CUDA by Example : The Final Countdown : NotesSubhajit Sahu
Highlighted notes of:
Chapter 12: The Final Countdown
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
AFDS 2011 Phil Rogers Keynote: “The Programmer’s Guide to the APU Galaxy.”HSA Foundation
AFDS Keynote: “The Programmer’s Guide to the APU Galaxy.”
Phil Rogers, AMD Corporate Fellow
It’s a well-understood maxim in the technology industry that software and hardware must evolve in parallel, and be well matched, to achieve greatness. With the introduction of the world’s first APU in January 2011, AMD pointed the world toward a new way of computing. This was very much a first step in an architectural journey that is well underway at AMD. APUs combine different processing engines in single-chip combinations to strike a unique balance between the dimensions of performance, power consumption and price. Hear how AMD is working to ease the programmer’s access to this new level of compute horsepower and dramatically expand the processing resources available to modern applications
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.
CUDA by Example : The Final Countdown : NotesSubhajit Sahu
Highlighted notes of:
Chapter 12: The Final Countdown
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
AFDS 2011 Phil Rogers Keynote: “The Programmer’s Guide to the APU Galaxy.”HSA Foundation
AFDS Keynote: “The Programmer’s Guide to the APU Galaxy.”
Phil Rogers, AMD Corporate Fellow
It’s a well-understood maxim in the technology industry that software and hardware must evolve in parallel, and be well matched, to achieve greatness. With the introduction of the world’s first APU in January 2011, AMD pointed the world toward a new way of computing. This was very much a first step in an architectural journey that is well underway at AMD. APUs combine different processing engines in single-chip combinations to strike a unique balance between the dimensions of performance, power consumption and price. Hear how AMD is working to ease the programmer’s access to this new level of compute horsepower and dramatically expand the processing resources available to modern applications
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.
Using GPUs to Handle Big Data with JavaTim Ellison
A copy of the slides presented at JavaOne conference 2014.
Learn how Java can exploit the power of graphics processing units (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.
HC-4020, Enhancing OpenCL performance in AfterShot Pro with HSA, by Michael W...AMD Developer Central
Presentation Hc-4020, Enhancing OpenCL performance in AfterShot Pro with HSA, by Michael Wootton at the AMD Developer Summit (APU13) November 11-13, 2013.
Great Paper on HSAemu Full system simulator built form PQUEMU to do Full System Emulation of HSA from our Academic Member Yeh-Ching Chung of National Tsing Hua University
A SURVEY ON GPU SYSTEM CONSIDERING ITS PERFORMANCE ON DIFFERENT APPLICATIONScseij
In this paper we study NVIDIA graphics processing unit (GPU) along with its computational power and applications. Although these units are specially designed for graphics application we can employee there computation power for non graphics application too. GPU has high parallel processing power, low cost of computation and less time utilization; it gives good result of performance per energy ratio. This GPU deployment property for excessive computation of similar small set of instruction played a significant role in reducing CPU overhead. GPU has several key advantages over CPU architecture as it provides high parallelism, intensive computation and significantly higher throughput. It consists of thousands of hardware threads that execute programs in a SIMD fashion hence GPU can be an alternate to CPU in high performance environment and in supercomputing environment. The base line is GPU based general purpose computing is a hot topics of research and there is great to explore rather than only graphics processing application.
An exposition of performance comparison of graphic processing unit virtualiza...Asif Farooq
As the demand for computing power is increasing the number of new and improved methodologies in computer architectures are expanding. With the introduction of accelerated heterogeneous computing model, compute times for complex algorithms and tasks are reduced significantly as a result of high degree data parallelism. GPU based heterogeneous computing can not only benefit Cloud infrastructures but also large-scale distributed computing models to work more cost-effective by improving resource efficiencies and decreasing energy consumptions. Thus to implement such paradigm on cloud and largescale infrastructure would require effective GPU virtualization techniques. In this survey, an overview of GPGPU virtualization techniques using CUDA programming model is reviewed with a detailed performance comparison.
CC-4000, Characterizing APU Performance in HadoopCL on Heterogeneous Distribu...AMD Developer Central
Presentation CC-4000, Characterizing APU Performance in HadoopCL on Heterogeneous Distributed Platforms, by Max Grossman at the AMD Developer Summit (APU13) November 11-13, 2013.
Using GPUs to Handle Big Data with JavaTim Ellison
A copy of the slides presented at JavaOne conference 2014.
Learn how Java can exploit the power of graphics processing units (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.
HC-4020, Enhancing OpenCL performance in AfterShot Pro with HSA, by Michael W...AMD Developer Central
Presentation Hc-4020, Enhancing OpenCL performance in AfterShot Pro with HSA, by Michael Wootton at the AMD Developer Summit (APU13) November 11-13, 2013.
Great Paper on HSAemu Full system simulator built form PQUEMU to do Full System Emulation of HSA from our Academic Member Yeh-Ching Chung of National Tsing Hua University
A SURVEY ON GPU SYSTEM CONSIDERING ITS PERFORMANCE ON DIFFERENT APPLICATIONScseij
In this paper we study NVIDIA graphics processing unit (GPU) along with its computational power and applications. Although these units are specially designed for graphics application we can employee there computation power for non graphics application too. GPU has high parallel processing power, low cost of computation and less time utilization; it gives good result of performance per energy ratio. This GPU deployment property for excessive computation of similar small set of instruction played a significant role in reducing CPU overhead. GPU has several key advantages over CPU architecture as it provides high parallelism, intensive computation and significantly higher throughput. It consists of thousands of hardware threads that execute programs in a SIMD fashion hence GPU can be an alternate to CPU in high performance environment and in supercomputing environment. The base line is GPU based general purpose computing is a hot topics of research and there is great to explore rather than only graphics processing application.
An exposition of performance comparison of graphic processing unit virtualiza...Asif Farooq
As the demand for computing power is increasing the number of new and improved methodologies in computer architectures are expanding. With the introduction of accelerated heterogeneous computing model, compute times for complex algorithms and tasks are reduced significantly as a result of high degree data parallelism. GPU based heterogeneous computing can not only benefit Cloud infrastructures but also large-scale distributed computing models to work more cost-effective by improving resource efficiencies and decreasing energy consumptions. Thus to implement such paradigm on cloud and largescale infrastructure would require effective GPU virtualization techniques. In this survey, an overview of GPGPU virtualization techniques using CUDA programming model is reviewed with a detailed performance comparison.
CC-4000, Characterizing APU Performance in HadoopCL on Heterogeneous Distribu...AMD Developer Central
Presentation CC-4000, Characterizing APU Performance in HadoopCL on Heterogeneous Distributed Platforms, by Max Grossman at the AMD Developer Summit (APU13) November 11-13, 2013.
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.
COMPARING PROGRAMMER PRODUCTIVITY IN OPENACC AND CUDA: AN EMPIRICAL INVESTIGA...IJCSEA Journal
OpenACC has been touted as a "high productivity" API designed to make GPGPU programming accessible
to scientific programmers, but to date, no studies have attempted to verify this quantitatively. In this paper,
we conduct an empirical investigation of program productivity comparisons between OpenACC and CUDA
in the programming time, the execution time and the analysis of independence of OpenACC model in high
performance problems. Our results show that, for our programs and our subject pool, this claim is true. We
created two assignments called Machine Problem 3(MP3) and Machine Problem 4(MP4) in the classroom
environment and instrumented the WebCode website developed by ourselves to record details of students’
coding process. Three hypotheses were supported by the statistical data: for the same parallelizable
problem, (1) the OpenACC programming time is at least 37% shorter than CUDA; (2) the CUDA running
speed is 9x faster than OpenACC; (3) the OpenACC development work is not significantly affected by
previous CUDA experience
COMPARING PROGRAMMER PRODUCTIVITY IN OPENACC AND CUDA: AN EMPIRICAL INVESTIGA...IJCSEA Journal
OpenACC has been touted as a "high productivity" API designed to make GPGPU programming accessible to scientific programmers, but to date, no studies have attempted to verify this quantitatively. In this paper,
we conduct an empirical investigation of program productivity comparisons between OpenACC and CUDA
in the programming time, the execution time and the analysis of independence of OpenACC model in high
performance problems. Our results show that, for our programs and our subject pool, this claim is true. We
created two assignments called Machine Problem 3(MP3) and Machine Problem 4(MP4) in the classroom environment and instrumented the WebCode website developed by ourselves to record details of students’
coding process. Three hypotheses were supported by the statistical data: for the same parallelizable
problem, (1) the OpenACC programming time is at least 37% shorter than CUDA; (2) the CUDA running
speed is 9x faster than OpenACC; (3) the OpenACC development work is not significantly affected by
previous CUDA experience
COMPARING PROGRAMMER PRODUCTIVITY IN OPENACC AND CUDA: AN EMPIRICAL INVESTIGA...IJCSEA Journal
OpenACC has been touted as a "high productivity" API designed to make GPGPU programming accessible to scientific programmers, but to date, no studies have attempted to verify this quantitatively. In this paper, we conduct an empirical investigation of program productivity comparisons between OpenACC and CUDA in the programming time, the execution time and the analysis of independence of OpenACC model in high performance problems. Our results show that, for our programs and our subject pool, this claim is true. We created two assignments called Machine Problem 3(MP3) and Machine Problem 4(MP4) in the classroom environment and instrumented the WebCode website developed by ourselves to record details of students’ coding process. Three hypotheses were supported by the statistical data: for the same parallelizable problem, (1) the OpenACC programming time is at least 37% shorter than CUDA; (2) the CUDA running speed is 9x faster than OpenACC; (3) the OpenACC development work is not significantly affected by previous CUDA experience
In this deck from FOSDEM'19, Thomas Schwinge presents: Speeding up Programs with OpenACC in GCC.
"Proven in production use for decades, GCC (the GNU Compiler Collection) offers C, C++, Fortran, and other compilers for a multitude of target systems. Over the last few years, we -- formerly known as "CodeSourcery", now a group in "Mentor, a Siemens Business" -- added support for the directive-based OpenACC programming model. Requiring only few changes to your existing source code, OpenACC allows for easy parallelization and code offloading to accelerators such as GPUs. We will present a short introduction of GCC and OpenACC, implementation status, examples, and performance results.
OpenACC is a user-driven directive-based performance-portable parallel programming model designed for scientists and engineers interested in porting their codes to a wide-variety of heterogeneous HPC hardware platforms and architectures with significantly less programming effort than required with a low-level model."
Watch the video: https://wp.me/p3RLHQ-jOR
Learn more: https://fosdem.org/2019/
and
https://www.openacc.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/07/a-new-open-standards-based-open-source-programming-model-for-all-accelerators-a-presentation-from-codeplay-software/
Charles Macfarlane, Chief Business Officer at Codeplay Software, presents the “New, Open-standards-based, Open-source Programming Model for All Accelerators” tutorial at the May 2023 Embedded Vision Summit.
As demand for AI grows, developers are attempting to squeeze more and more performance from accelerators. Ideally, developers would choose the accelerators best suited to their applications. Unfortunately, today many developers are locked into limited hardware choices because they use proprietary programming models like NVIDIA’s CUDA. The oneAPI project was launched to create an open specification and open-source software that enables developers to write software using standard C++ code and deploy to GPUs from multiple vendors.
OneAPI is an open-source ecosystem based on the Khronos open-standard SYCL with libraries for enabling AI and HPC applications. OneAPI-enabled software is currently deployed on numerous supercomputers, with plans to extend into other market segments. OneAPI is evolving rapidly and the whole community of hardware and software developers is invited to contribute. In this presentation, Macfarlane introduces how oneAPI enables developers to write multi-target software and highlights opportunities for developers to contribute to making oneAPI available for all accelerators.
Deploying deep learning models with Docker and KubernetesPetteriTeikariPhD
Short introduction for platform agnostic production deployment with some medical examples.
Alternative download: https://www.dropbox.com/s/qlml5k5h113trat/deep_cloudArchitecture.pdf?dl=0
Check out these DLI training courses at GTC 2019 designed for developers, data scientists & researchers looking to solve the world’s most challenging problems with accelerated computing.
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the upcoming OpenACC Summit, a complete schedule of upcoming events, using OpenACC to optimize structural analysis, new resources and more!
Shared by Mansoor Mirza
Distributed Computing
What is it?
Why & when we need it?
Comparison with centralized computing
‘MapReduce’ (MR) Framework
Theory and practice
‘MapReduce’ in Action
Using Hadoop
Lab exercises
Introduction to Real-Time Operating Systemscoolmirza143
shared by Mansoor Mirza
Understanding Real-Time Operating Systems
Types of Real-Time Operating System
Requirements for Real-Time Operating System
Difference between General Purpose Operating System (GPOS) and Real-Time Operating System (RTOS)
Conversion Linux kernel to support Real-Time operations
Patching the linux kernel
Major changes in patched kernel
Hands-on labs
Conversion of Linux kernel to support real time
Code a real time application (Audio Feedback removal)
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
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
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.