Der Trend zu hochskalierenden Cloud-Anwendungen, die stark auf datengetriebene Features setzen, ist ungebrochen. Dadurch laufen immer mehr Anwendungen nur noch unter Eventual Consistency. Nebenläufige Änderungsoperationen auf inkonsistenten, replizierten Datenbeständen können allerdings zu schweren Replikations-Anomalien wie Lost Updates führen. Das Implementieren korrekter Merge-Logik im Fall von Schreibkonflikten ist eine große Fehlerquelle und selbst für sehr erfahrene Software-Architekt:innen eine Herausforderung. Basierend auf unseren Erfahrungen aus verschiedenen Kunden- und Forschungsprojekten entwickeln wir Architektur-Empfehlungen und Entwurfsmuster für das Design von Anwendungen, die unter Eventual Consistency laufen. Parallel treiben wir die Entwicklung eines Open-Source-Replikations-Frameworks, welches unsere Methoden unterstützt, voran. Der Vortrag gibt konkrete Hilfestellungen für Architekt:innen und beinhaltet eine Demo-Session.
OOP 2021 - Eventual Consistency - Du musst keine Angst habenSusanne Braun
Der Trend zu hochskalierenden Cloud-Anwendungen, die auf datengetriebene Features setzen, ist ungebrochen und immer mehr Anwendungen laufen nur noch unter Eventual Consistency. Nebenläufige Änderungen auf inkonsistenten Daten können zu Replikations-Anomalien wie Lost Updates führen, deren Behandlung selbst für erfahrene Software-Architekt:innen eine Herausforderung darstellt. Der Vortrag vereint die neuesten Forschungsergebnisse und Lessons Learned aus mehreren Case Studies mit konkreten Entwurfsmustern für Architekt:innen.
Eventual Consistency – Du musst keine Angst habenSusanne Braun
Slides of mit Talk at the Software Architecture Alliance Conference: https://www.software-architecture-alliance.de/software-architecture-alliance-2021.html
Distributed data-intensive systems are increasingly designed to be only eventually consistent. Persistent data is no longer processed with serialized and transactional access, exposing applications to a range of potential consistency and concurrency anomalies that need to be handled by the application itself. Controlling concurrent data access in monolithic systems is already challenging, but the problem is exacerbated in distributed systems. To make it worse, only little systematic engineering guidance is provided by the software architecture community regarding this issue.
Susanne shares her experiences from different case studies with industry clients, and novel design guidelines developed by using action research. You will learn settled and novel approaches to tackle consistency- and concurrency related design challenges.
Tackling Consistency-related Design Challenges of Distributed Data-Intensive ...Susanne Braun
In the presentation we share the results of an action research study. In this study we set off to investigate how consistency-related design challenges of distributed, data-intensive systems can be tackled.
OOP 2021 - Eventual Consistency - Du musst keine Angst habenSusanne Braun
Der Trend zu hochskalierenden Cloud-Anwendungen, die auf datengetriebene Features setzen, ist ungebrochen und immer mehr Anwendungen laufen nur noch unter Eventual Consistency. Nebenläufige Änderungen auf inkonsistenten Daten können zu Replikations-Anomalien wie Lost Updates führen, deren Behandlung selbst für erfahrene Software-Architekt:innen eine Herausforderung darstellt. Der Vortrag vereint die neuesten Forschungsergebnisse und Lessons Learned aus mehreren Case Studies mit konkreten Entwurfsmustern für Architekt:innen.
Eventual Consistency – Du musst keine Angst habenSusanne Braun
Slides of mit Talk at the Software Architecture Alliance Conference: https://www.software-architecture-alliance.de/software-architecture-alliance-2021.html
Distributed data-intensive systems are increasingly designed to be only eventually consistent. Persistent data is no longer processed with serialized and transactional access, exposing applications to a range of potential consistency and concurrency anomalies that need to be handled by the application itself. Controlling concurrent data access in monolithic systems is already challenging, but the problem is exacerbated in distributed systems. To make it worse, only little systematic engineering guidance is provided by the software architecture community regarding this issue.
Susanne shares her experiences from different case studies with industry clients, and novel design guidelines developed by using action research. You will learn settled and novel approaches to tackle consistency- and concurrency related design challenges.
Tackling Consistency-related Design Challenges of Distributed Data-Intensive ...Susanne Braun
In the presentation we share the results of an action research study. In this study we set off to investigate how consistency-related design challenges of distributed, data-intensive systems can be tackled.
PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHMecij
The running generation of world, cloud computing has become the most powerful, chief and also lightning technology. IT based companies has already changed their way to buy and design hardware through this technology. It is a high utility which can also make software more attractive. Load balancing research in
cloud technology is one of the burning technologies in modern time. In this paper, pointing various proposed algorithms, the topic of load balancing in Cloud Computing are researched and compared to provide a gist of the latest way in this research area. By using Genetic Algorithm the balance is most
flexible which is represented here.
Comparing reinforcement learning and access points with rowelijcseit
Due to the fast development of the Cloud Computing technologies, the rapid increase of cloud services
are became very remarkable. The fact of integration of these services with many of the modern
enterprises cannot be ignored. Microsoft, Google, Amazon, SalesForce.com and the other leading IT
companies are entered the field of developing these services. This paper presents a comprehensive survey
of current cloud services, which are divided into eleven categories. Also the most famous providers for
these services are listed. Finally, the Deployment Models of Cloud Computing are mentioned and briefly
discussed.
Der Trend zu hochskalierenden Cloud-Anwendungen, die stark auf datengetriebene Features setzen, ist ungebrochen. Dadurch laufen immer mehr Anwendungen nur noch unter Eventual Consistency. Nebenläufige Änderungsoperationen auf inkonsistenten, replizierten Datenbeständen können allerdings zu schweren Replikationsanomalien wie Lost Updates führen. Das Implementieren korrekter Merge-Logik im Fall von Schreibkonflikten ist eine große Fehlerquelle und selbst für sehr erfahrene Software-Architekt:innen eine Herausforderung. Basierend auf unseren Erfahrungen aus verschiedenen Kunden- und Forschungsprojekten entwickeln wir Architekturempfehlungen und Entwurfsmuster für das Design von Anwendungen, die unter Eventual Consistency laufen. Parallel treiben wir die Entwicklung eines Open-Source-Replikations-Frameworks voran, das unsere Methoden unterstützt. Der Vortrag gibt konkrete Hilfestellungen für Architekt:innen.
Building and deploying LLM applications with Apache AirflowKaxil Naik
Behind the growing interest in Generate AI and LLM-based enterprise applications lies an expanded set of requirements for data integrations and ML orchestration. Enterprises want to use proprietary data to power LLM-based applications that create new business value, but they face challenges in moving beyond experimentation. The pipelines that power these models need to run reliably at scale, bringing together data from many sources and reacting continuously to changing conditions.
This talk focuses on the design patterns for using Apache Airflow to support LLM applications created using private enterprise data. We’ll go through a real-world example of what this looks like, as well as a proposal to improve Airflow and to add additional Airflow Providers to make it easier to interact with LLMs such as the ones from OpenAI (such as GPT4) and the ones on HuggingFace, while working with both structured and unstructured data.
In short, this shows how these Airflow patterns enable reliable, traceable, and scalable LLM applications within the enterprise.
https://airflowsummit.org/sessions/2023/keynote-llm/
Integration Patterns for Big Data ApplicationsMichael Häusler
Big Data technologies like distributed databases, queues, batch processors, and stream processors are fun and exciting to play with. Making them play nicely together can be challenging. Keeping it fun for engineers to continuously improve and operate them is hard. At ResearchGate, we run thousands of YARN applications every day to gain insights and to power user facing features. Of course, there are numerous integration challenges on the way:
* integrating batch and stream processors with operational systems
* ingesting data and playing back results while controlling performance crosstalk
* rolling out new versions of synchronous, stream, and batch applications and their respective data schemas
* controlling the amount of glue and adapter code between different technologies
* modeling cross-flow dependencies while handling failures gracefully and limiting their repercussions
We describe our ongoing journey in identifying patterns and principles to make our big data stack integrate well. Technologies to be covered will include MongoDB, Kafka, Hadoop (YARN), Hive (TEZ), Flink Batch, and Flink Streaming.
Keeping CALM – Konsistenz in verteilten Systemen leichtgemachtSusanne Braun
10 Jahre nach CAP wurde das CALM-Theorem formuliert, und beweist das, was wir immer alle geahnt haben: Im Falle von Netzwerkpartitionen sind Konsistenz UND Verfügbarkeit unter bestimmten Bedingungen möglich! Im Vortrag gehen wir auf eine Journey vom CAP-Theorem hin zum CALM-Theorem. Ich räume mit gängigen Mythen auf und zeige euch, wie ihr das CALM-Theorem praktisch anwenden könnt. Der Vortrag vereint neueste Forschungsergebnisse und Lessons Learned aus mehreren Studien mit konkreten Entwurfsmustern und Code-Beispielen für Architekt:innen.
Keynote VariVolution/VM4ModernTech@SPLC 2022
At compile-time or at runtime, varying software is a powerful means to achieve optimal functional and performance goals. An observation is that only considering the software layer might be naive to tune the performance of the system or test that the functionality behaves correctly. In fact, many layers (hardware, operating system, input data, build process etc.), themselves subject to variability, can alter performances of software configurations. For instance, configurations' options may have very different effects on execution time or energy consumption when used with different input data, depending on the way it has been compiled and the hardware on which it is executed.
In this talk, I will introduce the concept of “deep software variability” which refers to the interactions of all external layers modifying the behavior or non-functional properties of a software system. I will show how compile-time options, inputs, and software evolution (versions), some dimensions of deep variability, can question the generalization of the variability knowledge of popular configurable systems like Linux, gcc, xz, or x264.
I will then argue that machine learning (ML) is particularly suited to manage very large variants space. The key idea of ML is to build a model based on sample data -- here observations about software variants in variable settings -- in order to make predictions or decisions. I will review state-of-the-art solutions developed in software engineering and software product line engineering while connecting with works in ML (e.g., transfer learning, dimensionality reduction, adversarial learning). Overall, the key challenge is to leverage the right ML pipeline in order to harness all variability layers (and not only the software layer), leading to more efficient systems and variability knowledge that truly generalizes to any usage and context.
From this perspective, we are starting an initiative to collect data, software, reusable artefacts, and body of knowledge related to (deep) software variability: https://deep.variability.io
Finally, I will open a broader discussion on how machine learning and deep software variability relate to the reproducibility, replicability, and robustness of scientific, software-based studies (e.g., in neuroimaging and climate modelling).
Swift Parallel Scripting for High-Performance WorkflowDaniel S. Katz
The Swift scripting language was created to provide a simple, compact way to write parallel scripts that run many copies of ordinary programs concurrently in various workflow patterns, reducing the need for complex parallel programming or arcane scripting to achieve this common high-level task. The result was a highly portable programming model based on implicitly parallel functional dataflow. The same Swift script runs on multi-core computers, clusters, grids, clouds, and supercomputers, and is thus a useful tool for moving workflow computations from laptop to distributed and/or high performance systems.
Swift has proven to be very general, and is in use in domains ranging from earth systems to bioinformatics to molecular modeling. It’s more recently been adapted to serve as a programming model for much finer-grain in-memory workflow on extreme scale systems, where it can perform task rates in the millions to billion-per-second.
In this talk, we describe the state of Swift’s implementation, present several Swift applications, and discuss ideas for of the future evolution of the programming model on which it’s based.
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHMecij
The running generation of world, cloud computing has become the most powerful, chief and also lightning technology. IT based companies has already changed their way to buy and design hardware through this technology. It is a high utility which can also make software more attractive. Load balancing research in
cloud technology is one of the burning technologies in modern time. In this paper, pointing various proposed algorithms, the topic of load balancing in Cloud Computing are researched and compared to provide a gist of the latest way in this research area. By using Genetic Algorithm the balance is most
flexible which is represented here.
Comparing reinforcement learning and access points with rowelijcseit
Due to the fast development of the Cloud Computing technologies, the rapid increase of cloud services
are became very remarkable. The fact of integration of these services with many of the modern
enterprises cannot be ignored. Microsoft, Google, Amazon, SalesForce.com and the other leading IT
companies are entered the field of developing these services. This paper presents a comprehensive survey
of current cloud services, which are divided into eleven categories. Also the most famous providers for
these services are listed. Finally, the Deployment Models of Cloud Computing are mentioned and briefly
discussed.
Der Trend zu hochskalierenden Cloud-Anwendungen, die stark auf datengetriebene Features setzen, ist ungebrochen. Dadurch laufen immer mehr Anwendungen nur noch unter Eventual Consistency. Nebenläufige Änderungsoperationen auf inkonsistenten, replizierten Datenbeständen können allerdings zu schweren Replikationsanomalien wie Lost Updates führen. Das Implementieren korrekter Merge-Logik im Fall von Schreibkonflikten ist eine große Fehlerquelle und selbst für sehr erfahrene Software-Architekt:innen eine Herausforderung. Basierend auf unseren Erfahrungen aus verschiedenen Kunden- und Forschungsprojekten entwickeln wir Architekturempfehlungen und Entwurfsmuster für das Design von Anwendungen, die unter Eventual Consistency laufen. Parallel treiben wir die Entwicklung eines Open-Source-Replikations-Frameworks voran, das unsere Methoden unterstützt. Der Vortrag gibt konkrete Hilfestellungen für Architekt:innen.
Building and deploying LLM applications with Apache AirflowKaxil Naik
Behind the growing interest in Generate AI and LLM-based enterprise applications lies an expanded set of requirements for data integrations and ML orchestration. Enterprises want to use proprietary data to power LLM-based applications that create new business value, but they face challenges in moving beyond experimentation. The pipelines that power these models need to run reliably at scale, bringing together data from many sources and reacting continuously to changing conditions.
This talk focuses on the design patterns for using Apache Airflow to support LLM applications created using private enterprise data. We’ll go through a real-world example of what this looks like, as well as a proposal to improve Airflow and to add additional Airflow Providers to make it easier to interact with LLMs such as the ones from OpenAI (such as GPT4) and the ones on HuggingFace, while working with both structured and unstructured data.
In short, this shows how these Airflow patterns enable reliable, traceable, and scalable LLM applications within the enterprise.
https://airflowsummit.org/sessions/2023/keynote-llm/
Integration Patterns for Big Data ApplicationsMichael Häusler
Big Data technologies like distributed databases, queues, batch processors, and stream processors are fun and exciting to play with. Making them play nicely together can be challenging. Keeping it fun for engineers to continuously improve and operate them is hard. At ResearchGate, we run thousands of YARN applications every day to gain insights and to power user facing features. Of course, there are numerous integration challenges on the way:
* integrating batch and stream processors with operational systems
* ingesting data and playing back results while controlling performance crosstalk
* rolling out new versions of synchronous, stream, and batch applications and their respective data schemas
* controlling the amount of glue and adapter code between different technologies
* modeling cross-flow dependencies while handling failures gracefully and limiting their repercussions
We describe our ongoing journey in identifying patterns and principles to make our big data stack integrate well. Technologies to be covered will include MongoDB, Kafka, Hadoop (YARN), Hive (TEZ), Flink Batch, and Flink Streaming.
Keeping CALM – Konsistenz in verteilten Systemen leichtgemachtSusanne Braun
10 Jahre nach CAP wurde das CALM-Theorem formuliert, und beweist das, was wir immer alle geahnt haben: Im Falle von Netzwerkpartitionen sind Konsistenz UND Verfügbarkeit unter bestimmten Bedingungen möglich! Im Vortrag gehen wir auf eine Journey vom CAP-Theorem hin zum CALM-Theorem. Ich räume mit gängigen Mythen auf und zeige euch, wie ihr das CALM-Theorem praktisch anwenden könnt. Der Vortrag vereint neueste Forschungsergebnisse und Lessons Learned aus mehreren Studien mit konkreten Entwurfsmustern und Code-Beispielen für Architekt:innen.
Keynote VariVolution/VM4ModernTech@SPLC 2022
At compile-time or at runtime, varying software is a powerful means to achieve optimal functional and performance goals. An observation is that only considering the software layer might be naive to tune the performance of the system or test that the functionality behaves correctly. In fact, many layers (hardware, operating system, input data, build process etc.), themselves subject to variability, can alter performances of software configurations. For instance, configurations' options may have very different effects on execution time or energy consumption when used with different input data, depending on the way it has been compiled and the hardware on which it is executed.
In this talk, I will introduce the concept of “deep software variability” which refers to the interactions of all external layers modifying the behavior or non-functional properties of a software system. I will show how compile-time options, inputs, and software evolution (versions), some dimensions of deep variability, can question the generalization of the variability knowledge of popular configurable systems like Linux, gcc, xz, or x264.
I will then argue that machine learning (ML) is particularly suited to manage very large variants space. The key idea of ML is to build a model based on sample data -- here observations about software variants in variable settings -- in order to make predictions or decisions. I will review state-of-the-art solutions developed in software engineering and software product line engineering while connecting with works in ML (e.g., transfer learning, dimensionality reduction, adversarial learning). Overall, the key challenge is to leverage the right ML pipeline in order to harness all variability layers (and not only the software layer), leading to more efficient systems and variability knowledge that truly generalizes to any usage and context.
From this perspective, we are starting an initiative to collect data, software, reusable artefacts, and body of knowledge related to (deep) software variability: https://deep.variability.io
Finally, I will open a broader discussion on how machine learning and deep software variability relate to the reproducibility, replicability, and robustness of scientific, software-based studies (e.g., in neuroimaging and climate modelling).
Swift Parallel Scripting for High-Performance WorkflowDaniel S. Katz
The Swift scripting language was created to provide a simple, compact way to write parallel scripts that run many copies of ordinary programs concurrently in various workflow patterns, reducing the need for complex parallel programming or arcane scripting to achieve this common high-level task. The result was a highly portable programming model based on implicitly parallel functional dataflow. The same Swift script runs on multi-core computers, clusters, grids, clouds, and supercomputers, and is thus a useful tool for moving workflow computations from laptop to distributed and/or high performance systems.
Swift has proven to be very general, and is in use in domains ranging from earth systems to bioinformatics to molecular modeling. It’s more recently been adapted to serve as a programming model for much finer-grain in-memory workflow on extreme scale systems, where it can perform task rates in the millions to billion-per-second.
In this talk, we describe the state of Swift’s implementation, present several Swift applications, and discuss ideas for of the future evolution of the programming model on which it’s based.
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
Welcome to the sixth and last webinar in the "Taming the Dragon" series about Zenoh and its use for robotics, autonomous vehicle and Internet-scale HPC communities.
In this webinar, Julien Loudet (our Product Conductor) and Gabriele Baldoni (our Runtime Lead) will talk about Zenoh-Flow and also show a hands-on demonstration of its capabilities.
You can read more about Zenoh-Flow here: https://github.com/eclipse-zenoh/zenoh-flow
More details about configuring and starting a Zenoh-Flow: https://github.com/eclipse-zenoh/zenoh-flow/wiki/
Stay up to date with the latest news:
Twitter: https://twitter.com/zettascaletech
LinkedIn: https://www.linkedin.com/company/zettascaletech/
Website: https://www.zettascale.tech/
Newsletter: http://eepurl.com/igPw31
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.