For More information, refer to Java EE 7 performance tuning and optimization book:
The book is published by Packt Publishing:
http://www.packtpub.com/java-ee-7-performance-tuning-and-optimization/book
It is easy to monitor the performance of JVM if one knows how GC and Threads work in JVM. This presentation throws light on Collector types, HotSpot Collection Algorithms, Thread Monitoring, Method Profiling and Heap Profiling
It is easy to monitor the performance of JVM if one knows how GC and Threads work in JVM. This presentation throws light on Collector types, HotSpot Collection Algorithms, Thread Monitoring, Method Profiling and Heap Profiling
Performance has always been a major concern in software development and should not be taken lightly even when commodity computers have multicore CPUs and a few gigabytes of RAM. One of the most handy, simple tools for performance testing are microbenchmarks. Unfortunately, developing correct Java microbenchmarks is a complex task with many pitfalls on the way. This presentation is about the Do's and Don'ts of Java microbenchmarking and about what tools are out there to help with this tricky task.
Java Colombo Meetup on 22nd March 2018
Speaker: Isuru Perera, Technical Lead at WSO2
Flame graphs are a visualization of profiled software and it was developed by Brendan Gregg, an industry expert in computing performance and cloud computing. Finding out why CPUs are busy is an important task when troubleshooting performance issues and we often use a sampling profiler to see which code-paths are hot. However, a profiler will dump a lot of data with thousands of lines and it is not easy to go through all data. With Flame Graphs, we can identify the most frequent code-paths quickly and accurately. Basically, a Flame Graph can simply visualize the stack traces output of a sampling profiler.
There are many ways to profile Java applications and Java Flight Recorder (JFR) is a really good tool to profile a Java application with a very low overhead. I will show how we can generate a Flame Graph from a Java Flight Recording using the JFR Flame Graph tool (https://github.com/chrishantha/jfr-flame-graph) I developed.
Since Flame Graphs can visualize any stack profiles, we can also use a Linux system profiler (perf) and create a Java Mixed-Mode Flame Graph, which will show how much CPU time is spent in Java methods, system libraries and the kernel. We can troubleshoot performance issues related to high CPU usage easily with a flame graph showing profile information from both system code paths and Java code paths. I will discuss how we can use the -XX:+PreserveFramePointer option in JDK and the perf system profiler to generate a Java Mixed-mode flame graph.
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
Multithreading and concurrency in androidRakesh Jha
Here you will learn -
What is Multithreading
What is concurrency
Process Vs Thread
Improvements and issues with concurrency
Limits of concurrency gains
Concurrency issues
Threads pools with the Executor Framework
AsyncTask and the UI Thread
Code
Every Java developer knows that multithreading is the root of all evil and it is quite hard to write correct code for concurrent environment. But what tasks do exist in real commercial development except running code in asynchronous way?
In this talk I will present several tasks from my real projects and solutions we designed for them. This talk is very application oriented and allows participants to extend their vision of concurrent programming.
Slides from tech talk about the art of non-blocking waiting in Java with LockSupport.park/unpark and AbstractQueuedSynchronizer. Presented on JPoint 2016 Conference.
Inside the JVM - Follow the white rabbit! / Breizh JUGSylvain Wallez
Presentation given at the Rennes (FR) Java User Group in Feb 2019.
How do we go from your Java code to the CPU assembly that actually runs it? Using high level constructs has made us forget what happens behind the scenes, which is however key to write efficient code.
Starting from a few lines of Java, we explore the different layers that constribute to running your code: JRE, byte code, structure of the OpenJDK virtual machine, HotSpot, intrinsic methds, benchmarking.
An introductory presentation to these low-level concerns, based on the practical use case of optimizing 6 lines of code, so that hopefully you to want to explore further!
The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!
Performance has always been a major concern in software development and should not be taken lightly even when commodity computers have multicore CPUs and a few gigabytes of RAM. One of the most handy, simple tools for performance testing are microbenchmarks. Unfortunately, developing correct Java microbenchmarks is a complex task with many pitfalls on the way. This presentation is about the Do's and Don'ts of Java microbenchmarking and about what tools are out there to help with this tricky task.
Java Colombo Meetup on 22nd March 2018
Speaker: Isuru Perera, Technical Lead at WSO2
Flame graphs are a visualization of profiled software and it was developed by Brendan Gregg, an industry expert in computing performance and cloud computing. Finding out why CPUs are busy is an important task when troubleshooting performance issues and we often use a sampling profiler to see which code-paths are hot. However, a profiler will dump a lot of data with thousands of lines and it is not easy to go through all data. With Flame Graphs, we can identify the most frequent code-paths quickly and accurately. Basically, a Flame Graph can simply visualize the stack traces output of a sampling profiler.
There are many ways to profile Java applications and Java Flight Recorder (JFR) is a really good tool to profile a Java application with a very low overhead. I will show how we can generate a Flame Graph from a Java Flight Recording using the JFR Flame Graph tool (https://github.com/chrishantha/jfr-flame-graph) I developed.
Since Flame Graphs can visualize any stack profiles, we can also use a Linux system profiler (perf) and create a Java Mixed-Mode Flame Graph, which will show how much CPU time is spent in Java methods, system libraries and the kernel. We can troubleshoot performance issues related to high CPU usage easily with a flame graph showing profile information from both system code paths and Java code paths. I will discuss how we can use the -XX:+PreserveFramePointer option in JDK and the perf system profiler to generate a Java Mixed-mode flame graph.
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
Multithreading and concurrency in androidRakesh Jha
Here you will learn -
What is Multithreading
What is concurrency
Process Vs Thread
Improvements and issues with concurrency
Limits of concurrency gains
Concurrency issues
Threads pools with the Executor Framework
AsyncTask and the UI Thread
Code
Every Java developer knows that multithreading is the root of all evil and it is quite hard to write correct code for concurrent environment. But what tasks do exist in real commercial development except running code in asynchronous way?
In this talk I will present several tasks from my real projects and solutions we designed for them. This talk is very application oriented and allows participants to extend their vision of concurrent programming.
Slides from tech talk about the art of non-blocking waiting in Java with LockSupport.park/unpark and AbstractQueuedSynchronizer. Presented on JPoint 2016 Conference.
Inside the JVM - Follow the white rabbit! / Breizh JUGSylvain Wallez
Presentation given at the Rennes (FR) Java User Group in Feb 2019.
How do we go from your Java code to the CPU assembly that actually runs it? Using high level constructs has made us forget what happens behind the scenes, which is however key to write efficient code.
Starting from a few lines of Java, we explore the different layers that constribute to running your code: JRE, byte code, structure of the OpenJDK virtual machine, HotSpot, intrinsic methds, benchmarking.
An introductory presentation to these low-level concerns, based on the practical use case of optimizing 6 lines of code, so that hopefully you to want to explore further!
The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!
This is a talk I did for JavaOne 2009. The focus of the talk was memory management and system monitoring with freely available tools that are in the jdk or open source.
The main body of work related to supporting dynamic languages on the JVM at Oracle today is done within the Nashorn project. While on the surface it looks like we're busy creating a JavaScript runtime, in reality JavaScript is only the beginning, and not the ultimate goal. Nashorn has served as the proving ground for new approaches for implementing a dynamic language on top of the JVM, and we're eager to – once solidified – crystallize these into a reusable dynamic language implementer's toolkit. We have faced challenges of optimally mapping JavaScript local variables to JVM types (or: "hey, there's a static type inference algorithm in your dynamic language compiler"), doing liveness analysis, cutting up methods too large to fit into a single JVM method, efficiently representing large array and object literals in compiled code, creating a system for on-demand compilation of several type-specialized variants of the same function, and more. Along the way, we have reached the limits of our initial internal representation (fun fact: you can't do liveness analysis on an AST. We learned it the hard way.) and started sketching up an intermediate representation that would be easy to emit from a dynamic language compiler, and that could be taken over by a toolchain to perform the operations described above then on it and finally output standard Java bytecode for JIT to take over. Elevator pitch: like LLVM, but for dynamic languages on the JVM.
Efficient Memory and Thread Management in Highly Parallel Java Applicationspkoza
This presentation discusses strategies to estimate and control the memory use of multi-threaded java applications. It includes a quick overview of how the JVM uses memory, followed by techniques to estimate the memory usage of various types of objects during testing. This knowledge is then used as the basis for a runtime scheme to estimate and control the memory use of multiple threads. The final part of the presentation describes how to implement robust handling for unchecked exceptions, especially Out Of Memory (OOM) errors, and how to ensure threads stop properly when unexpected events occur.
For More information, refer to Java EE 7 performance tuning and optimization book:
The book is published by Packt Publishing:
http://www.packtpub.com/java-ee-7-performance-tuning-and-optimization/book
Vortragsreihe Dortmund: Unified Development EnvironmentsThorsten Kamann
Große Entwicklungsabteilungen stehen oft vor dem Problem einheitlicher Entwicklungsprozesse und Werkzeuge. Nach einiger Zeit hat jedes Projekt eigene Prozesse und Werkzeuge etabliert. Dies ist nicht im Sinne der Entwicklungsabteilung. Softwaresysteme müssen i. d. R. über Jahre hinweg gewartet und erweitert werden - oft von einem Team, das sich neu in die Anwendung einarbeiten muss.
Nicht selten stellt die Rekonstruktion der Entwicklungsumgebung einen erheblichen Aufwand dar.
Dieser Vortrag beschreibt - anhand eines Erfahrungsberichts - den Aufbau einer strukturierten Entwicklungsumgebung, die auch für grosse Entwicklungsabteilungen skaliert.
- Zentrale Projekt- und Codeverwaltung (ähnlich wie Sourceforge)
- Buildmanagement mit Maven
- Entwicklungswerkzeuge basierend auf Maven und Eclipse
- Installierbare Teamserver mit Virtualisierungstechnologie für Continuous Integration
Efficient Memory and Thread Management in Highly Parallel Java ApplicationsPhillip Koza
This presentation discusses strategies to estimate and control the memory use of multi-threaded java applications. It includes a quick overview of how the JVM uses memory, followed by techniques to estimate the memory usage of various types of objects during testing. This knowledge is then used as the basis for a runtime scheme to estimate and control the memory use of multiple threads. The final part of the presentation describes how to implement robust handling for unchecked exceptions, especially Out Of Memory (OOM) errors, and how to ensure threads stop properly when unexpected events occur.
The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!
JetBrains Day Seoul - Exploring .NET’s memory management – a trip down memory...Maarten Balliauw
The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!
Looming Marvelous - Virtual Threads in Java Javaland.pdfjexp
Nowadays we have 2 options for concurrency in Java:
* simple, synchronous, blocking code with limited scalability that tracks well linearly at runtime, or.
* complex, asynchronous libraries with high scalability that are harder to handle.
Project Loom aims to bring together the best aspects of these two approaches and make them available to developers.
In the talk, I'll briefly cover the history and challenges of concurrency in Java before we dive into Loom's approaches and do some behind-the-scenes implementation. To manage so many threads reasonably needs some structure - for this there are proposals for "Structured Concurrency" which we will also look at. Some examples and comparisons to test Loom will round up the talk.
Project Loom is included in Java 19 and 20 as a preview feature, it can already be tested how well it works with our applications and libraries.
Spoiler: Pretty good.
Quantifying the Performance of Garbage Collection vs. Explicit Memory ManagementEmery Berger
This talk answers an age-old question: is garbage collection faster/slower/the same speed as malloc/free? We introduce oracular memory management, an approach that lets us measure unaltered Java programs as if they used malloc and free. The result: a good GC can match the performance of a good allocator, but it takes 5X more space. If physical memory is tight, however, conventional garbage collectors suffer an order-of-magnitude performance penalty.
This session is all about - the mechanism provided by Java Virtual Machine to reclaim heap space from objects which are eligible for Garbage collection.
Similar to Profiler Guided Java Performance Tuning (20)
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.
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
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
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.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
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.
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."
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Top 7 Unique WhatsApp API Benefits | Saudi ArabiaYara Milbes
Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience.
In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
4. Performance is one of the NFRs.
Usually you have SLA for each transaction.
2 types of performance issues :
◦ Performance Testing Results
In Dev or Test environment.
◦ Production Performance Issues
Difficult to handle.
5. Problem Definition (UC, Scenario, Conditions,
User, ..etc…)
Gather Information
Try to replicate (if possible)
Get all tools ready to use.
Build your plan:
◦ Analyze Tools output.
◦ Code Inspection
◦ Potential fixes. (Google it …)
◦ Re-test.
6. Better if :
◦ Relay on tools output.
◦ Less dependant on personal experience.
◦ Concrete (not abstract)
◦ Always comparative.
◦ Quick POC
◦ Proven from Google
Better if not:
◦ Trial and error approach.
◦ Optimize as you go.
7. Hardware
◦ CPU
◦ Network
◦ Memory
◦ Storage
Software
◦ Operating System
◦ Libraries, Drivers and Utilities.
◦ Application
8. CPU :
◦ Detect root cause (anti-virus!)
◦ Change algorithm
◦ Increase CPU power.
Network :
◦ Detect root cause (OS updates!)
◦ Change architecture.
Memory :
◦ Root cause (memory leakage)
◦ add more memory, re-structure caching.
Storage :
◦ Add storage, free more space (archive) , etc.
9. Good but sometimes you can consider:
◦ CPU :
Use MT.
Change workflow.
◦ Memory :
Utilize more memory in caching.
Change architecture.
10. Google it.
Continuous follow-up is essential , as new
tips always come:
◦ Use StringBuffer rather than the string
concatenation operator (+).
◦ Use primitive data types instead of objects.
◦ Use short-circuit boolean operators whenever
possible.
◦ Flatten objects as much as possible.
◦ Use the clone() method to avoid calling any
constructors.
◦ Don’t use exception to return flag.
11. Vector, Stack, Hashtable are deprecated
For single threaded use :
◦ ArrayList
◦ Deque
◦ HashMap
For MT use : (a lot of other alternatives)
◦ CopyOnWriteArrayList
◦ ConcurrentLinkedDeque
◦ ConcurrentHashMap
14. Has a self-contained execution environment.
A process generally has a complete, private
set of basic run-time resources; in particular,
each process has its own memory space.
Most operating systems support Inter Process
Communication (IPC) resources, such as pipes
and sockets
Most implementations of the Java virtual
machine run as a single process.
A Java application can create additional
processes using a ProcessBuilder object.
15. As simple as :
Process pb = new
ProcessBuilder("myCommand",
"myArg").start();
But can be more complex by defining the
Input, Output , Error streams or inherit them
using: pb.inheritIO()
public Process start() throws IOException
16. Both processes and threads provide an
execution environment, but creating a new
thread requires fewer resources than creating
a new process.
Threads exist within a process — every
process has at least one.
Threads share the process's resources,
including memory and open files.
This makes for efficient, but potentially
problematic, communication.
17. Every application has at least one thread — or
several, if you count "system" threads ( like
memory management ).
But from the application programmer's point
of view, you start with just one thread, called
the main thread.
This thread has the ability to create additional
threads.
18. Using the Interface or extending the Class :
public class HelloRunnable implements Runnable {
public void run() {
System.out.println("Hello!");
}
public static void main(String args[]) {
(new Thread(new HelloRunnable())).start();
}
}
19. Each object in Java is associated with a
monitor, which a thread can lock or unlock.
Only one thread at a time may hold a lock on
a monitor.
A synchronized statement :
◦ It then attempts to perform a lock action on that
object's monitor and does not proceed further until
the lock action has successfully.
20. A synchronized method automatically
performs a lock action when it is invoked;
◦ Its body is not executed until the lock action has
successfully completed.
◦ If the method is an instance method :
It locks the monitor associated with the instance for
which it was invoked (this).
◦ If the method is static :
It locks the monitor associated with the Class object
that represents the class in which the method is
defined.
21.
22. Use Generational Collection
◦ Memory is divided into generations, that is,
separate pools holding objects of different ages.
23. A garbage collector is responsible for
◦ Allocating memory
◦ Ensuring that any referenced objects remain in
memory
◦ Recovering memory used by objects that are no
longer reachable from references in executing code.
24. Serial versus Parallel
◦ When parallel collection is used, the task of garbage
collection is split into parts and those subparts are
executed simultaneously, on different CPUs.
Concurrent versus Stop-the-world
◦ Concurrent need extra care, as it is operating over
objects that might be updated at the same time by the
application.
◦ Adds some overhead and requires a larger heap size.
◦ Stop-the-world garbage collection is simpler since the
heap is frozen and objects are not changing during the
collection.
◦ It may be undesirable for some applications to be
paused.
25. Compacting versus Non-compacting
◦ Make it easy and fast to allocate a new object at
the first free location (One pointer is enough)
◦ Non-compacting collector releases the space
utilized by garbage objects in-place.
◦ Faster completion of garbage collection, but the
drawback is potential fragmentation. (Need array of
pointers)
◦ In general, it is more expensive to allocate from a
heap with in-place deallocation than from a
compacted heap.
26. Most objects are initially allocated in Eden.
◦ A few large objects may be allocated directly in the
old generation
The survivor spaces hold objects that have
survived at least one young generation
collection
◦ i.e. given additional chances to die before being
considered “old enough” to be promoted to the old
generation.
27. Both young and old collections are done
serially (using a single CPU), in a stop-the
world fashion.
Application execution is halted while
collection is taking place
28.
29.
30. The collector then performs sliding
compaction, sliding the live objects towards
the beginning of the old generation space,
leaving any free space in a single contiguous
chunk at the opposite end.
(mark-sweep-compact collection algorithm)
35. -verbose:gc
[GC 325816K->83372K(776768K), 0.2454258 secs]
[Full GC 267628K->83769K(776768K), 1.8479984 secs]
[GC (1)->(2)(3), (4) secs]
(1->2) Combined size of live objects before and
after garbage collection.
(3) Amount of space usable for java objects
without requesting more memory from the
operating system.
(4) time taken to perform GC.
-XX:+PrintGCDetails : print more details
-XX:+PrintGCTimeStamps : print timestamp
36. Variant :
◦ Java heap space / Requested array size exceeds VM limit
= heap size issue
◦ PermGen space = no memory for creating new class.
◦ unable to create new native thread / <reason>
<stacktrace> (Native method) = no memory available for
allocation of Thread (native stacktrace)
◦ request <size> bytes for <reason>. Out of swap space?
= no memory left in OS.
Doesn’t mean no memory left :
◦ If >98% of the total time is spent in GC and only less
than 2% of the heap is recovered.
◦ Adding element to Array require new Array creation, and
no enough space in any generation.
39. Location: Local or Remote.
GUI: Online or Offline.
Time: Attach or started for profiling.
CPU: Sampled or Instrumented
Classes: Filtered or not filtered.
Type : Web Server or Standalone.
etc..
40. We will try 3 profilers:
◦ NetBeans Profiler
◦ JProfiler
◦ Eclipse TPTP
54. Add triggers to define what to record and to
save the snapshots..
55. The session is added to configuration file
with “id” example :
◦ <session id="119"
◦ ….
◦ </session>
Now in run command add the following:
-
agentpath:D:PROGRA~1JPROFI~1binwind
owsjprofilerti.dll=offline,id=119;
60. For More information refer to Java EE 7
performance tuning and optimization book.
The book is published by Packt Publishing.
◦ http://www.packtpub.com/java-ee-7-
performance-tuning-and-optimization/book
◦ http://www.amazon.com/dp/178217642X/?tag=pa
cktpubli-20
◦ http://www.amazon.co.uk/dp/178217642X/?tag=p
acktpubli-21