This document provides an introduction to cloud computing and parallel/distributed processing through a lecture on the topic. It discusses web-scale problems involving large amounts of data, the trend toward large centralized data centers, and different computing models like utility computing. It also introduces concepts like virtualization, MapReduce, and designing highly interactive web applications. The lecture covers challenges in parallelization like assigning work and synchronizing workers. It provides examples of patterns for parallelism like master/slave and producer/consumer models.
This document provides an introduction to cloud computing concepts through a lecture on the topic. It discusses how web-scale problems involving large amounts of data require distributed processing across large data centers. It also outlines different cloud computing models including utility computing, platform as a service (PaaS), and software as a service (SaaS). The lecture introduces concepts like MapReduce and AJAX that enable distributed and interactive web applications in the cloud.
This document provides an introduction to cloud computing and parallel/distributed processing. It discusses how web-scale problems involving large amounts of data require distributed computing across large data centers. Different models of cloud computing are described, including utility computing, platform as a service (PaaS), and software as a service (SaaS). The document also discusses how web applications are moving to highly-interactive models enabled by technologies like AJAX. Key aspects of cloud computing covered include large data centers, virtualization, MapReduce processing for large datasets, and interactive web applications.
This document provides an introduction to cloud computing concepts through a lecture on the topic. It discusses how web-scale problems involving large amounts of data require distributed processing across large data centers. It also outlines different cloud computing models including utility computing, platform as a service (PaaS), and software as a service (SaaS). The lecture introduces concepts like MapReduce and AJAX that enable distributed and interactive web applications in the cloud.
Hector Guerrero- Road to Business AnalyticsErika Marr
This document provides an overview of key concepts in business analytics including:
- Definitions of data science, data scientist, and analytics which involve extracting insights from data.
- A process map of data science including data collection, cleaning, modeling, and communication.
- A brief history and timeline of developments in computer technology, statistics, and analytics from the 1960s to present.
- Emerging areas like artificial intelligence, autonomous systems, and the impact of technology on jobs and society.
According to experts, yoga creates a deep impact not only on your physical and mental forms but also on all aspects of your life, including your behavior and thoughts.
Barrett began in 1941 as a wool warehouse and over decades expanded its expertise and facilities to support various industries. It developed logistics expertise in textiles, consumer products, and industrial products. Barrett implemented warehouse management systems to improve operations and opened several dedicated contract operations and facilities for major retailers and manufacturers. Its goal is to optimize customers' supply chains and financial performance through continuous improvement processes.
Paes Andrano - Bozza - Piano d’Azione per l’Energia SostenibileComune di Andrano
Bozza del Paes di - Piano d’Azione per l’Energia Sostenibile
C'è tempo fino al 27 luglio per inviare osservazioni o proposte di integrazione a:
Ufficio Tecnico Comunale - tel. 0836/1900997 - fax.0836/926032 - e-mail: a.urso@comune.andrano.le.it
This document provides an introduction to cloud computing concepts through a lecture on the topic. It discusses how web-scale problems involving large amounts of data require distributed processing across large data centers. It also outlines different cloud computing models including utility computing, platform as a service (PaaS), and software as a service (SaaS). The lecture introduces concepts like MapReduce and AJAX that enable distributed and interactive web applications in the cloud.
This document provides an introduction to cloud computing and parallel/distributed processing. It discusses how web-scale problems involving large amounts of data require distributed computing across large data centers. Different models of cloud computing are described, including utility computing, platform as a service (PaaS), and software as a service (SaaS). The document also discusses how web applications are moving to highly-interactive models enabled by technologies like AJAX. Key aspects of cloud computing covered include large data centers, virtualization, MapReduce processing for large datasets, and interactive web applications.
This document provides an introduction to cloud computing concepts through a lecture on the topic. It discusses how web-scale problems involving large amounts of data require distributed processing across large data centers. It also outlines different cloud computing models including utility computing, platform as a service (PaaS), and software as a service (SaaS). The lecture introduces concepts like MapReduce and AJAX that enable distributed and interactive web applications in the cloud.
Hector Guerrero- Road to Business AnalyticsErika Marr
This document provides an overview of key concepts in business analytics including:
- Definitions of data science, data scientist, and analytics which involve extracting insights from data.
- A process map of data science including data collection, cleaning, modeling, and communication.
- A brief history and timeline of developments in computer technology, statistics, and analytics from the 1960s to present.
- Emerging areas like artificial intelligence, autonomous systems, and the impact of technology on jobs and society.
According to experts, yoga creates a deep impact not only on your physical and mental forms but also on all aspects of your life, including your behavior and thoughts.
Barrett began in 1941 as a wool warehouse and over decades expanded its expertise and facilities to support various industries. It developed logistics expertise in textiles, consumer products, and industrial products. Barrett implemented warehouse management systems to improve operations and opened several dedicated contract operations and facilities for major retailers and manufacturers. Its goal is to optimize customers' supply chains and financial performance through continuous improvement processes.
Paes Andrano - Bozza - Piano d’Azione per l’Energia SostenibileComune di Andrano
Bozza del Paes di - Piano d’Azione per l’Energia Sostenibile
C'è tempo fino al 27 luglio per inviare osservazioni o proposte di integrazione a:
Ufficio Tecnico Comunale - tel. 0836/1900997 - fax.0836/926032 - e-mail: a.urso@comune.andrano.le.it
Chinese culture is very rich in nature in terms of colors and calligraphic patterns. Chinese food boxes portray the same tradition in their style and design
.
The Assembly Information Management System (AIMS) is a digital system used by the Northern Ireland Assembly that consists of several modules like meetings, agendas, witnesses, and reports. It allows information to be created once and published in multiple places. The AIMS portal provides a central public area for viewing and searching all procedural business of the Assembly. It also includes separate search options for different procedures and functions like questions, plenary sessions, committees, and indexing. Future plans for AIMS include adding all official reports, legislation, and members' expenses and interests information.
Finding the perfect toy box for kids toys can be a challenge. Most parents want a large toy box with storage to house all of the toys your little boy or girl has come to accumulate. We make quality plastic toy boxes so whether you are looking for a toy box with a bookshelf or a shelf for stuffed animals, all of our options provide a lot of storage for your children's mess - or, um, toys! https://www.thecustomboxes.com/toy-boxes/ .
This document outlines the history of computers through five generations. The first generation used vacuum tubes and were very large, expensive machines. The second generation introduced transistors, making computers smaller and more reliable. The third generation used integrated circuits, further reducing size and heat. The fourth generation saw the rise of microprocessors and microcomputers, leading to affordable personal computers. The fifth generation continues advancing processor and memory technology, with a focus on artificial intelligence and parallel processing.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Thecustomboxes.com offers top quality Bookmarks in all customized shapes and sizes at extremely affordable rates and the best possible top quality. Our Bookmarks are resilient and do not cuddle off or vanish with usage and time. For more information visit us @ https://www.thecustomboxes.com/
This document describes the services of a hypnotherapist and life coach, including cathexis therapeutic imagery, hypnotherapy, meditation and mindfulness training, and workplace wellness programs. The therapist has certifications in clinical hypnotherapy and therapeutic imagery from the Hypnosis Motivation Institute. Services are offered for stress, anxiety, smoking, weight loss, chronic pain, sports performance, public speaking, academic achievement, and life coaching. Workplace wellness programs include meditation and mindfulness training to improve emotional intelligence, leadership, culture and reduce costs.
This document discusses cloud computing and network traffic. It covers several topics:
1. Web-scale problems that are data-intensive and require large data centers to solve. Examples include searching the web and scientific research data.
2. Large data centers have become necessary to handle web-scale problems by centralizing computing resources. Issues include redundancy, efficiency, and management.
3. Different cloud computing models have emerged like utility computing, platform as a service, and software as a service.
For all the network traffic generated, smart network access control, traffic management, and effective security techniques are needed, and there is ongoing research in these areas.
eScience: A Transformed Scientific MethodDuncan Hull
The document discusses the concept of eScience, which involves synthesizing information technology and science. It explains how science is becoming more data-driven and computational, requiring new tools to manage large amounts of data. It recommends that organizations foster the development of tools to help with data capture, analysis, publication, and access across various scientific disciplines.
In this video from the DDN User Group at SC17, Jake Carroll from The Queensland Brain Institute, The University of Queensland, Australia presents: MeDiCI - How to Withstand a Research Data Tsunami.
The Metropolitan Data Caching Infrastructure (MeDiCI) project is a data storage fabric developed and trialled at UQ that delivers data to researchers where needed at any time. The “magic” of MeDiCI is it offers the illusion of a single virtual data centre next door even when it is actually distributed over potentially very wide areas with varying network connectivity. MeDiCI enables a range of techniques for national and international data sharing. Recent tests between UQ, the University of California, San Diego and the National Institute of Advanced Industrial Science and Technology in Japan indicated that MeDiCI can even support seamless access at very high data rates globally.
Watch the video: https://wp.me/p3RLHQ-hNG
Learn more: http://ddn.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
ESWC SS 2012 - Friday Keynote Marko Grobelnik: Big Data Tutorialeswcsummerschool
The document discusses big data techniques, tools, and applications. It describes how big data is enabled by increases in storage capacity, processing power, and data availability. It outlines common approaches to distributed processing, storage, and programming models for big data, including MapReduce, NoSQL databases, and cloud computing. It also provides examples of applications involving log file analysis, network alarm monitoring, media content analysis, and social network analysis.
The Hidden Web, XML and the Semantic Web: A Scientific Data Management Perspe...Dr. Aparna Varde
These are slides from a 3-hour tutorial on some interesting aspects of the Web addressed from a scientific data management angle. It is co-authored by Fabian Suchanek, Aparna Varde, Pierre Senellart and Richi Nayak and has been presented at the ACM EDBT conference, March 2011, in Uppsala, Sweden.
The document provides an overview of the data mining concepts and techniques course offered at the University of Illinois at Urbana-Champaign. It discusses the motivation for data mining due to abundant data collection and the need for knowledge discovery. It also describes common data mining functionalities like classification, clustering, association rule mining and the most popular algorithms used.
A New Year in Data Science: ML UnpausedPaco Nathan
This document summarizes Paco Nathan's presentation at Data Day Texas in 2015. Some key points:
- Paco Nathan discussed observations and trends from the past year in machine learning, data science, big data, and open source technologies.
- He argued that the definitions of data science and statistics are flawed and ignore important areas like development, visualization, and modeling real-world business problems.
- The presentation covered topics like functional programming approaches, streaming approximations, and the importance of an interdisciplinary approach combining computer science, statistics, and other fields like physics.
- Paco Nathan advocated for newer probabilistic techniques for analyzing large datasets that provide approximations using less resources compared to traditional batch processing approaches.
The document discusses scaling up information extraction to large collections by focusing on efficiency. It describes approaches such as using simple rules to process the majority of documents, filtering irrelevant documents without full processing, sharing annotations across tasks, and exploiting keyword indexes and specialized indexes to retrieve only relevant documents in an efficient manner. The goal is to apply information extraction techniques to massive web-scale data.
The document discusses big data and data science. It begins with definitions of big data and what differentiates it from traditional small data. It then covers the motivation and state of the big data market, as well as techniques, tools, and data science approaches used for working with big data. The document provides examples of research areas involved and risks to consider when mining big data. It concludes by discussing opportunities for applying big data analysis.
The document discusses the future of data science, including increased use of functional programming, cloud notebooks, and probabilistic modeling of large and diverse datasets from IoT devices, drones, and satellites. It also predicts data scientists will displace traditional product managers as data becomes more important for decision making. Overall, the future involves analyzing exponentially larger volumes of diverse data using scalable cloud tools and probabilistic algorithms.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
The document discusses parallel computing over the past 25 years and challenges for using multicore chips in the next decade. It aims to provide context to scale applications effectively to 32-1024 cores. Key challenges include expressing inherent application parallelism while enabling efficient mapping to hardware through programming models and runtime systems. Future work includes developing methods to restore lost parallelism information and tradeoffs between programming effort, generality and performance.
Cyberinfrastructure and Applications Overview: Howard University June22marpierc
1) Cyberinfrastructure refers to the combination of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people that enable knowledge discovery through integrated multi-scale simulations and analyses.
2) Cloud computing, multicore processors, and Web 2.0 tools are changing the landscape of cyberinfrastructure by providing new approaches to distributed computing and data sharing that emphasize usability, collaboration, and accessibility.
3) Scientific applications are increasingly data-intensive, requiring high-performance computing resources to analyze large datasets from sources like gene sequencers, telescopes, sensors, and web crawlers.
The document discusses themes around public versus private academic clouds. It summarizes the author's experience using public clouds for research and education purposes at UC Berkeley. Some key benefits included lower costs compared to operating their own cluster, easier administration and provisioning, and the ability to run large-scale experiments. However, private clouds may be needed for certain types of "cloud provider" research or to address security and regulatory issues with sensitive data. The author argues that public and private clouds each have advantages and it is unlikely any single solution will be best for all situations. Metering costs accurately across shared resources is also challenging.
Chinese culture is very rich in nature in terms of colors and calligraphic patterns. Chinese food boxes portray the same tradition in their style and design
.
The Assembly Information Management System (AIMS) is a digital system used by the Northern Ireland Assembly that consists of several modules like meetings, agendas, witnesses, and reports. It allows information to be created once and published in multiple places. The AIMS portal provides a central public area for viewing and searching all procedural business of the Assembly. It also includes separate search options for different procedures and functions like questions, plenary sessions, committees, and indexing. Future plans for AIMS include adding all official reports, legislation, and members' expenses and interests information.
Finding the perfect toy box for kids toys can be a challenge. Most parents want a large toy box with storage to house all of the toys your little boy or girl has come to accumulate. We make quality plastic toy boxes so whether you are looking for a toy box with a bookshelf or a shelf for stuffed animals, all of our options provide a lot of storage for your children's mess - or, um, toys! https://www.thecustomboxes.com/toy-boxes/ .
This document outlines the history of computers through five generations. The first generation used vacuum tubes and were very large, expensive machines. The second generation introduced transistors, making computers smaller and more reliable. The third generation used integrated circuits, further reducing size and heat. The fourth generation saw the rise of microprocessors and microcomputers, leading to affordable personal computers. The fifth generation continues advancing processor and memory technology, with a focus on artificial intelligence and parallel processing.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Thecustomboxes.com offers top quality Bookmarks in all customized shapes and sizes at extremely affordable rates and the best possible top quality. Our Bookmarks are resilient and do not cuddle off or vanish with usage and time. For more information visit us @ https://www.thecustomboxes.com/
This document describes the services of a hypnotherapist and life coach, including cathexis therapeutic imagery, hypnotherapy, meditation and mindfulness training, and workplace wellness programs. The therapist has certifications in clinical hypnotherapy and therapeutic imagery from the Hypnosis Motivation Institute. Services are offered for stress, anxiety, smoking, weight loss, chronic pain, sports performance, public speaking, academic achievement, and life coaching. Workplace wellness programs include meditation and mindfulness training to improve emotional intelligence, leadership, culture and reduce costs.
This document discusses cloud computing and network traffic. It covers several topics:
1. Web-scale problems that are data-intensive and require large data centers to solve. Examples include searching the web and scientific research data.
2. Large data centers have become necessary to handle web-scale problems by centralizing computing resources. Issues include redundancy, efficiency, and management.
3. Different cloud computing models have emerged like utility computing, platform as a service, and software as a service.
For all the network traffic generated, smart network access control, traffic management, and effective security techniques are needed, and there is ongoing research in these areas.
eScience: A Transformed Scientific MethodDuncan Hull
The document discusses the concept of eScience, which involves synthesizing information technology and science. It explains how science is becoming more data-driven and computational, requiring new tools to manage large amounts of data. It recommends that organizations foster the development of tools to help with data capture, analysis, publication, and access across various scientific disciplines.
In this video from the DDN User Group at SC17, Jake Carroll from The Queensland Brain Institute, The University of Queensland, Australia presents: MeDiCI - How to Withstand a Research Data Tsunami.
The Metropolitan Data Caching Infrastructure (MeDiCI) project is a data storage fabric developed and trialled at UQ that delivers data to researchers where needed at any time. The “magic” of MeDiCI is it offers the illusion of a single virtual data centre next door even when it is actually distributed over potentially very wide areas with varying network connectivity. MeDiCI enables a range of techniques for national and international data sharing. Recent tests between UQ, the University of California, San Diego and the National Institute of Advanced Industrial Science and Technology in Japan indicated that MeDiCI can even support seamless access at very high data rates globally.
Watch the video: https://wp.me/p3RLHQ-hNG
Learn more: http://ddn.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
ESWC SS 2012 - Friday Keynote Marko Grobelnik: Big Data Tutorialeswcsummerschool
The document discusses big data techniques, tools, and applications. It describes how big data is enabled by increases in storage capacity, processing power, and data availability. It outlines common approaches to distributed processing, storage, and programming models for big data, including MapReduce, NoSQL databases, and cloud computing. It also provides examples of applications involving log file analysis, network alarm monitoring, media content analysis, and social network analysis.
The Hidden Web, XML and the Semantic Web: A Scientific Data Management Perspe...Dr. Aparna Varde
These are slides from a 3-hour tutorial on some interesting aspects of the Web addressed from a scientific data management angle. It is co-authored by Fabian Suchanek, Aparna Varde, Pierre Senellart and Richi Nayak and has been presented at the ACM EDBT conference, March 2011, in Uppsala, Sweden.
The document provides an overview of the data mining concepts and techniques course offered at the University of Illinois at Urbana-Champaign. It discusses the motivation for data mining due to abundant data collection and the need for knowledge discovery. It also describes common data mining functionalities like classification, clustering, association rule mining and the most popular algorithms used.
A New Year in Data Science: ML UnpausedPaco Nathan
This document summarizes Paco Nathan's presentation at Data Day Texas in 2015. Some key points:
- Paco Nathan discussed observations and trends from the past year in machine learning, data science, big data, and open source technologies.
- He argued that the definitions of data science and statistics are flawed and ignore important areas like development, visualization, and modeling real-world business problems.
- The presentation covered topics like functional programming approaches, streaming approximations, and the importance of an interdisciplinary approach combining computer science, statistics, and other fields like physics.
- Paco Nathan advocated for newer probabilistic techniques for analyzing large datasets that provide approximations using less resources compared to traditional batch processing approaches.
The document discusses scaling up information extraction to large collections by focusing on efficiency. It describes approaches such as using simple rules to process the majority of documents, filtering irrelevant documents without full processing, sharing annotations across tasks, and exploiting keyword indexes and specialized indexes to retrieve only relevant documents in an efficient manner. The goal is to apply information extraction techniques to massive web-scale data.
The document discusses big data and data science. It begins with definitions of big data and what differentiates it from traditional small data. It then covers the motivation and state of the big data market, as well as techniques, tools, and data science approaches used for working with big data. The document provides examples of research areas involved and risks to consider when mining big data. It concludes by discussing opportunities for applying big data analysis.
The document discusses the future of data science, including increased use of functional programming, cloud notebooks, and probabilistic modeling of large and diverse datasets from IoT devices, drones, and satellites. It also predicts data scientists will displace traditional product managers as data becomes more important for decision making. Overall, the future involves analyzing exponentially larger volumes of diverse data using scalable cloud tools and probabilistic algorithms.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
The document discusses parallel computing over the past 25 years and challenges for using multicore chips in the next decade. It aims to provide context to scale applications effectively to 32-1024 cores. Key challenges include expressing inherent application parallelism while enabling efficient mapping to hardware through programming models and runtime systems. Future work includes developing methods to restore lost parallelism information and tradeoffs between programming effort, generality and performance.
Cyberinfrastructure and Applications Overview: Howard University June22marpierc
1) Cyberinfrastructure refers to the combination of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people that enable knowledge discovery through integrated multi-scale simulations and analyses.
2) Cloud computing, multicore processors, and Web 2.0 tools are changing the landscape of cyberinfrastructure by providing new approaches to distributed computing and data sharing that emphasize usability, collaboration, and accessibility.
3) Scientific applications are increasingly data-intensive, requiring high-performance computing resources to analyze large datasets from sources like gene sequencers, telescopes, sensors, and web crawlers.
The document discusses themes around public versus private academic clouds. It summarizes the author's experience using public clouds for research and education purposes at UC Berkeley. Some key benefits included lower costs compared to operating their own cluster, easier administration and provisioning, and the ability to run large-scale experiments. However, private clouds may be needed for certain types of "cloud provider" research or to address security and regulatory issues with sensitive data. The author argues that public and private clouds each have advantages and it is unlikely any single solution will be best for all situations. Metering costs accurately across shared resources is also challenging.
This document summarizes a talk on data science for software engineering. It discusses how data science involves various fields like statistics, machine learning, and data mining. It notes that while "big data" is often discussed, software engineering data is typically small and sparse. Domain knowledge is important for data mining to avoid misinterpreting data. Data science with software engineering data requires understanding organizations and their willingness to share data given privacy concerns. The document outlines sharing data, models, and methods for learning across different organizations and discusses techniques for balancing privacy and utility when sharing data.
Big Data is changing abruptly, and where it is likely headingPaco Nathan
Big Data technologies are changing rapidly due to shifts in hardware, data types, and software frameworks. Incumbent Big Data technologies do not fully leverage newer hardware like multicore processors and large memory spaces, while newer open source projects like Spark have emerged to better utilize these resources. Containers, clouds, functional programming, databases, approximations, and notebooks represent significant trends in how Big Data is managed and analyzed at large scale.
Ibm and innovation overview 20150326 v15 shortISSIP
IBM's University Programs work to accelerate regional development through partnerships with universities worldwide. The document discusses IBM's innovation capabilities and how they are dynamic. It provides examples of how IBM senses opportunities, seizes them, and manages threats and transformation.
Introduction to the Artificial Intelligence and Computer Vision revolutionDarian Frajberg
Deep learning and computer vision have revolutionized artificial intelligence. Deep learning uses artificial neural networks inspired by the human brain to learn from large amounts of data without being explicitly programmed. Computer vision gives computers the ability to understand digital images and videos. Key breakthroughs include AlexNet achieving unprecedented accuracy on ImageNet in 2012, demonstrating the power of deep convolutional neural networks for computer vision tasks. Challenges remain around ensuring AI systems are beneficial to society, avoiding data biases, and increasing transparency.
The document discusses the need for a new open source database management system called SciDB to address the challenges of storing and analyzing extremely large scientific datasets. SciDB is being designed to handle petabyte-scale multidimensional array data with native support for features important to science like provenance tracking, uncertainty handling, and integration with statistical tools. An international partnership involving scientists, database experts, and a nonprofit company is developing SciDB with initial funding and use cases coming from astronomy, industry, genomics and other domains.
NIMA2024 | De toegevoegde waarde van DEI en ESG in campagnes | Nathalie Lam |...BBPMedia1
Nathalie zal delen hoe DEI en ESG een fundamentele rol kunnen spelen in je merkstrategie en je de juiste aansluiting kan creëren met je doelgroep. Door middel van voorbeelden en simpele handvatten toont ze hoe dit in jouw organisatie toegepast kan worden.
𝐔𝐧𝐯𝐞𝐢𝐥 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐍𝐄𝐖𝐍𝐓𝐈𝐃𝐄’𝐬 𝐋𝐚𝐭𝐞𝐬𝐭 𝐎𝐟𝐟𝐞𝐫𝐢𝐧𝐠𝐬
Explore the details in our newly released product manual, which showcases NEWNTIDE's advanced heat pump technologies. Delve into our energy-efficient and eco-friendly solutions tailored for diverse global markets.
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART
Cover Story - China's Investment Leader - Dr. Alyce SUmsthrill
In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART KALYAN CHART
Unlocking WhatsApp Marketing with HubSpot: Integrating Messaging into Your Ma...Niswey
50 million companies worldwide leverage WhatsApp as a key marketing channel. You may have considered adding it to your marketing mix, or probably already driving impressive conversions with WhatsApp.
But wait. What happens when you fully integrate your WhatsApp campaigns with HubSpot?
That's exactly what we explored in this session.
We take a look at everything that you need to know in order to deploy effective WhatsApp marketing strategies, and integrate it with your buyer journey in HubSpot. From technical requirements to innovative campaign strategies, to advanced campaign reporting - we discuss all that and more, to leverage WhatsApp for maximum impact. Check out more details about the event here https://events.hubspot.com/events/details/hubspot-new-delhi-presents-unlocking-whatsapp-marketing-with-hubspot-integrating-messaging-into-your-marketing-strategy/
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART
1. Cloud Computing Lecture #1
What is Cloud Computing?
(and an intro to parallel/distributed processing)
Jimmy Lin
The iSchool
University of Maryland
Wednesday, September 3, 2008
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States
See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details
Some material adapted from slides by Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet,
Google Distributed Computing Seminar, 2007 (licensed under Creation Commons Attribution 3.0 License)
3. The iSchool
University of Maryland
What is Cloud Computing?
1. Web-scale problems
2. Large data centers
3. Different models of computing
4. Highly-interactive Web applications
4. The iSchool
University of Maryland
1. Web-Scale Problems
Characteristics:
Definitely data-intensive
May also be processing intensive
Examples:
Crawling, indexing, searching, mining the Web
“Post-genomics” life sciences research
Other scientific data (physics, astronomers, etc.)
Sensor networks
Web 2.0 applications
…
5. The iSchool
University of Maryland
How much data?
Wayback Machine has 2 PB + 20 TB/month (2006)
Google processes 20 PB a day (2008)
“all words ever spoken by human beings” ~ 5 EB
NOAA has ~1 PB climate data (2007)
CERN’s LHC will generate 15 PB a year (2008)
640K ought to be
enough for anybody.
8. The iSchool
University of Maryland
There’s nothing like more data!
s/inspiration/data/g;
(Banko and Brill, ACL 2001)
(Brants et al., EMNLP 2007)
9. The iSchool
University of Maryland
What to do with more data?
Answering factoid questions
Pattern matching on the Web
Works amazingly well
Learning relations
Start with seed instances
Search for patterns on the Web
Using patterns to find more instances
Who shot Abraham Lincoln? → X shot Abraham Lincoln
Birthday-of(Mozart, 1756)
Birthday-of(Einstein, 1879)
Wolfgang Amadeus Mozart (1756 - 1791)
Einstein was born in 1879
PERSON (DATE –
PERSON was born in DATE
(Brill et al., TREC 2001; Lin, ACM TOIS 2007)
(Agichtein and Gravano, DL 2000; Ravichandran and Hovy, ACL 2002; … )
10. The iSchool
University of Maryland
2. Large Data Centers
Web-scale problems? Throw more machines at it!
Clear trend: centralization of computing resources in large
data centers
Necessary ingredients: fiber, juice, and space
What do Oregon, Iceland, and abandoned mines have in
common?
Important Issues:
Redundancy
Efficiency
Utilization
Management
13. The iSchool
University of Maryland
Key Technology: Virtualization
Hardware
Operating System
App App App
Traditional Stack
Hardware
OS
App App App
Hypervisor
OS OS
Virtualized Stack
14. The iSchool
University of Maryland
3. Different Computing Models
Utility computing
Why buy machines when you can rent cycles?
Examples: Amazon’s EC2, GoGrid, AppNexus
Platform as a Service (PaaS)
Give me nice API and take care of the implementation
Example: Google App Engine
Software as a Service (SaaS)
Just run it for me!
Example: Gmail
“Why do it yourself if you can pay someone to do it for you?”
15. The iSchool
University of Maryland
4. Web Applications
A mistake on top of a hack built on sand held together by
duct tape?
What is the nature of software applications?
From the desktop to the browser
SaaS == Web-based applications
Examples: Google Maps, Facebook
How do we deliver highly-interactive Web-based
applications?
AJAX (asynchronous JavaScript and XML)
For better, or for worse…
16. The iSchool
University of Maryland
What is the course about?
MapReduce: the “back-end” of cloud computing
Batch-oriented processing of large datasets
Ajax: the “front-end” of cloud computing
Highly-interactive Web-based applications
Computing “in the clouds”
Amazon’s EC2/S3 as an example of utility computing
17. The iSchool
University of Maryland
Amazon Web Services
Elastic Compute Cloud (EC2)
Rent computing resources by the hour
Basic unit of accounting = instance-hour
Additional costs for bandwidth
Simple Storage Service (S3)
Persistent storage
Charge by the GB/month
Additional costs for bandwidth
You’ll be using EC2/S3 for course assignments!
18. The iSchool
University of Maryland
This course is not for you…
If you’re not genuinely interested in the topic
If you’re not ready to do a lot of programming
If you’re not open to thinking about computing in new ways
If you can’t cope with uncertainly, unpredictability, poor
documentation, and immature software
If you can’t put in the time
Otherwise, this will be a richly rewarding course!
20. The iSchool
University of Maryland
Cloud Computing Zen
Don’t get frustrated (take a deep breath)…
This is bleeding edge technology
Those W$*#T@F! moments
Be patient…
This is the second first time I’ve taught this course
Be flexible…
There will be unanticipated issues along the way
Be constructive…
Tell me how I can make everyone’s experience better
25. The iSchool
University of Maryland
Things to go over…
Course schedule
Assignments and deliverables
Amazon EC2/S3
26. The iSchool
University of Maryland
Web-Scale Problems?
Don’t hold your breath:
Biocomputing
Nanocomputing
Quantum computing
…
It all boils down to…
Divide-and-conquer
Throwing more hardware at the problem
Simple to understand… a lifetime to master…
27. The iSchool
University of Maryland
Divide and Conquer
“Work”
w1 w2 w3
r1 r2 r3
“Result”
“worker” “worker” “worker”
Partition
Combine
28. The iSchool
University of Maryland
Different Workers
Different threads in the same core
Different cores in the same CPU
Different CPUs in a multi-processor system
Different machines in a distributed system
29. The iSchool
University of Maryland
Choices, Choices, Choices
Commodity vs. “exotic” hardware
Number of machines vs. processor vs. cores
Bandwidth of memory vs. disk vs. network
Different programming models
30. The iSchool
University of Maryland
Flynn’s Taxonomy
Instructions
Single (SI) Multiple (MI)
Data
Multiple(M
SISD
Single-threaded
process
MISD
Pipeline
architecture
SIMD
Vector Processing
MIMD
Multi-threaded
Programming
Single(SD)
33. The iSchool
University of Maryland
MIMD
D D D D D D D
Processor
Instructions
D D D D D D D
Processor
Instructions
34. The iSchool
University of Maryland
Memory Typology: Shared
Memory
Processor
Processor Processor
Processor
35. The iSchool
University of Maryland
Memory Typology: Distributed
MemoryProcessor MemoryProcessor
MemoryProcessor MemoryProcessor
Network
36. The iSchool
University of Maryland
Memory Typology: Hybrid
Memory
Processor
Network
Processor
Memory
Processor
Processor
Memory
Processor
Processor
Memory
Processor
Processor
37. The iSchool
University of Maryland
Parallelization Problems
How do we assign work units to workers?
What if we have more work units than workers?
What if workers need to share partial results?
How do we aggregate partial results?
How do we know all the workers have finished?
What if workers die?
What is the common theme of all of these problems?
38. The iSchool
University of Maryland
General Theme?
Parallelization problems arise from:
Communication between workers
Access to shared resources (e.g., data)
Thus, we need a synchronization system!
This is tricky:
Finding bugs is hard
Solving bugs is even harder
39. The iSchool
University of Maryland
Managing Multiple Workers
Difficult because
(Often) don’t know the order in which workers run
(Often) don’t know where the workers are running
(Often) don’t know when workers interrupt each other
Thus, we need:
Semaphores (lock, unlock)
Conditional variables (wait, notify, broadcast)
Barriers
Still, lots of problems:
Deadlock, livelock, race conditions, ...
Moral of the story: be careful!
Even trickier if the workers are on different machines
40. The iSchool
University of Maryland
Patterns for Parallelism
Parallel computing has been around for decades
Here are some “design patterns” …
44. The iSchool
University of Maryland
Rubber Meets Road
From patterns to implementation:
pthreads, OpenMP for multi-threaded programming
MPI for clustering computing
…
The reality:
Lots of one-off solutions, custom code
Write you own dedicated library, then program with it
Burden on the programmer to explicitly manage everything
MapReduce to the rescue!
(for next time)