Are your application's tail-latencies holding it back from delivering its near-real time SLOs? Do your in-memory processing platform's long pauses only get worse with increasing heap sizes? How about those latency spikes causing variability in your end-to-end latency for your multi-tiered distributed systems?
If any of the above keep you up at night, then have no fear as Z Garbage Collector (GC) is here and is production ready in JDK 15.
In this talk, Monica Beckwith will cover the basics of Z GC and contrast it with G1 GC (the current default collector for OpenJDK JDK 11 LTS and tip).
Secrets of Performance Tuning Java on KubernetesBruno Borges
Java on Kubernetes may seem complicated, but after a bit of YAML and Dockerfiles, you will wonder what all that fuss was. But then the performance of your app in 1 CPU/1 GB of RAM makes you wonder. Learn how JVM ergonomics, CPU throttling, and GCs can help increase performance while reducing costs.
Virtual machines don't have to be slow, they don't even have to be slower than running native code.
All you have to do is write your code, lay back and let the JVM do its magic !
Learn about various JVM runtime optimizations and why is it considered one of the best VMs in the world.
This presentation was given to the system adminstration team to give them an idea of how GC works and what to look for when there is abottleneck and troubles.
Hadoop Meetup Jan 2019 - Dynamometer and a Case Study in NameNode GCErik Krogen
Erik Krogen of LinkedIn presents regarding Dynamometer, a system open sourced by LinkedIn for scale- and performance-testing HDFS. He discusses one major use case for Dynamometer, tuning NameNode GC, and discusses characteristics of NameNode GC such as why it is important, and how it interacts with various current and future GC algorithms.
This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.
Secrets of Performance Tuning Java on KubernetesBruno Borges
Java on Kubernetes may seem complicated, but after a bit of YAML and Dockerfiles, you will wonder what all that fuss was. But then the performance of your app in 1 CPU/1 GB of RAM makes you wonder. Learn how JVM ergonomics, CPU throttling, and GCs can help increase performance while reducing costs.
Virtual machines don't have to be slow, they don't even have to be slower than running native code.
All you have to do is write your code, lay back and let the JVM do its magic !
Learn about various JVM runtime optimizations and why is it considered one of the best VMs in the world.
This presentation was given to the system adminstration team to give them an idea of how GC works and what to look for when there is abottleneck and troubles.
Hadoop Meetup Jan 2019 - Dynamometer and a Case Study in NameNode GCErik Krogen
Erik Krogen of LinkedIn presents regarding Dynamometer, a system open sourced by LinkedIn for scale- and performance-testing HDFS. He discusses one major use case for Dynamometer, tuning NameNode GC, and discusses characteristics of NameNode GC such as why it is important, and how it interacts with various current and future GC algorithms.
This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.
Title: Java at Scale - What Works and What Doesn't Work Nearly so Well
Speaker: Matt Schuetze, Product Manager, Azul Systems
Abstract: Java gets used everywhere and for everything due to its efficiency, portability, the productivity it offers developers, and the platform it provides for application frameworks and non-Java languages. But all is not perfect; developers both benefit from and struggle against Java's greatest strength: its memory management. In this session, Matt will describe where Java needs help, the challenges it presents developers who need to provide reliable performance, the reasons those challenges exist, and how developers have traditionally worked around them. He will then discuss where Zing fits in the spectrum of use cases where large memory and predictable performance dominate essential application characteristics.
Garbage First Garbage Collector (G1 GC) - Migration to, Expectations and Adva...Monica Beckwith
Learn what you need to know to experience nirvana in the evaluation of G1 GC even if your are migrating from Parallel GC to G1, or CMS GC to G1 GC
You also get a walk through of some case study data
G1 GC
HBaseCon 2015: Taming GC Pauses for Large Java Heap in HBaseHBaseCon
In this presentation, we will introduce Hotspot's Garbage First collector (G1GC) as the most suitable collector for latency-sensitive applications running with large memory environments. We will first discuss G1GC internal operations and tuning opportunities, and also cover tuning flags that set desired GC pause targets, change adaptive GC thresholds, and adjust GC activities at runtime. We will provide several HBase case studies using Java heaps as large as 100GB that show how to best tune applications to remove unpredicted, protracted GC pauses.
All the fundamental concepts and tools for understanding performance tuning in Java. Garbage collection, memory management and collector types and tools for profiling Java applications.
Software Profiling: Java Performance, Profiling and FlamegraphsIsuru Perera
Guest lecture at University of Colombo School of Computing on 30th May 2018
Covers following topics:
Software Profiling
Measuring Performance
Java Garbage Collection
Sampling vs Instrumentation
Java Profilers. Java Flight Recorder
Java Just-in-Time (JIT) compilation
Flame Graphs
Linux Profiling
Optimizing your java applications for multi core hardwareIndicThreads
Session Presented at 5th IndicThreads.com Conference On Java held on 10-11 December 2010 in Pune, India
WEB: http://J10.IndicThreads.com
------------
Rising power dissipation in microprocessor chips is leading to a trend towards increasing the number of cores on a chip (multi-core processors) rather than increasing clock frequency as the primary basis for increasing system performance. Consequently the number of threads in commodity hardware has also exploded. This leads to complexity in designing and configuring high performance Java applications that make effective use of new hardware. In this talk we provide a summary of the changes happening in the multi-core world and subsequently discuss about some of the JVM features which exploit the multi-core capabilities of the underlying hardware. We also explain techniques to analyze and optimize your application for highly concurrent systems. Key topics include an overview of Java Virtual Machine features & configuration, ways to correctly leverage java.util.concurrent package to achieve enhanced parallelism for applications in a multi-core environment, operating system issues, virtualization, Java code optimizations and useful profiling tools and techniques.
Takeaways for the Audience
Attendees will leave with a better understanding of the new multi-core world, understanding of Java Virtual Machine features which exploit mulit-core and the techniques they can apply to ensure their Java applications run well in mulit-core environment.
This session brings to your attention how several millions of dollars are wasted and what you can do to save money. Optimizing garbage collection performance not only saves money, but also improves the overall customer experience as well.
Elastic JVM for Scalable Java EE Applications Running in Containers #Jakart...Jelastic Multi-Cloud PaaS
Being configured smartly, Java can be scalable and cost-effective for all ranges of projects — from cloud-native startups to legacy enterprise applications. During this session, we will share our experiences in tuning RAM usage in a Java process to make it more elastic and gain the benefits of faster scaling and lower total cost of ownership (TCO). With microservices, cloud hosting, and vertical scaling in mind, we'll compare the top Java garbage collectors to see how efficiently they handle memory resources. The provided results of testing G1, Parallel, ConcMarkSweep, Serial, Shenandoah, ZGC and OpenJ9 garbage collectors while scaling Java EE applications vertically will help you to make the right choice for own projects.
More details about Garbage Collector types https://jelastic.com/blog/garbage-collection/
Free registration at Jelastic https://jelastic.com/
GARBAGE COLLECTOR Automatic garbage collection is the process of looking at heap memory, identifying which objects are in use and which are not, and deleting the unused objects. An in use object, or a referenced object, means that some part of your program still maintains a pointer to that object. An unused object, or unreferenced object, is no longer referenced by any part of your program. So the memory used by an unreferenced object can be reclaimed. In a programming language like C, allocating and deallocating memory is a manual process. In Java, process of deallocating memory is handled automatically by the garbage collector.
Implementing data and databases on K8s within the Dutch governmentDoKC
A small walkthrough of projects within the dutch government running Data(bases) on OpenShift. This talk shares success stories, provides a proven recipe to `get it done` and debunks some of the FUD.
About Sebastiaan:
I have always been a weird DBA, trying to combine Databases with out-of-the-box thinking and a DevOps mindset. Around 2016 I fell in love with both Postgres and Kubernetes, and I then committed my life to enabling Dutch organisations with running their Database workloads CloudNative.
Over the last few years I worked as a private contractor for 2 large government agencies doing exactly that, and I want to share my and others (success stories) hoping to enable and inspire Data on Kubernetes adoption.
DevoxxUK: Optimizating Application Performance on KubernetesDinakar Guniguntala
Now that you have your apps running on K8s, wondering how to get the response time that you need ? Tuning a polyglot set of microservices to get the performance that you need can be challenging in Kubernetes. The key to overcoming this is observability. Luckily there are a number of tools such as Prometheus that can provide all the metrics you need, but here is the catch, there is so much of data and metrics that is difficult make sense of it all. This is where Hyperparameter tuning can come to the rescue to help build the right models.
This talk covers best practices that will help attendees
1. To understand and avoid common performance related problems.
2. Discuss observability tools and how they can help identify perf issues.
3. Look closer into Kruize Autotune which is a Open Source Autonomous Performance Tuning Tool for Kubernetes and where it can help.
Software Profiling: Understanding Java Performance and how to profile in JavaIsuru Perera
Guest lecture at University of Colombo School of Computing on 27th May 2017
Covers following topics:
Software Profiling
Measuring Performance
Java Garbage Collection
Sampling vs Instrumentation
Java Profilers. Java Flight Recorder
Java Just-in-Time (JIT) compilation
Flame Graphs
Linux Profiling
Since JDK 9, G1 GC is the default garbage collector (JEP 248). Up until 2017, Monica has shared some G1 GC details, performance tips, and optimizations that help G1's adaptiveness and provided advice on manual tuning.
Since then, G1 has come a long way in improving its adaptiveness. In this session, Monica will cover most of the important optimizations that are delivered from JDK9+ and how they can help your application's responsiveness, throughput, and help with footprint reduction. This is a saga that involves various regions and generations (all pun intended).
Title: Java at Scale - What Works and What Doesn't Work Nearly so Well
Speaker: Matt Schuetze, Product Manager, Azul Systems
Abstract: Java gets used everywhere and for everything due to its efficiency, portability, the productivity it offers developers, and the platform it provides for application frameworks and non-Java languages. But all is not perfect; developers both benefit from and struggle against Java's greatest strength: its memory management. In this session, Matt will describe where Java needs help, the challenges it presents developers who need to provide reliable performance, the reasons those challenges exist, and how developers have traditionally worked around them. He will then discuss where Zing fits in the spectrum of use cases where large memory and predictable performance dominate essential application characteristics.
Garbage First Garbage Collector (G1 GC) - Migration to, Expectations and Adva...Monica Beckwith
Learn what you need to know to experience nirvana in the evaluation of G1 GC even if your are migrating from Parallel GC to G1, or CMS GC to G1 GC
You also get a walk through of some case study data
G1 GC
HBaseCon 2015: Taming GC Pauses for Large Java Heap in HBaseHBaseCon
In this presentation, we will introduce Hotspot's Garbage First collector (G1GC) as the most suitable collector for latency-sensitive applications running with large memory environments. We will first discuss G1GC internal operations and tuning opportunities, and also cover tuning flags that set desired GC pause targets, change adaptive GC thresholds, and adjust GC activities at runtime. We will provide several HBase case studies using Java heaps as large as 100GB that show how to best tune applications to remove unpredicted, protracted GC pauses.
All the fundamental concepts and tools for understanding performance tuning in Java. Garbage collection, memory management and collector types and tools for profiling Java applications.
Software Profiling: Java Performance, Profiling and FlamegraphsIsuru Perera
Guest lecture at University of Colombo School of Computing on 30th May 2018
Covers following topics:
Software Profiling
Measuring Performance
Java Garbage Collection
Sampling vs Instrumentation
Java Profilers. Java Flight Recorder
Java Just-in-Time (JIT) compilation
Flame Graphs
Linux Profiling
Optimizing your java applications for multi core hardwareIndicThreads
Session Presented at 5th IndicThreads.com Conference On Java held on 10-11 December 2010 in Pune, India
WEB: http://J10.IndicThreads.com
------------
Rising power dissipation in microprocessor chips is leading to a trend towards increasing the number of cores on a chip (multi-core processors) rather than increasing clock frequency as the primary basis for increasing system performance. Consequently the number of threads in commodity hardware has also exploded. This leads to complexity in designing and configuring high performance Java applications that make effective use of new hardware. In this talk we provide a summary of the changes happening in the multi-core world and subsequently discuss about some of the JVM features which exploit the multi-core capabilities of the underlying hardware. We also explain techniques to analyze and optimize your application for highly concurrent systems. Key topics include an overview of Java Virtual Machine features & configuration, ways to correctly leverage java.util.concurrent package to achieve enhanced parallelism for applications in a multi-core environment, operating system issues, virtualization, Java code optimizations and useful profiling tools and techniques.
Takeaways for the Audience
Attendees will leave with a better understanding of the new multi-core world, understanding of Java Virtual Machine features which exploit mulit-core and the techniques they can apply to ensure their Java applications run well in mulit-core environment.
This session brings to your attention how several millions of dollars are wasted and what you can do to save money. Optimizing garbage collection performance not only saves money, but also improves the overall customer experience as well.
Elastic JVM for Scalable Java EE Applications Running in Containers #Jakart...Jelastic Multi-Cloud PaaS
Being configured smartly, Java can be scalable and cost-effective for all ranges of projects — from cloud-native startups to legacy enterprise applications. During this session, we will share our experiences in tuning RAM usage in a Java process to make it more elastic and gain the benefits of faster scaling and lower total cost of ownership (TCO). With microservices, cloud hosting, and vertical scaling in mind, we'll compare the top Java garbage collectors to see how efficiently they handle memory resources. The provided results of testing G1, Parallel, ConcMarkSweep, Serial, Shenandoah, ZGC and OpenJ9 garbage collectors while scaling Java EE applications vertically will help you to make the right choice for own projects.
More details about Garbage Collector types https://jelastic.com/blog/garbage-collection/
Free registration at Jelastic https://jelastic.com/
GARBAGE COLLECTOR Automatic garbage collection is the process of looking at heap memory, identifying which objects are in use and which are not, and deleting the unused objects. An in use object, or a referenced object, means that some part of your program still maintains a pointer to that object. An unused object, or unreferenced object, is no longer referenced by any part of your program. So the memory used by an unreferenced object can be reclaimed. In a programming language like C, allocating and deallocating memory is a manual process. In Java, process of deallocating memory is handled automatically by the garbage collector.
Implementing data and databases on K8s within the Dutch governmentDoKC
A small walkthrough of projects within the dutch government running Data(bases) on OpenShift. This talk shares success stories, provides a proven recipe to `get it done` and debunks some of the FUD.
About Sebastiaan:
I have always been a weird DBA, trying to combine Databases with out-of-the-box thinking and a DevOps mindset. Around 2016 I fell in love with both Postgres and Kubernetes, and I then committed my life to enabling Dutch organisations with running their Database workloads CloudNative.
Over the last few years I worked as a private contractor for 2 large government agencies doing exactly that, and I want to share my and others (success stories) hoping to enable and inspire Data on Kubernetes adoption.
DevoxxUK: Optimizating Application Performance on KubernetesDinakar Guniguntala
Now that you have your apps running on K8s, wondering how to get the response time that you need ? Tuning a polyglot set of microservices to get the performance that you need can be challenging in Kubernetes. The key to overcoming this is observability. Luckily there are a number of tools such as Prometheus that can provide all the metrics you need, but here is the catch, there is so much of data and metrics that is difficult make sense of it all. This is where Hyperparameter tuning can come to the rescue to help build the right models.
This talk covers best practices that will help attendees
1. To understand and avoid common performance related problems.
2. Discuss observability tools and how they can help identify perf issues.
3. Look closer into Kruize Autotune which is a Open Source Autonomous Performance Tuning Tool for Kubernetes and where it can help.
Software Profiling: Understanding Java Performance and how to profile in JavaIsuru Perera
Guest lecture at University of Colombo School of Computing on 27th May 2017
Covers following topics:
Software Profiling
Measuring Performance
Java Garbage Collection
Sampling vs Instrumentation
Java Profilers. Java Flight Recorder
Java Just-in-Time (JIT) compilation
Flame Graphs
Linux Profiling
Since JDK 9, G1 GC is the default garbage collector (JEP 248). Up until 2017, Monica has shared some G1 GC details, performance tips, and optimizations that help G1's adaptiveness and provided advice on manual tuning.
Since then, G1 has come a long way in improving its adaptiveness. In this session, Monica will cover most of the important optimizations that are delivered from JDK9+ and how they can help your application's responsiveness, throughput, and help with footprint reduction. This is a saga that involves various regions and generations (all pun intended).
This is part 1 in a series of talks covering Padawan Monica Beckwith’s hands-on practical experience over the last two decades. Monica, who has trained with many Knights and a few Masters, will cover what it means to be sympathetic to the underlying hardware in Scaling Up and Scaling Out scenarios. In addition, she will share examples to put cloud performance into perspective.
The Performance Engineer's Guide to Java (HotSpot) Virtual MachineMonica Beckwith
Monica Beckwith has worked with the Java Virtual Machine for more than a decade not just optimizing the JVM heuristics, but also improving the Just-in-time (JIT) code quality for various processor architectures as well as working with the garbage collectors and improving garbage collection for server systems.
During this talk, Monica will cover a few JIT and Runtime optimizations and she will dive into the HotSpot garbage collection and provide an overview of the various garbage collectors available in HotSpot.
At JavaOne keynote this year, Mark Reinhold talked about how Java 9 was much bigger than Jigsaw. To put that in numbers - 80+ JEPs bigger! Yes, we see more presentations on Jigsaw since it brings about modularity to the once monolithic JDK. But what about those other JEPs?! One of those "other" JEPs, is JEP 143 - 'Improve Contended Locking'. Monica will apply her performance engineering approach and talk about JEP 143 and Oracle's Studio Analyzer Performance Tool. The crux of the presentation will entail comparing performance of contended locks in JDK 9 to JDK 8.
Managed runtime performance expert, Monica Beckwith will divulge her survival guide which is essential for any application performance engineer. Following simple rules and performance engineering patterns will make you and your stakeholders happy.
The Performance Engineer's Guide To (OpenJDK) HotSpot Garbage Collection - Th...Monica Beckwith
Monica Beckwith is a JVM/GC Performance Engineer and has worked with the HotSpot VM for more than a decade. In this presentation, she will talk about GC Performance Engineering while providing GC facts and examples. She will also discuss various OpenJDK HotSpot GC algorithms and will provide advice on this topic.
The Performance Engineer's Guide To HotSpot Just-in-Time CompilationMonica Beckwith
Adaptive compilation and runtime in the OpenJDK Hotspot VM offers significant performance enhancements for our tools and applications in Java and other JVM languages. Understanding how it works provides developers with critical information on the Java HotSpot JIT compilation and runtime techniques such as vectorization, compressed OOPs etc., to assist in understanding performance for both client and server applications. We will focus on the internals of OpenJDK 8, the reference implementation for Java SE 8.
In Java 9, Garbage First Garbage Collector (G1 GC) will be the default GC. This presentation makes an effort to help Hotspot VM users to understand the concept of G1 GC as well as provides some tuning advice.
Garbage First Garbage Collector (G1 GC): Current and Future Adaptability and ...Monica Beckwith
G1 GC Presentation @ JavaOne 2013
Sneak a peek under the hood of the latest and coolest garbage collector, Garbage-First!
Dive deep into G1's adaptability and ergonomics
Discuss the future of G1's adaptability
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.
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/
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
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.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
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.
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.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
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.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-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
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Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
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.
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
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
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.
2. Agenda The Basic Principles of an Adaptive,
Predictable Garbage Collector
Designing a GC
Introduction to Z Garbage Collector
Design Considerations
Phases
Production Readiness
Performance Impact and Considerations
Comparison with G1 GC
4. OpenJDK HotSpot collectors are designed with different optimization
goals that lead to different algorithmic considerations.
• E.g., a generational collector helps with scaling and maximizing
throughput
• Having multiple GC threads working in parallel helps speed up the time
it takes to complete the GC work
Designing a Garbage Collector (GC)
5. Let’s Design a GC
GC
Generational Parallel Work
Young Old
Stop-the-
World Threads
Concurrent
Threads
Maintenance Barriers Maintenance Barriers
6. The goal is to avoid fragmentation and to not take resources
away from the application:
• The work of marking and compaction can be done in a single
stop-the-world (STW) pause/collection known as the Full GC.
• Avoid concurrent work as that may take resources away from
the application threads.
Designing a Throughput Maximizing GC
7. Let’s Design a Throughput Maximizing GC
Generational Parallel Work
Young Old
Stop-the-
World Threads
Concurrent
Threads
Maintenance Barriers Maintenance Barriers
GC
Throughput Maximizer
8. A full compacting GC:
• Can get unpredictable and cause stalls that can cause you to miss delivering on your
system level objectives!
• May not scale well when your application has higher promotion rates with lots of
transients
In-order to be able to scale well and add some predictability, we need to
add:
• Regionalized heap &
• Partial compaction with concurrent marking
But I Can’t Deal With Those Long Pauses
9. Latency Sensitive
Generational Parallel Work
Partial
Compaction
Concurrent
Marking
Let’s Design a Latency Sensitive GC
Regionalized Heap
Young Old
Maintenance Barriers Maintenance Barriers
10. So, you need a predictable, scalable, low-latency GC?
• Keep the partial compaction, but make it concurrent
• Set time budgets on the STW phases
• Fall back to concurrent work once time budget is exceeded
• Repeat incremental concurrent work and time budgeted STW phases until work is
done.
• Elicit application threads to help with concurrent compaction aka relocation work – this
is known as ‘self-healing’
But I Can’t Deal With Those Long Pauses Tail Latencies
11. Low-Latency Sensitive
Generational Parallel Work
Let’s Design a Low-Latency Sensitive GC
Regionalized Heap
Partial/Incremental
Concurrent
Marking
Compaction
Self Healing
Young Old
Maintenance Barriers
Maintenance Barriers
Generational
Young Old
Maintenance Barriers
Z GC
Not There
Yet
13. ZGC is an adaptive, near-real-time, scalable, predictable low-latency
collector
• It can guarantee sub milliseconds pause times
• The GC pause doesn’t increase with the application heap, live dataset or
the root set sizes
• It can span heap sizes from 8MBs up to 16TBs!
• It works concurrently with your application and strives to not let the
application throughput fall below 15%!
Z GC Design Goals
14. Z GC Core Concept – Colored Pointers
http://cr.openjdk.java.net/~pliden/slides/ZGC-Jfokus-2018.pdf
Object Address
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63
Object is known to
be marked?
Object is known to
not be pointing into
the relocation set?
Object is reachable
only through a
Finalizer?
Metadata stores in the unused bits of the 64-bit pointers
Virtual address mapping/tagging
Multi-mapping on x86-64, aarch64
18. Barriers – Loaded Reference Barrier
• Update a “bad” reference to a “good” reference
• Can be self-healing/repairing barrier when updating the source memory
location
• Imposes a set of invariants –
• “All visible loaded reference values will be safely “marked through” by the
collector, if they haven’t been already.
• All visible loaded reference values point to the current location of the
safely accessible contents of the target objects they refer to.”
Tene, G.; Iyengar, B. & Wolf, M. (2011), C4: The Continuously Concurrent Compacting Collector, in 'Proceedings
of the international symposium on Memory management' , ACM, New York, NY, USA , pp. 79--88 .
19. Concurrent Compaction
Load barrier to detect object pointers into the collection set
Can be self-healing
Off-heap forwarding tables enable to immediately release and reuse
virtual and physical memory
http://cr.openjdk.java.net/~pliden/slides/ZGC-Jfokus-2018.pdf
21. In-order to provide more near-real-time control over the pauses, Z GC has provided many
improvements to OpenJDK HotSpot. Here’s a list of few of the optimizations:
• Thread local handshakes – JDK 10
• Load barriers and colored pointers – JDK 11
• Concurrent reference processing – JDK 11
• Concurrent class unloading – JDK 12
• Uncommit unused memory – JDK 13
• Windows and macOS support; Parallel PreTouch – JDK 14
• Compressed class pointers – JDK 15
• Concurrent Thread Stack Scanning – JDK 16
• Extended AArch64 support – JDK 16 (Windows), JDK 17 (macOS)
Z GC Is Production Ready in JDK 15
22. Thread Local Handshakes vs Global STW
Application Threads Application Threads
Safepoint
Requested
GC
Completed
Application Threads GC Threads Application Threads
Safepoint
Requested
GC
Completed
Handshakes
Time To Safepoint
(TTSP)
GC Threads
31. Under Pressure : High Allocation Rate, Short-Lived Objects
Live At Mark Start Live At Relocation End
32. Reasons For Triggering a GC Cycle
Cause Name Description
Timer When timer is up and if no other GC has been performed
yet.
Warmup Based on heap occupancy and if no other GC has been
performed yet.
Allocation Rate Based on high allocation rates and possibility to run out
of heap space
Allocation Stall Mutator blocked due to lack of heap space
Proactive To maintain lower heap sizes if occupancy increases by
10% since the last GC or 5 minutes have passes since.
High
Utilization
Avoid GC due to ‘Allocation Rate’ trigger by
preventatively trigger GC if heap is 95% occupied and
application has a low allocation rate
Count
Timer Warmup
Allocation Rate Allocation Stall
Proactive High Utilization
39. Under Pressure : High Allocation Rate, Medium-Lived Objects
Live At Mark Start Live At Relocation End
40. Reasons For Triggering a GC Cycle
Cause Name Description
Timer When timer is up and if no other GC has been performed
yet.
Warmup Based on heap occupancy and if no other GC has been
performed yet.
Allocation Rate Based on high allocation rates and possibility to run out
of heap space
Allocation Stall Mutator blocked due to lack of heap space
Proactive To maintain lower heap sizes if occupancy increases by
10% since the last GC or 5 minutes have passes since.
High
Utilization
Avoid GC due to ‘Allocation Rate’ trigger by
preventatively trigger GC if heap is 95% occupied and
application has a low allocation rate
Count
Timer Warmup
Allocation Rate Allocation Stall
Proactive High Utilization