brands including 888casino, 888poker and 888sport. The initial up front fee was reported in the London Evening Standard to be £11 million, rising as high as £59.7 million depending on performance-based earn out arrangements. The acquisition included Daub Ltd’s other online bingo businesses Posh Bingo and Bingo Fabulous. In 2011, the sellers agreed to amend the terms and accept two subsequent payments in addition to the initial cost, of £9.2 million in May and £6.1 million in August. In 2011 Wink Bingo sponsored ITV2's The Only Way Is Essex, and other notable advertising campaigns have included sponsorship of Harry Hill's TV Burp. In 2014, Wink Bingo rebranded with an updated slogan 'Wink if you're in!', with an aim of creating a 'sunny, calm and inclusive' online destination, and an accompanying TV commercial featuring the Ottawan song D.I.S.C.O. re-recorded as B.I.N.G.O.. Wink also launched a new digital magazine, 'Winkly', and 'Winkipedia, a bingo encyclopedia'. Wink Bingo is available on desktop and as a mobile app. Wink launched Wink Slots in 2016 as a companion site to Wink Bingo. The Advertising Standards Authority has ruled on Wink Bingo's advertisements on a number of occasions. In August 2008, Wink ran a television ad which showed a midwife celebrating while at work at a hospital maternity unit. The ASA banned the ad, concluding that it condoned gambling in the workplace and suggested that it took priority over professional commitments. In June 2013, the Gambling Reform & Society Perception Group (GRASP) challenged the use of semi-naked "athletic" men together with the claim "Go on ... you know you want to" on an outdoor ad, suggesting it linked gambling to seduction and enhanced attractiveness. The complaint was not upheld. The site underwent another rebrand and pop art inspired redesign in April 2018, taking on a new tone of voice and a new slogan, "You’ve Earned It". An online shop was added, where players can redeem reward points for free play or vouchers for online high street retailers. In 2021 Wink Bingo was purchased by Saphalata Holdings, a company that forms part of the Broadway Gaming group. === Cancer Research UK campaign === In 2015 Wink Bingo began an open-ended partnership with the Peter Andre Fund to raise money for Cancer Research UK. Peter Andre also met with players who were selected in a raffle. == Awards ==
Read more →
RCUDA

rCUDA, which stands for Remote CUDA, is a type of middleware software framework for remote GPU virtualization. Fully compatible with the CUDA application programming interface (API), it allows the allocation of one or more CUDA-enabled GPUs to a single application. Each GPU can be part of a cluster or running inside of a virtual machine. The approach is aimed at improving performance in GPU clusters that are lacking full utilization. GPU virtualization reduces the number of GPUs needed in a cluster, and in turn, leads to a lower cost configuration – less energy, acquisition, and maintenance. The recommended distributed acceleration architecture is a high performance computing cluster with GPUs attached to only a few of the cluster nodes. When a node without a local GPU executes an application needing GPU resources, remote execution of the kernel is supported by data and code transfers between local system memory and remote GPU memory. rCUDA is designed to accommodate this client-server architecture. On one end, clients employ a library of wrappers to the high-level CUDA Runtime API, and on the other end, there is a network listening service that receives requests on a TCP port. Several nodes running different GPU-accelerated applications can concurrently make use of the whole set of accelerators installed in the cluster. The client forwards the request to one of the servers, which accesses the GPU installed in that computer and executes the request in it. Time-multiplexing the GPU, or in other words sharing it, is accomplished by spawning different server processes for each remote GPU execution request. == rCUDA v20.07 == The rCUDA middleware enables the concurrent usage of CUDA-compatible devices remotely. rCUDA employs either the InfiniBand network or the socket API for the communication between clients and servers. rCUDA can be useful in three different environments: Clusters. To reduce the number of GPUs installed in High Performance Clusters. This leads to energy savings, as well as other related savings like acquisition costs, maintenance, space, cooling, etc. Academia. In commodity networks, to offer access to a few high performance GPUs concurrently to many students. Virtual Machines. To enable the access to the CUDA facilities on the physical machine. The current version of rCUDA (v20.07) supports CUDA version 9.0, excluding graphics interoperability. rCUDA v20.07 targets the Linux OS (for 64-bit architectures) on both client and server sides. CUDA applications do not need any change in their source code in order to be executed with rCUDA.
Read more →