GPU computing provides a way to access the power of massively parallel graphics processing units (GPUs) for general purpose computing. GPUs contain over 100 processing cores and can achieve over 500 gigaflops of performance. The CUDA programming model allows programmers to leverage this parallelism by executing compute kernels on the GPU from their existing C/C++ applications. This approach democratizes parallel computing by making highly parallel systems accessible through inexpensive GPUs in personal computers and workstations. Researchers can now explore manycore architectures and parallel algorithms using GPUs as a platform.
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
This presentation focuses on Nvidia GPUs and explores the topics of what a GPU is, its basic architecture, how it is different from a CPU, its basic working, and what new Nvidia has to offer in consumer as well as server market
An introduction to Docker native clustering: Swarm.
Deployment and configuration, integration with Consul, for a product-like cluster to serve web-application with multiple containers on multiple hosts. #dockerops
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
This presentation focuses on Nvidia GPUs and explores the topics of what a GPU is, its basic architecture, how it is different from a CPU, its basic working, and what new Nvidia has to offer in consumer as well as server market
An introduction to Docker native clustering: Swarm.
Deployment and configuration, integration with Consul, for a product-like cluster to serve web-application with multiple containers on multiple hosts. #dockerops
Building Data Integration and Transformations using PentahoAshnikbiz
This presentation will showcase the Data Integration capabilities of Pentaho which helps in building data transformations, through two demonstrations:
- How to build your first transformation to extract, transform and blend the data from various data sources
- How to add additional steps and filters to your transformation
NGINX Plus PLATFORM For Flawless Application DeliveryAshnikbiz
Flawless Application Delivery using Nginx Plus
By leveraging these latest features:
• Support for HTTP/2 standard
• Thread pools and socket sharding and how it can help improve performance
• NTLM support and new TCP security enhancements
• Advanced NGINX Plus monitoring, management and visibility of health & load checks
Catch this exclusive Google Hangout live!
November 4th, 2015 | 2.00-2.30PM IST | 4.30-5.00PM SGT
About the speaker: Sandeep Khuperkar, Director and CTO at Ashnik will be heading this session. He is an author, enthusiast and community moderator at opensource.com. He is also member of Open Source Initiative, Linux Foundation and Open Source Consortium Of India.
Scaling Jenkins with Docker: Swarm, Kubernetes or Mesos?Carlos Sanchez
The Jenkins platform can be dynamically scaled by using several Docker cluster and orchestration platforms, using containers to run slaves and jobs and also isolating job execution. But which cluster technology should be used? Docker Swarm? Apache Mesos? Kubernetes? How do they compare? All of them can be used to dynamically run jobs inside containers. This talk will cover these main container clusters, outlining the pros and cons of each, the current state of the art of the technologies and Jenkins support.
Business Intelligence and Big Data Analytics with Pentaho Uday Kothari
This webinar gives an overview of the Pentaho technology stack and then delves deep into its features like ETL, Reporting, Dashboards, Analytics and Big Data. The webinar also facilitates a cross industry perspective and how Pentaho can be leveraged effectively for decision making. In the end, it also highlights how apart from strong technological features, low TCO is central to Pentaho’s value proposition. For BI technology enthusiasts, this webinar presents easiest ways to learn an end to end analytics tool. For those who are interested in developing a BI / Analytics toolset for their organization, this webinar presents an interesting option of leveraging low cost technology. For big data enthusiasts, this webinar presents overview of how Pentaho has come out as a leader in data integration space for Big data.
Pentaho is one of the leading niche players in Business Intelligence and Big Data Analytics. It offers a comprehensive, end-to-end open source platform for Data Integration and Business Analytics. Pentaho’s leading product: Pentaho Business Analytics is a data integration, BI and analytics platform composed of ETL, OLAP, reporting, interactive dashboards, ad hoc analysis, data mining and predictive analytics.
Scaling Jenkins with Docker and KubernetesCarlos Sanchez
Docker is revolutionizing the way people think about applications and deployments. It provides a simple way to run and distribute Linux containers for a variety of use cases, from lightweight virtual machines to complex distributed micro-services architectures. Kubernetes is an open source project to manage a cluster of Linux containers as a single system, managing and running Docker containers across multiple Docker hosts, offering co-location of containers, service discovery and replication control. It was started by Google and now it is supported by Microsoft, RedHat, IBM and Docker Inc amongst others. Jenkins Continuous Integration environment can be dynamically scaled by using the Kubernetes and Docker plugins, using containers to run slaves and jobs, and also isolate job execution.
Load Balancing Apps in Docker Swarm with NGINXNGINX, Inc.
On-demand webinar recording: http://bit.ly/2mRjk2g
Docker and other container technologies continue to gain in popularity. We recently surveyed the broad community of NGINX and NGINX Plus users and found that two-thirds of organizations are either investigating containers, using them in development, or using them in production. Why? Because abstracting your applications from the underlying infrastructure makes developing, distributing, and running software simpler, faster, and more robust than ever before.
But when you move from running your app in a development environment to deploying containers in production, you face new challenges – such as how to effectively run and scale an application across multiple hosts with the performance and uptime that your customers demand.
The latest Docker release, 1.12, supports multihost container orchestration, which simplifies deployment and management of containers across a cluster of Docker hosts. In a complex environment like this, load balancing plays an essential part in delivering your container-based application with reliability and high performance.
Join us in this webinar to learn:
* The basic built-in load balancing options available in Docker Swarm Mode
* The pros and cons of moving to an advanced load balancer like NGINX
* How to integrate NGINX and NGINX Plus with Swarm Mode to provide an advanced load-balancing solution for a cluster with orchestration
* How to scale your Docker-based application with Swarm Mode and NGINX Plus
EDW CENIPA is a opensource project designed to enable analysis of aeronautical incidentes that occured in the brazilian civil aviation. The project uses techniques and BI tools that explore innovative low-cost technologies. Historically, Business Intelligence platforms are expensive and impracticable for small projects. BI projects require specialized skills and high development costs. This work aims to break this barrier.
Graphics processing unit or GPU (also occasionally called visual processing unit or VPU) is a specialized microprocessor that offloads and accelerates graphics rendering from the central (micro) processor. Modern GPUs are very efficient at manipulating computer graphics, and their highly parallel structure makes them more effective than general-purpose CPUs for a range of complex algorithms. In CPU, only a fraction of the chip does computations where as the GPU devotes more transistors to data processing.
GPGPU is a programming methodology based on modifying algorithms to run on existing GPU hardware for increased performance. Unfortunately, GPGPU programming is significantly more complex than traditional programming for several reasons.
Presentation I gave at the SORT Conference in 2011. Was generalized from some work I had done with using GPUs to accelerate image processing at FamilySearch.
Backend.AI Technical Introduction (19.09 / 2019 Autumn)Lablup Inc.
This slide introduces technical specs and details about Backend.AI 19.09.
* On-premise clustering / container orchestration / scaling on cloud
* Container-level fractional GPU technology to use one GPU as many GPUs on many containers at the same time.
* NVidia GPU Cloud integrations
* Enterprise features
The presentation will introduce Nvidia and the concept of GPU computing in the context of Financial Services industry. Customer successes are referenced where dramatic speed-ups in performance have been achieved.
Axel Koehler from Nvidia presented this deck at the 2016 HPC Advisory Council Switzerland Conference.
“Accelerated computing is transforming the data center that delivers unprecedented through- put, enabling new discoveries and services for end users. This talk will give an overview about the NVIDIA Tesla accelerated computing platform including the latest developments in hardware and software. In addition it will be shown how deep learning on GPUs is changing how we use computers to understand data.”
In related news, the GPU Technology Conference takes place April 4-7 in Silicon Valley.
Watch the video presentation: http://insidehpc.com/2016/03/tesla-accelerated-computing/
See more talks in the Swiss Conference Video Gallery:
http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter:
http://insidehpc.com/newsletter
Semiconductors are the driving force behind the AI evolution and enable its adoption across various application areas ranging from connected and automated driving to smart healthcare and wearables. Given that, electronics research, design and manufacturing communities around the world are increasingly investing in specialized AI chips providing less latency, greater processing power, higher bandwidth and faster performance. AI also attracts new technology players to invest in making their own specialized AI chips, changing the electronics manufacturing landscape and moving the AI technology towards machine learning, deep learning and neural networks.
Liberati dal sovraccarico e dalle limitazioni dell’infrastruttura locale. Sfrutta risorse illimitate per ottenere scalabilità per i processi HPC (High Performance Computing), per analizzare dati su vasta scala, eseguire simulazioni e modelli finanziari e sperimentare riducendo il tempo di immissione sul mercato.
Using GPUs to handle Big Data with Java by Adam Roberts.J On The Beach
Modern graphics processing units (GPUs) are efficient general-purpose stream processors. Learn how Java can exploit the power of GPUs to optimize high-performance enterprise and technical computing applications such as big data and analytics workloads. This presentation covers principles and considerations for GPU programming from Java and looks at the software stack and developer tools available. It also presents a demo showing GPU acceleration and discusses what is coming in the future.
In today's increasingly digitalised world, software defects are enormously expensive. In 2018, the Consortium for IT Software Quality reported that software defects cost the global economy $2.84 trillion dollars and affected more than 4 billion people. The average annual cost of software defects on Australian businesses is A$29 billion per year. Thus, failure to eliminate defects in safety-critical systems could result in serious injury to people, threats to life, death, and disasters. Traditionally, software quality assurance activities like testing and code review are widely adopted to discover software defects in a software product. However, ultra-large-scale systems, such as, Google, can consist of more than two billion lines of code, so exhaustively reviewing and testing every single line of code isn't feasible with limited time and resources. This project aims to create technologies that enable software engineers to produce the highest quality software systems with the lowest operational costs. To achieve this, this project will invent an end-to-end explainable AI platform to (1) understand the nature of critical defects; (2) predict and locate defects; (3) explain and visualise the characteristics of defects; (4) suggest potential patches to automatically fix defects; (5) integrate such platform as a GitHub bot plugin.
Defect models that are trained on class imbalanced datasets (i.e., the proportion of defective and clean modules is not equally represented) are highly susceptible to produce inaccurate prediction models. Prior research compares the impact of class rebalancing techniques on the performance of defect models but arrives at contradictory conclusions due to the use of different choice of datasets, classification techniques, and performance measures. Such contradictory conclusions make it hard to derive practical guidelines for whether class rebalancing techniques should be applied in the context of defect models. In this paper, we investigate the impact of class rebalancing techniques on performance measures and the interpretation of defect models. We also investigate the experimental settings in which class rebalancing techniques are beneficial for defect models. Through a case study of 101 datasets that span across proprietary and open-source systems, we conclude that the impact of class rebalancing techniques on the performance of defect prediction models depends on the used performance measure and the used classification techniques. We observe that the optimized SMOTE technique and the under-sampling technique are beneficial when quality assurance teams wish to increase AUC and Recall, respectively, but they should be avoided when deriving knowledge and understandings from defect models.
With the rise of the Mining Software Repositories (MSR) field, defect datasets extracted from software repositories play a foundational role in many empirical studies related to software quality. At the core of defect data preparation is the identification of post-release defects. Prior studies leverage many heuristics (e.g., keywords and issue IDs) to identify post-release defects. However, such the heuristic approach is based on several assumptions, which pose common threats to the validity of many studies. In this paper, we set out to investigate the nature of the difference of defect datasets generated by the heuristic approach and the realistic approach that leverages the earliest affected release that is realistically estimated by a software development team for a given defect. In addition, we investigate the impact of defect identification approaches on the predictive accuracy and the ranking of defective modules that are produced by defect models. Through a case study of defect datasets of 32 releases, we find that that the heuristic approach has a large impact on both defect count datasets and binary defect datasets. Surprisingly, we find that the heuristic approach has a minimal impact on defect count models, suggesting that future work should not be too concerned about defect count models that are constructed using heuristic defect datasets. On the other hand, using defect datasets generated by the realistic approach lead to an improvement in the predictive accuracy of defect classification models.
Software analytics focuses on analyzing and modeling a rich source of software data using well-established data analytics techniques in order to glean actionable insights for improving development practices, productivity, and software quality. However, if care is not taken when analyzing and modeling software data, the predictions and insights that are derived from analytical models may be inaccurate and unreliable. The goal of this hands-on tutorial is to guide participants on how to (1) analyze software data using statistical techniques like correlation analysis, hypothesis testing, effect size analysis, and multiple comparisons, (2) develop accurate, reliable, and reproducible analytical models, (3) interpret the models to uncover relationships and insights, and (4) discuss pitfalls associated with analytical techniques including hands-on examples with real software data. R will be the primary programming language. Code samples will be available in a public GitHub repository. Participants will do exercises via either RStudio or Jupyter Notebook through Binder.
With the rise of software systems ranging from personal assistance to the nation's facilities, software defects become more critical concerns as they can cost millions of dollar as well as impact human lives. Yet, at the breakneck pace of rapid software development settings (like DevOps paradigm), the Quality Assurance (QA) practices nowadays are still time-consuming. Continuous Analytics for Software Quality (i.e., defect prediction models) can help development teams prioritize their QA resources and chart better quality improvement plan to avoid pitfalls in the past that lead to future software defects. Due to the need of specialists to design and configure a large number of configurations (e.g., data quality, data preprocessing, classification techniques, interpretation techniques), a set of practical guidelines for developing accurate and interpretable defect models has not been well-developed.
The ultimate goal of my research aims to (1) provide practical guidelines on how to develop accurate and interpretable defect models for non-specialists; (2) develop an intelligible defect model that offer suggestions how to improve both software quality and processes; and (3) integrate defect models into a real-world practice of rapid development cycles like CI/CD settings. My research project is expected to provide significant benefits including the reduction of software defects and operating costs, while accelerating development productivity for building software systems in many of Australia's critical domains such as Smart Cities and e-Health.
Software analytics (for software quality purpose) is a statistical or machine learning classifier that is trained to identify defect-prone software modules. The goal of software analytics is to help software engineers prioritize their software testing effort on the most-risky modules and understand past pitfalls that lead to defective code. While the adoption of software analytics enables software organizations to distil actionable insights, there are still many barriers to broad and successful adoption of such analytics systems. Indeed, even if software organizations can access such invaluable software artifacts and toolkits for data analytics, researchers and practitioners often have little knowledge to properly develop analytics systems. Thus, the accuracy of the predictions and the insights that are derived from analytics systems is one of the most important challenges of data science in software engineering.
In this work, we conduct a series of empirical investigation to better understand the impact of experimental components (i.e., class mislabelling, parameter optimization of classification techniques, and model validation techniques) on the performance and interpretation of software analytics. To accelerate a large amount of compute-intensive experiment, we leverage the High-Performance-Computing (HPC) resources of Centre for Advanced Computing (CAC) from Queen’s University, Canada. Through case studies of systems that span both proprietary and open- source domains, we demonstrate that (1) realistic noise does not impact the precision of software analytics; (2) automated parameter optimization for classification techniques substantially improve the performance and stability of software analytics; and (3) the out-of- sample bootstrap validation technique produces a good balance between bias and variance of performance estimates. Our results lead us to conclude that the experimental components of analytics modelling impact the predictions and associated insights that are derived from software analytics. Empirical investigations on the impact of overlooked experimental components are needed to derive practical guidelines for analytics modelling.
Defect prediction models help software quality assurance teams to effectively allocate their limited resources to the most defect-prone software modules. Model validation techniques, such as k-fold cross-validation, use this historical data to estimate how well a model will perform in the future. However, little is known about how accurate the performance estimates of these model validation techniques tend to be. In this paper, we set out to investigate the bias and variance of model validation techniques in the domain of defect prediction. A preliminary analysis of 101 publicly available defect prediction datasets suggests that 77% of them are highly susceptible to producing unstable results. Hence, selecting an appropriate model validation technique is a critical experimental design choice. Based on an analysis of 256 studies in the defect prediction literature, we select the 12 most commonly adopted model validation techniques for evaluation. Through a case study of data from 18 systems that span both open-source and proprietary domains, we derive the following practical guidelines for future defect prediction studies: (1) the single holdout validation techniques should be avoided; and (2) researchers should use the out-of-sample bootstrap validation technique instead of holdout or the commonly-used cross-validation techniques.
Software Quality Assurance (SQA) teams play a critical role in the software development process to ensure the absence of software defects. It is not feasible to perform exhaustive SQA tasks (i.e., software testing and code review) on a large software product given the limited SQA resources that are available. Thus, the prioritization of SQA efforts is an essential step in all SQA efforts. Defect prediction models are used to prioritize risky software modules and understand the impact of software metrics on the defect-proneness of software modules. The predictions and insights that are derived from defect prediction models can help software teams allocate their limited SQA resources to the modules that are most likely to be defective and avoid common past pitfalls that are associated with the defective modules of the past. However, the predictions and insights that are derived from defect prediction models may be inaccurate and unreliable if practitioners do not control for the impact of experimental components (e.g., datasets, metrics, and classifiers) on defect prediction models, which could lead to erroneous decision-making in practice. In this thesis, we investigate the impact of experimental components on the performance and interpretation of defect prediction models. More specifically, we investigate the impact of the three often overlooked experimental components (i.e., issue report mislabelling, parameter optimization of classification techniques, and model validation techniques) have on defect prediction models. Through case studies of systems that span both proprietary and open-source domains, we demonstrate that (1) issue report mislabelling does not impact the precision of defect prediction models, suggesting that researchers can rely on the predictions of defect prediction models that were trained using noisy defect datasets; (2) automated parameter optimization for classification techniques substantially improve the performance and stability of defect prediction models, as well as they change their interpretation, suggesting that researchers should no longer shy from applying parameter optimization to their models; and (3) the out-of-sample bootstrap validation technique produces a good balance between bias and variance of performance estimates, suggesting that the single holdout and cross-validation families that are commonly-used nowadays should be avoided.
The reliability of a prediction model depends on the quality of the data from which it was trained. Therefore, defect prediction models may be unreliable if they are trained using noisy data. Recent research suggests that randomly-injected noise that changes the classification (label) of software modules from defective to clean (and vice versa) can impact the performance of defect models. Yet, in reality, incorrectly labelled (i.e., mislabelled) issue reports are likely non-random. In this paper, we study whether mislabelling is random, and the impact that realistic mislabelling has on the performance and interpretation of defect models. Through a case study of 3,931 manually-curated issue reports from the Apache Jackrabbit and Lucene systems, we find that: (1) issue report mislabelling is not random; (2) precision is rarely impacted by mislabelled issue reports, suggesting that practitioners can rely on the accuracy of modules labelled as defective by models that are trained using noisy data; (3) however, models trained on noisy data typically achieve 56%-68% of the recall of models trained on clean data; and (4) only the metrics in top influence rank of our defect models are robust to the noise introduced by mislabelling, suggesting that the less influential metrics of models that are trained on noisy data should not be interpreted or used to make decisions.
Abstract: Due to the increasing of software requirements and software features,
modern software systems continue to grow in size and complexity. Locating
source code entities that required to implement a feature in millions lines of code
is labor and cost intensive for developers. To this end, several studies have proposed
the use of Information Retrieval (IR) to rank source code entities based on
their textual similarity to an issue report. The ranked source code entities could be
at a class or function granularity level. Source code entities at the class-level are
usually large in size and might contain a lot of functions that are not implemented
for the feature. Hence, we conjecture that the class-level feature location technique
requires more effort than function-level feature location technique. In this
paper, we investigate the impact of granularity levels on a feature location technique.
We also presented a new evaluation method using effort-based evaluation.
The results indicated that function-level feature location technique outperforms
class-level feature location technique. Moreover, function-level feature location
technique also required 7 times less effort than class-level feature location technique
to localize the first relevant source code entity. Therefore, we conclude that
feature location technique at the function-level of program elements is effective
in practice.
Reference:
Chakkrit Tantithamthavorn, Akinori Ihara, Hideaki Hata and Kenichi Matsumoto, Impact Analysis of Granularity Levels on Feature Location Technique, In Proceedings of The First Asia Pacific Requirements Engineering Symposium (APRES’14), pp. 135 - 149, Aukland, New Zealand, April 28-29, 2014.
Part I: Introduction to Cloud Computing
- What is Cloud Computing?
- Classification of Cloud Computing
Part II: Introduction to Google App Engine
- What is Google App Engine?
- Why Google App Engine?
- Core APIs & Language Support
- Google App Engine for Business
- Google App Engine Customers
- Q&A
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
5. Parallel Computing’s Dark Age
But…impact of data-parallel computing limited
Thinking Machines sold 7 CM-1s (100s of systems total)
MasPar sold ~200 systems
Commercial and research activity subsided
Massively-parallel machines replaced by clusters
of ever-more powerful commodity microprocessors
Beowulf, Legion, grid computing, …
Massively parallel computing lost momentum to
the inexorable advance of commodity technology
16. Heterogeneous Programming
CUDA = serial program with parallel kernels, all in C
Serial C code executes in a CPU thread
Parallel kernel C code executes in thread blocks
across multiple processing elements
Serial Code
Parallel Kernel
KernelA<<< nBlk, nTid >>>(args); ...
Serial Code
Parallel Kernel
KernelB<<< nBlk, nTid >>>(args); ...
18. CUDA: Programming GPU in C
Philosophy: provide minimal set of extensions necessary to expose power
Declaration specifiers to indicate where things live
__global__ void KernelFunc(...); // kernel function, runs on device
__device__ int GlobalVar; // variable in device memory
__shared__ int SharedVar; // variable in per-block shared memory
Extend function invocation syntax for parallel kernel launch
KernelFunc<<<500, 128>>>(...); // launch 500 blocks w/ 128 threads each
Special variables for thread identification in kernels
dim3 threadIdx; dim3 blockIdx; dim3 blockDim; dim3 gridDim;
Intrinsics that expose specific operations in kernel code
__syncthreads(); // barrier synchronization within kernel