This document discusses Linux file permissions and commands used to modify permissions. It explains the rwx permissions for owner, group, and other using an example ls -l output. It then covers the chmod, chown, and chgrp commands to change file ownership, group, and permissions including recursive (-R) options and using symbolic modes.
Exploring Parallel Merging In GPU Based Systems Using CUDA C.Rakib Hossain
We present a program that implemented to execute Adaptive merge sort algorithm in parallel on a GPU based system. Parallel implementation is used to get better performance than serial implementation in runtime perspective. Parallel implementation executes independent executable operation in parallel using large number of cores in GPU based system. Results from a parallel implementation of the algorithm is given and compared with its serial implementation on run time basis. The parallel version is implemented with CUDA platform in a system based on NVIDIA GPU (GTX 650)
Java Core | Understanding the Disruptor: a Beginner's Guide to Hardcore Concu...JAX London
2011-11-02 | 05:45 PM - 06:35 PM | Victoria
The Disruptor is new open-source concurrency framework, designed as a high performance mechanism for inter-thread messaging. It was developed at LMAX as part of our efforts to build the world's fastest financial exchange. Using the Disruptor as an example, this talk will explain of some of the more detailed and less understood areas of concurrency, such as memory barriers and cache coherency. These concepts are often regarded as scary complex magic only accessible by wizards like Doug Lea and Cliff Click. Our talk will try and demystify them and show that concurrency can be understood by us mere mortal programmers.
Распределенные системы хранения данных, особенности реализации DHT в проекте ...yaevents
В этом докладе будет описана система хранения данных Elliptics network, основной задачей которой является предоставление пользователям доступа к данным, расположенным на физически распределенных серверах с плоской адресной моделью в децентрализованном окружении. Распределенная система хранения данных, предоставляющая доступ к объекту по ключу (key/value storage), и в частности распределенная хэш-таблица (distributed hash table), является весьма эффективным решением с незначительным набором ограничений. Для подтверждения работоспособности данной идеи и функционала в докладе будет представлена практическая реализация распределенной хэш-таблицы с модульной системой хранения данных и различными системами доступа: от POSIX файловой системы до доступа по протоколу HTTP. Также мы обсудим ограничения, накладываемые технологией распределенной хэш таблицы, и сравним особенности высоконагруженного и высоконадежного доступа в ненадежной среде с классическими моделями, использующими централизованные системы. Опираясь на полученные практические результаты и гибкость реализованной системы, будут предложены способы решения поставленных задач и расширения функционала.
What's the great thing about a database? Why, it stores data of course! However, one feature that makes a database useful is the different data types that can be stored in it, and the breadth and sophistication of the data types in PostgreSQL is second-to-none, including some novel data types that do not exist in any other database software!
This talk will take an in-depth look at the special data types built right into PostgreSQL version 9.4, including:
* INET types
* UUIDs
* Geometries
* Arrays
* Ranges
* Document-based Data Types:
* Key-value store (hstore)
* JSON (text [JSON] & binary [JSONB])
We will also have some cleverly concocted examples to show how all of these data types can work together harmoniously.
Abstract: Nowadays it’s only a lazy one who haven’t written his own metric storage and aggregation system. I am lazy, and that’s why I have to choose what to use and how to use. I don’t want you to do the same job, so I decided to share my considerations concerning architectures and test results.
Exploring Parallel Merging In GPU Based Systems Using CUDA C.Rakib Hossain
We present a program that implemented to execute Adaptive merge sort algorithm in parallel on a GPU based system. Parallel implementation is used to get better performance than serial implementation in runtime perspective. Parallel implementation executes independent executable operation in parallel using large number of cores in GPU based system. Results from a parallel implementation of the algorithm is given and compared with its serial implementation on run time basis. The parallel version is implemented with CUDA platform in a system based on NVIDIA GPU (GTX 650)
Java Core | Understanding the Disruptor: a Beginner's Guide to Hardcore Concu...JAX London
2011-11-02 | 05:45 PM - 06:35 PM | Victoria
The Disruptor is new open-source concurrency framework, designed as a high performance mechanism for inter-thread messaging. It was developed at LMAX as part of our efforts to build the world's fastest financial exchange. Using the Disruptor as an example, this talk will explain of some of the more detailed and less understood areas of concurrency, such as memory barriers and cache coherency. These concepts are often regarded as scary complex magic only accessible by wizards like Doug Lea and Cliff Click. Our talk will try and demystify them and show that concurrency can be understood by us mere mortal programmers.
Распределенные системы хранения данных, особенности реализации DHT в проекте ...yaevents
В этом докладе будет описана система хранения данных Elliptics network, основной задачей которой является предоставление пользователям доступа к данным, расположенным на физически распределенных серверах с плоской адресной моделью в децентрализованном окружении. Распределенная система хранения данных, предоставляющая доступ к объекту по ключу (key/value storage), и в частности распределенная хэш-таблица (distributed hash table), является весьма эффективным решением с незначительным набором ограничений. Для подтверждения работоспособности данной идеи и функционала в докладе будет представлена практическая реализация распределенной хэш-таблицы с модульной системой хранения данных и различными системами доступа: от POSIX файловой системы до доступа по протоколу HTTP. Также мы обсудим ограничения, накладываемые технологией распределенной хэш таблицы, и сравним особенности высоконагруженного и высоконадежного доступа в ненадежной среде с классическими моделями, использующими централизованные системы. Опираясь на полученные практические результаты и гибкость реализованной системы, будут предложены способы решения поставленных задач и расширения функционала.
What's the great thing about a database? Why, it stores data of course! However, one feature that makes a database useful is the different data types that can be stored in it, and the breadth and sophistication of the data types in PostgreSQL is second-to-none, including some novel data types that do not exist in any other database software!
This talk will take an in-depth look at the special data types built right into PostgreSQL version 9.4, including:
* INET types
* UUIDs
* Geometries
* Arrays
* Ranges
* Document-based Data Types:
* Key-value store (hstore)
* JSON (text [JSON] & binary [JSONB])
We will also have some cleverly concocted examples to show how all of these data types can work together harmoniously.
Abstract: Nowadays it’s only a lazy one who haven’t written his own metric storage and aggregation system. I am lazy, and that’s why I have to choose what to use and how to use. I don’t want you to do the same job, so I decided to share my considerations concerning architectures and test results.
Abstract: At DataRobot we deal with automation challenges every day. This talk will give insight into how we use Python tools built around Ansible, Terraform, and Docker to solve real-world problems in infrastructure and automation.
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
We present some of the lessons learned while designing faster indexes, with a particular emphasis on compressed bitmap indexes. Compressed bitmap indexes accelerate queries in popular systems such as Apache Spark, Git, Elastic, Druid and Apache Kylin.
Scaling infrastructure is tricky,
I will try to explain what methods I use when dealing with this issue, and demonstrate an approach which can be applied to almost any type of work load.
Apply Hammer Directly to Thumb; Avoiding Apache Spark and Cassandra AntiPatt...Databricks
Learn from someone who has made just about every basic Apache Spark mistake possible so you don’t have to! We’ll go over some of the most common things that users do that end up doing that cause unnecessary pain and actually explain how to avoid them.
Confused about serialization? Not sure what is meant by use a singleton to share connections? Together we will walk through concrete examples of how to handle these situation. Learn how to: do all your work remotely, not break your catalyst optimizations, use all your resources, and much more! Together lets learn how to make our Spark Applications better!
Weather of the Century: Design and PerformanceMongoDB
This talk walks you through how you can use MongoDB to store and analyze worldwide weather data from the entire 20th century in a graphical application.
Accelerating Local Search with PostgreSQL (KNN-Search)Jonathan Katz
KNN-GiST indexes were added in PostgreSQL 9.1 and greatly accelerate some common queries in the geospatial and textual search realms. This presentation will demonstrate the power of KNN-GiST indexes on geospatial and text searching queries, but also their present limitations through some of my experimentations. I will also discuss some of the theory behind KNN (k-nearest neighbor) as well as some of the applications this feature can be applied too.
To see a version of the talk given at PostgresOpen 2011, please visit http://www.youtube.com/watch?v=N-MD08QqGEM
Extending Spark SQL API with Easier to Use Array Types Operations with Marek ...Databricks
Big companies typically integrate their data from various heterogeneous systems when building a data lake as single point for accessing data. To achieve this goal technical teams often deal with data defined by complex schemas and various data formats. Spark SQL Datasets are currently compatible with data formats such as XML, Avro and Parquet by providing primitive and complex data types such as structs and arrays.
Although Dataset API offers rich set of functions, general manipulation of array and deeply nested data structures is lacking. We will demonstrate this fact by providing examples of data which is currently very hard to process in Spark efficiently. We designed and developed an extension of Dataset API to allow developers to work with array and complex type elements in a more straightforward and consistent way. The extension should help users dealing with complex and structured big data to use Apache Spark as a truly generic processing framework.
Most file systems have methods to assign permissions or access rights to specific users and groups of users.
These system control the ability of the users to view, change, navigate, and execute the contents of the file system.
Permissions on the linux- systems are managed in three distinct scopes or classes. Theses scopes are known as users, groups or others.
Abstract: At DataRobot we deal with automation challenges every day. This talk will give insight into how we use Python tools built around Ansible, Terraform, and Docker to solve real-world problems in infrastructure and automation.
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
We present some of the lessons learned while designing faster indexes, with a particular emphasis on compressed bitmap indexes. Compressed bitmap indexes accelerate queries in popular systems such as Apache Spark, Git, Elastic, Druid and Apache Kylin.
Scaling infrastructure is tricky,
I will try to explain what methods I use when dealing with this issue, and demonstrate an approach which can be applied to almost any type of work load.
Apply Hammer Directly to Thumb; Avoiding Apache Spark and Cassandra AntiPatt...Databricks
Learn from someone who has made just about every basic Apache Spark mistake possible so you don’t have to! We’ll go over some of the most common things that users do that end up doing that cause unnecessary pain and actually explain how to avoid them.
Confused about serialization? Not sure what is meant by use a singleton to share connections? Together we will walk through concrete examples of how to handle these situation. Learn how to: do all your work remotely, not break your catalyst optimizations, use all your resources, and much more! Together lets learn how to make our Spark Applications better!
Weather of the Century: Design and PerformanceMongoDB
This talk walks you through how you can use MongoDB to store and analyze worldwide weather data from the entire 20th century in a graphical application.
Accelerating Local Search with PostgreSQL (KNN-Search)Jonathan Katz
KNN-GiST indexes were added in PostgreSQL 9.1 and greatly accelerate some common queries in the geospatial and textual search realms. This presentation will demonstrate the power of KNN-GiST indexes on geospatial and text searching queries, but also their present limitations through some of my experimentations. I will also discuss some of the theory behind KNN (k-nearest neighbor) as well as some of the applications this feature can be applied too.
To see a version of the talk given at PostgresOpen 2011, please visit http://www.youtube.com/watch?v=N-MD08QqGEM
Extending Spark SQL API with Easier to Use Array Types Operations with Marek ...Databricks
Big companies typically integrate their data from various heterogeneous systems when building a data lake as single point for accessing data. To achieve this goal technical teams often deal with data defined by complex schemas and various data formats. Spark SQL Datasets are currently compatible with data formats such as XML, Avro and Parquet by providing primitive and complex data types such as structs and arrays.
Although Dataset API offers rich set of functions, general manipulation of array and deeply nested data structures is lacking. We will demonstrate this fact by providing examples of data which is currently very hard to process in Spark efficiently. We designed and developed an extension of Dataset API to allow developers to work with array and complex type elements in a more straightforward and consistent way. The extension should help users dealing with complex and structured big data to use Apache Spark as a truly generic processing framework.
Most file systems have methods to assign permissions or access rights to specific users and groups of users.
These system control the ability of the users to view, change, navigate, and execute the contents of the file system.
Permissions on the linux- systems are managed in three distinct scopes or classes. Theses scopes are known as users, groups or others.
This Presentation help you to start your first three programs on Linux. all the codes are taken from another book, and resources. I just make it together.
Weird things we've seen with OpenStack NeutronNick Jones
A presentation given at the Manchester OpenStack Meetup, talking through some of the odd things we've hit up against in our time as a public OpenStack operator using Neuton with OpenvSwitch.
Oracle Enterprise Manager Cloud Control 12c: how to solve 'ERROR: NMO Not Set...Marco Vigelini
Oracle Enterprise Manager Cloud Control 12c:
How to solve the error 'ERROR: NMO Not Setuid-root (Unix-only)' on Oracle Enterprise Manager Cloud Control while contacting the EM Agent
Abusing Microsoft Kerberos - Sorry you guys don't get itBenjamin Delpy
Talk of Skip Duckwall and I at BlackHat 2014 USA / Defcon Wall of Sheep.
Kerberos, and new pass-the-* feature, like overpass-the-hash and the Golden Ticket
Aaron Mildenstein - Using Logstash with ZabbixZabbix
Logstash is a terrific tool for capturing, filtering, parsing and enriching data from a number of sources—including logs, of course. But Logstash is also able to capture from many other sources, including social media streams, databases, and many more. Data streams like these are a potential gold mine for Zabbix trending and alerting of all kinds.
In this talk Aaron Mildensten will provide an overview of how to configure and integrate Logstash with Zabbix to:
* capture data
* parse data events into key/value pairs
* associate an event with the time-stamp provided by the data
* generate metrics from the data
* output these values to Zabbix, with the associated time-stamp
Zabbix Conference 2015
"Containers wrap up software with all its dependencies in packages that can be executed anywhere. This can be specially useful in HPC environments where, often, getting the right combination of software tools to build applications is a daunting task. However, typical container solutions such as Docker are not a perfect fit for HPC environments. Instead, Shifter is a better fit as it has been built from the ground up with HPC in mind. In this talk, we show you what Shifter is and how to leverage from the current Docker environment to run your applications with Shifter."
Watch the video presentation: http://wp.me/p3RLHQ-f81
See more talks in the Switzerland HPC Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Splunk talk at the AWS Big Data Meetup in Palo Alto on Nov 17 2015stevemcpherson
Ledion Bitincka from Splunk spoke at the AWS Big Data Meetup in Palo Alto and give an overview of Splunk’s processing pipeline topology and explained their approach to indexing data at scale.
Introduction to Facebook JavaScript & Python SDKColin Su
This is a workshop for teaching people building Facebook app with its JavaScript & Python SDK, and also included a code lab to let people do it in real
Nested List Comprehension and Binary SearchColin Su
Introduction to:
- Nested List Comprehension
- Binary Search implementation with Python
Python Programming for Non-programmer
Department of Computer Science, NCCU
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!