This document summarizes a lecture on MapReduce and distributed systems. It introduces MapReduce as a programming model for large-scale analytics that applies to tasks like word counting. It provides an example of using MapReduce to count words in documents by mapping words to counts and reducing the counts. It also discusses challenges like failures that MapReduce addresses through techniques like replication and data locality.
MapReduce is a programming model used for processing large datasets in a distributed manner. It involves two key steps - the map step and the reduce step. The map step processes individual records in parallel to generate intermediate key-value pairs. The reduce step merges all intermediate values associated with the same key. Hadoop is an open-source implementation of MapReduce that runs jobs across large clusters of commodity hardware.
This document provides an overview of MapReduce and Hadoop. It defines MapReduce terms like map and reduce. It describes how MapReduce processes data in parallel across multiple servers and tasks. Example MapReduce applications like word count are shown. The document also discusses the Hadoop implementation of MapReduce including code examples for map and reduce functions and the driver program. Fault tolerance, locality, and slow tasks in MapReduce systems are also covered.
Quantifying Overheads in Charm++ and HPX using Task BenchPatrick Diehl
Asynchronous Many-Task (AMT) runtime systems take advantage of multi-core architectures with light-weight threads, asynchronous executions, and smart scheduling. In this paper, we present the comparison of the AMT systems Charm++ and HPX with the main stream MPI, OpenMP, and MPI+OpenMP libraries using the Task Bench benchmarks. Charm++ is a parallel programming language based on C++, supporting stackless tasks as well as light-weight threads asynchronously along with an adaptive runtime system. HPX is a C++ library for concurrency and parallelism, exposing C++ standards conforming API. First, we analyze the commonalities, differences, and advantageous scenarios of Charm++ and HPX in detail. Further, to investigate the potential overheads introduced by the tasking systems of Charm++ and HPX, we utilize an existing parameterized benchmark, Task Bench, wherein 15 different programming systems were implemented, e.g., MPI, OpenMP, MPI + OpenMP, and extend Task Bench by adding HPX implementations. We quantify the overheads of Charm++, HPX, and the main stream libraries in different scenarios where a single task and multi-task are assigned to each core, respectively. We also investigate each system's scalability and the ability to hide the communication latency.
This document provides an overview of a distributed systems course taught at Columbia University. It discusses the challenges of distributed systems including ever-growing load, failures, and consistency. It describes goals like scalability and fault tolerance. Approaches covered include sharding, replication, and rigorous protocols. An example web service architecture is presented and gradually improved to address performance, scalability, fault tolerance, and consistency challenges through techniques like caching, replicating frontend servers and databases, and partitioning data.
Architectural details of the computing clusters (Hadoop) and designing MapReduce applications.
Matrix multiplication code is here: https://github.com/marufaytekin/MatrixMultiply
Details are on this post: https://lendap.wordpress.com/2015/02/16/matrix-multiplication-with-mapreduce/
Hadoop and Mapreduce for .NET User GroupCsaba Toth
This document provides an introduction to Hadoop and MapReduce. It discusses big data characteristics and challenges. It provides a brief history of Hadoop and compares it to RDBMS. Key aspects of Hadoop covered include the Hadoop Distributed File System (HDFS) for scalable storage and MapReduce for scalable processing. MapReduce uses a map function to process key-value pairs and generate intermediate pairs, and a reduce function to merge values by key and produce final results. The document demonstrates MapReduce through an example word count program and includes demos of implementing it on Hortonworks and Azure HDInsight.
This is a deck of slides from a recent meetup of AWS Usergroup Greece, presented by Ioannis Konstantinou from the National Technical University of Athens.
The presentation gives an overview of the Map Reduce framework and a description of its open source implementation (Hadoop). Amazon's own Elastic Map Reduce (EMR) service is also mentioned. With the growing interest on Big Data this is a good introduction to the subject.
This document provides an overview of key concepts in designing parallel programs, including manual vs automatic parallelization, partitioning work, communication factors like cost, latency and bandwidth, load balancing, granularity, and Amdahl's law. It discusses analyzing problems to identify parallelism, partitioning work via domain and functional decomposition, and handling data dependencies. Types of parallelizing compilers and their limitations are also covered.
MapReduce is a programming model used for processing large datasets in a distributed manner. It involves two key steps - the map step and the reduce step. The map step processes individual records in parallel to generate intermediate key-value pairs. The reduce step merges all intermediate values associated with the same key. Hadoop is an open-source implementation of MapReduce that runs jobs across large clusters of commodity hardware.
This document provides an overview of MapReduce and Hadoop. It defines MapReduce terms like map and reduce. It describes how MapReduce processes data in parallel across multiple servers and tasks. Example MapReduce applications like word count are shown. The document also discusses the Hadoop implementation of MapReduce including code examples for map and reduce functions and the driver program. Fault tolerance, locality, and slow tasks in MapReduce systems are also covered.
Quantifying Overheads in Charm++ and HPX using Task BenchPatrick Diehl
Asynchronous Many-Task (AMT) runtime systems take advantage of multi-core architectures with light-weight threads, asynchronous executions, and smart scheduling. In this paper, we present the comparison of the AMT systems Charm++ and HPX with the main stream MPI, OpenMP, and MPI+OpenMP libraries using the Task Bench benchmarks. Charm++ is a parallel programming language based on C++, supporting stackless tasks as well as light-weight threads asynchronously along with an adaptive runtime system. HPX is a C++ library for concurrency and parallelism, exposing C++ standards conforming API. First, we analyze the commonalities, differences, and advantageous scenarios of Charm++ and HPX in detail. Further, to investigate the potential overheads introduced by the tasking systems of Charm++ and HPX, we utilize an existing parameterized benchmark, Task Bench, wherein 15 different programming systems were implemented, e.g., MPI, OpenMP, MPI + OpenMP, and extend Task Bench by adding HPX implementations. We quantify the overheads of Charm++, HPX, and the main stream libraries in different scenarios where a single task and multi-task are assigned to each core, respectively. We also investigate each system's scalability and the ability to hide the communication latency.
This document provides an overview of a distributed systems course taught at Columbia University. It discusses the challenges of distributed systems including ever-growing load, failures, and consistency. It describes goals like scalability and fault tolerance. Approaches covered include sharding, replication, and rigorous protocols. An example web service architecture is presented and gradually improved to address performance, scalability, fault tolerance, and consistency challenges through techniques like caching, replicating frontend servers and databases, and partitioning data.
Architectural details of the computing clusters (Hadoop) and designing MapReduce applications.
Matrix multiplication code is here: https://github.com/marufaytekin/MatrixMultiply
Details are on this post: https://lendap.wordpress.com/2015/02/16/matrix-multiplication-with-mapreduce/
Hadoop and Mapreduce for .NET User GroupCsaba Toth
This document provides an introduction to Hadoop and MapReduce. It discusses big data characteristics and challenges. It provides a brief history of Hadoop and compares it to RDBMS. Key aspects of Hadoop covered include the Hadoop Distributed File System (HDFS) for scalable storage and MapReduce for scalable processing. MapReduce uses a map function to process key-value pairs and generate intermediate pairs, and a reduce function to merge values by key and produce final results. The document demonstrates MapReduce through an example word count program and includes demos of implementing it on Hortonworks and Azure HDInsight.
This is a deck of slides from a recent meetup of AWS Usergroup Greece, presented by Ioannis Konstantinou from the National Technical University of Athens.
The presentation gives an overview of the Map Reduce framework and a description of its open source implementation (Hadoop). Amazon's own Elastic Map Reduce (EMR) service is also mentioned. With the growing interest on Big Data this is a good introduction to the subject.
This document provides an overview of key concepts in designing parallel programs, including manual vs automatic parallelization, partitioning work, communication factors like cost, latency and bandwidth, load balancing, granularity, and Amdahl's law. It discusses analyzing problems to identify parallelism, partitioning work via domain and functional decomposition, and handling data dependencies. Types of parallelizing compilers and their limitations are also covered.
This document provides an overview of Hadoop frameworks and concepts. It discusses distributed file systems like HDFS and how they are organized at large scale. It then explains the MapReduce execution model and how it allows distributed processing of large datasets in a fault-tolerant manner. Specific algorithms like matrix-vector multiplication are discussed as examples of how MapReduce can be used. Finally, it introduces Hadoop YARN, which separates resource management from job execution, allowing more flexible processing of different types of applications on Hadoop clusters.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
This document provides an introduction to MapReduce and Hadoop. It begins by explaining the problems MapReduce aims to address like parallel database operations and processing unknown data schemas. It then describes the MapReduce programming model including the map and reduce functions. The rest of the document details how MapReduce is implemented in Hadoop, including the job launching process, use of mappers and reducers, and reading/writing of data. It provides an example word count program and discusses aspects like locality, fault tolerance, and optimizations in MapReduce and Hadoop.
Evaluating Cues for Resuming Interrupted Programming TAsksChris Parnin
1) Programmers often forget where they left off after interruptions like lunch breaks due to the large amount of information they must hold in their memory.
2) The researchers conducted a survey of programmers and experiments to evaluate strategies for resuming interrupted tasks. They found that providing cues from the programmer's recent work history was more effective than notes.
3) Specifically, a content timeline view of changed files and a difference-based integrated development environment interface helped programmers resume their tasks faster and with a higher success rate compared to only taking notes.
This document provides a tutorial on recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It begins with introductions to the speaker and an overview of the content. It then explains RNNs and how they work sequentially through hidden layers. Issues like vanishing gradients are discussed. LSTMs are introduced as an advanced RNN that can retain information over longer periods of time using gates. Pre-trained word embeddings like Word2Vec, GloVe, and FastText are briefly explained. Finally, homework is assigned to build a sentiment analysis model using an LSTM and pre-trained word embeddings on a Chinese text dataset.
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduceLeonidas Akritidis
This document describes using MapReduce to efficiently compute scientometrics like the h-index on large academic datasets. It introduces four MapReduce algorithms to parallelize the computation. The most efficient approach uses an in-mapper combiner to create a list of <paper, score> pairs for each unique author during the map phase. This reduces running times by at least 20% and bandwidth usage by around 13% compared to alternatives. Experiments on a 1.8 million paper dataset showed this first method performed best both in terms of runtime and I/O sizes.
L19CloudMapReduce introduction for cloud computing .pptMaruthiPrasad96
This document provides an overview of cloud computing with MapReduce and Hadoop. It discusses what cloud computing and MapReduce are, how they work, and examples of applications that use MapReduce. Specifically, MapReduce is introduced as a programming model for large-scale data processing across thousands of machines in a fault-tolerant way. Example applications like search, sorting, inverted indexing, finding popular words, and numerical integration are described. The document also outlines how to get started with Hadoop and write MapReduce jobs in Java.
Hyperoptimized Machine Learning and Deep Learning Methods For Geospatial and ...Neelabha Pant
Neelabh Pant successfully defended his PhD thesis titled "Hyper-optimized Machine Learning and Deep Learning Methods for Geo-Spatial and Temporal Function Estimation" at the University of Texas at Arlington. His research focused on developing recurrent neural networks, long short-term memory models, and genetic optimization techniques to predict locations, stock prices, and currency exchanges based on spatio-temporal data. Pant's dissertation committee included Dr. Ramez Elmasri as his advisor along with four other professors who evaluated his work.
Galen Shipman from LANL gave this talk at the 2017 Argonne Training Program on Extreme-Scale Computing.
"Developed by Stanford University, Legion is a data-centric programming model for writing high-performance applications for distributed heterogeneous architectures. Legion provides a common framework for implementing applications which can achieve portable performance across a range of architectures. The target class of users also dictates that productivity in Legion will always be a second-class design constraint behind performance. Instead Legion is designed to be extensible and to support higher-level productivity languages and libraries."
Watch the video: https://wp.me/p3RLHQ-hD7
Learn more: https://extremecomputingtraining.anl.gov/sessions/presentation-and-hands-on-the-legion-programming-model/
and
http://legion.stanford.edu/overview/index.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Online learning with structured streaming, spark summit brussels 2016Ram Sriharsha
This document summarizes an online presentation about online learning with structured streaming in Spark. The key points are:
- Online learning updates model parameters for each data point as it arrives, unlike batch learning which sees the full dataset before updating.
- Structured streaming in Spark provides a single API for batch, streaming, and machine learning workloads. It offers exactly-once guarantees and understands external event time.
- Streaming machine learning on structured streaming works by having a stateful aggregation query that picks up the last trained model and performs a distributed update and merge on each trigger interval. This allows modeling streaming data in a fault-tolerant way.
K-Means clustering is an algorithm that partitions data points into k clusters based on their distances from initial cluster center points. It is commonly used for classification applications on large datasets and can be parallelized by duplicating cluster centers and processing each data point independently. Mahout provides implementations of K-Means clustering and other algorithms that can operate on distributed datasets stored in Hadoop SequenceFiles.
The document discusses MapReduce concepts including the Job, Mapper, Reducer, and implementation classes. It provides details on:
1) The Job class allows configuration, submission, and monitoring of MapReduce jobs. The Mapper class defines Map tasks to transform input key-value pairs into intermediate pairs.
2) The Reducer class defines Reduce tasks to combine intermediate pairs with the same key. It involves shuffle, sort, and reduce phases.
3) An example MapReduce program is provided to count word frequencies in a text file using WordCount, WordMapper, and WordReducer classes.
This document discusses statistical computing for big data using distributed computing frameworks like MapReduce and Hadoop. It introduces MapReduce concepts like mappers, reducers, and Hadoop components including HDFS and YARN. Statistical challenges with big data are described, like scalability, dimensionality, and heterogeneity. The document discusses approaches for computing statistics on large datasets in parallel, including the Bag of Little Bootstraps method which breaks data into partitions to allow bootstrapping computations to run independently on clusters. Examples of computing means and counts in parallel using MapReduce are also provided.
MapReduce is a programming model for processing large datasets in a distributed system. It allows parallel processing of data across clusters of computers. A MapReduce program defines a map function that processes key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. The MapReduce framework handles parallelization of tasks, scheduling, input/output handling, and fault tolerance.
MapReduce is a programming model for processing large datasets in parallel across clusters of machines. It involves splitting the input data into independent chunks which are processed by the "map" step, and then grouping the outputs of the maps together and inputting them to the "reduce" step to produce the final results. The MapReduce paper presented Google's implementation which ran on a large cluster of commodity machines and used the Google File System for fault tolerance. It demonstrated that MapReduce can efficiently process very large amounts of data for applications like search, sorting and counting word frequencies.
OrientDB - Time Series and Event Sequences - Codemotion Milan 2014Luigi Dell'Aquila
This document discusses using a multi-model database approach to manage time series and event sequence data. It describes some common approaches like using a relational database with timestamp fields or storing events in a document database. It then outlines how OrientDB combines graph and document models to provide flexibility while maintaining fast write and read speeds. Events can be connected in the graph and stored as documents to allow for relationships and complex properties. The document summarizes how OrientDB allows aggregating data during writes using hooks and querying pre-aggregated data to enable fast analysis of time-based data.
This document discusses MapReduce and its ability to process large datasets in a distributed manner. MapReduce addresses challenges of distributed computation by allowing programmers to specify map and reduce functions. It then parallelizes the execution of these functions across large clusters and handles failures transparently. The map function processes input key-value pairs to generate intermediate pairs, which are then grouped by key and passed to reduce functions to generate the final output.
This document provides an overview of a course on algorithms and data structures. It outlines the course topics that will be covered over 15 weeks of lectures. These include data types, arrays, matrices, pointers, linked lists, stacks, queues, trees, graphs, sorting, and searching algorithms. Evaluation will be based on assignments, quizzes, projects, sessionals, and a final exam. The goal is for students to understand different algorithm techniques, apply suitable data structures to problems, and gain experience with classical algorithm problems.
This document provides an overview of Hadoop frameworks and concepts. It discusses distributed file systems like HDFS and how they are organized at large scale. It then explains the MapReduce execution model and how it allows distributed processing of large datasets in a fault-tolerant manner. Specific algorithms like matrix-vector multiplication are discussed as examples of how MapReduce can be used. Finally, it introduces Hadoop YARN, which separates resource management from job execution, allowing more flexible processing of different types of applications on Hadoop clusters.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
This document provides an introduction to MapReduce and Hadoop. It begins by explaining the problems MapReduce aims to address like parallel database operations and processing unknown data schemas. It then describes the MapReduce programming model including the map and reduce functions. The rest of the document details how MapReduce is implemented in Hadoop, including the job launching process, use of mappers and reducers, and reading/writing of data. It provides an example word count program and discusses aspects like locality, fault tolerance, and optimizations in MapReduce and Hadoop.
Evaluating Cues for Resuming Interrupted Programming TAsksChris Parnin
1) Programmers often forget where they left off after interruptions like lunch breaks due to the large amount of information they must hold in their memory.
2) The researchers conducted a survey of programmers and experiments to evaluate strategies for resuming interrupted tasks. They found that providing cues from the programmer's recent work history was more effective than notes.
3) Specifically, a content timeline view of changed files and a difference-based integrated development environment interface helped programmers resume their tasks faster and with a higher success rate compared to only taking notes.
This document provides a tutorial on recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It begins with introductions to the speaker and an overview of the content. It then explains RNNs and how they work sequentially through hidden layers. Issues like vanishing gradients are discussed. LSTMs are introduced as an advanced RNN that can retain information over longer periods of time using gates. Pre-trained word embeddings like Word2Vec, GloVe, and FastText are briefly explained. Finally, homework is assigned to build a sentiment analysis model using an LSTM and pre-trained word embeddings on a Chinese text dataset.
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduceLeonidas Akritidis
This document describes using MapReduce to efficiently compute scientometrics like the h-index on large academic datasets. It introduces four MapReduce algorithms to parallelize the computation. The most efficient approach uses an in-mapper combiner to create a list of <paper, score> pairs for each unique author during the map phase. This reduces running times by at least 20% and bandwidth usage by around 13% compared to alternatives. Experiments on a 1.8 million paper dataset showed this first method performed best both in terms of runtime and I/O sizes.
L19CloudMapReduce introduction for cloud computing .pptMaruthiPrasad96
This document provides an overview of cloud computing with MapReduce and Hadoop. It discusses what cloud computing and MapReduce are, how they work, and examples of applications that use MapReduce. Specifically, MapReduce is introduced as a programming model for large-scale data processing across thousands of machines in a fault-tolerant way. Example applications like search, sorting, inverted indexing, finding popular words, and numerical integration are described. The document also outlines how to get started with Hadoop and write MapReduce jobs in Java.
Hyperoptimized Machine Learning and Deep Learning Methods For Geospatial and ...Neelabha Pant
Neelabh Pant successfully defended his PhD thesis titled "Hyper-optimized Machine Learning and Deep Learning Methods for Geo-Spatial and Temporal Function Estimation" at the University of Texas at Arlington. His research focused on developing recurrent neural networks, long short-term memory models, and genetic optimization techniques to predict locations, stock prices, and currency exchanges based on spatio-temporal data. Pant's dissertation committee included Dr. Ramez Elmasri as his advisor along with four other professors who evaluated his work.
Galen Shipman from LANL gave this talk at the 2017 Argonne Training Program on Extreme-Scale Computing.
"Developed by Stanford University, Legion is a data-centric programming model for writing high-performance applications for distributed heterogeneous architectures. Legion provides a common framework for implementing applications which can achieve portable performance across a range of architectures. The target class of users also dictates that productivity in Legion will always be a second-class design constraint behind performance. Instead Legion is designed to be extensible and to support higher-level productivity languages and libraries."
Watch the video: https://wp.me/p3RLHQ-hD7
Learn more: https://extremecomputingtraining.anl.gov/sessions/presentation-and-hands-on-the-legion-programming-model/
and
http://legion.stanford.edu/overview/index.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Online learning with structured streaming, spark summit brussels 2016Ram Sriharsha
This document summarizes an online presentation about online learning with structured streaming in Spark. The key points are:
- Online learning updates model parameters for each data point as it arrives, unlike batch learning which sees the full dataset before updating.
- Structured streaming in Spark provides a single API for batch, streaming, and machine learning workloads. It offers exactly-once guarantees and understands external event time.
- Streaming machine learning on structured streaming works by having a stateful aggregation query that picks up the last trained model and performs a distributed update and merge on each trigger interval. This allows modeling streaming data in a fault-tolerant way.
K-Means clustering is an algorithm that partitions data points into k clusters based on their distances from initial cluster center points. It is commonly used for classification applications on large datasets and can be parallelized by duplicating cluster centers and processing each data point independently. Mahout provides implementations of K-Means clustering and other algorithms that can operate on distributed datasets stored in Hadoop SequenceFiles.
The document discusses MapReduce concepts including the Job, Mapper, Reducer, and implementation classes. It provides details on:
1) The Job class allows configuration, submission, and monitoring of MapReduce jobs. The Mapper class defines Map tasks to transform input key-value pairs into intermediate pairs.
2) The Reducer class defines Reduce tasks to combine intermediate pairs with the same key. It involves shuffle, sort, and reduce phases.
3) An example MapReduce program is provided to count word frequencies in a text file using WordCount, WordMapper, and WordReducer classes.
This document discusses statistical computing for big data using distributed computing frameworks like MapReduce and Hadoop. It introduces MapReduce concepts like mappers, reducers, and Hadoop components including HDFS and YARN. Statistical challenges with big data are described, like scalability, dimensionality, and heterogeneity. The document discusses approaches for computing statistics on large datasets in parallel, including the Bag of Little Bootstraps method which breaks data into partitions to allow bootstrapping computations to run independently on clusters. Examples of computing means and counts in parallel using MapReduce are also provided.
MapReduce is a programming model for processing large datasets in a distributed system. It allows parallel processing of data across clusters of computers. A MapReduce program defines a map function that processes key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. The MapReduce framework handles parallelization of tasks, scheduling, input/output handling, and fault tolerance.
MapReduce is a programming model for processing large datasets in parallel across clusters of machines. It involves splitting the input data into independent chunks which are processed by the "map" step, and then grouping the outputs of the maps together and inputting them to the "reduce" step to produce the final results. The MapReduce paper presented Google's implementation which ran on a large cluster of commodity machines and used the Google File System for fault tolerance. It demonstrated that MapReduce can efficiently process very large amounts of data for applications like search, sorting and counting word frequencies.
OrientDB - Time Series and Event Sequences - Codemotion Milan 2014Luigi Dell'Aquila
This document discusses using a multi-model database approach to manage time series and event sequence data. It describes some common approaches like using a relational database with timestamp fields or storing events in a document database. It then outlines how OrientDB combines graph and document models to provide flexibility while maintaining fast write and read speeds. Events can be connected in the graph and stored as documents to allow for relationships and complex properties. The document summarizes how OrientDB allows aggregating data during writes using hooks and querying pre-aggregated data to enable fast analysis of time-based data.
This document discusses MapReduce and its ability to process large datasets in a distributed manner. MapReduce addresses challenges of distributed computation by allowing programmers to specify map and reduce functions. It then parallelizes the execution of these functions across large clusters and handles failures transparently. The map function processes input key-value pairs to generate intermediate pairs, which are then grouped by key and passed to reduce functions to generate the final output.
This document provides an overview of a course on algorithms and data structures. It outlines the course topics that will be covered over 15 weeks of lectures. These include data types, arrays, matrices, pointers, linked lists, stacks, queues, trees, graphs, sorting, and searching algorithms. Evaluation will be based on assignments, quizzes, projects, sessionals, and a final exam. The goal is for students to understand different algorithm techniques, apply suitable data structures to problems, and gain experience with classical algorithm problems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
3. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Outline
• Today: MapReduce
– Analytics programming interface
– Word count example
– Chaining
– Reading/write from/to HDFS
– Dealing with failures
• In future: Spark
– Motivation
– Resilient Distributed Datasets (RDDs)
– Programming interface
– Transformations and actions
3
4. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Large-scale Analytics
• Compute the frequency of
words in a corpus of
documents.
• Count how many times users
have clicked on each of a
(large) set of ads.
• PageRank: Compute the
“importance” of a web page
based on the “importances”
of the pages that link to it.
• ….
4
5. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 1: SQL
5
• Before MapReduce, analytics mostly done in SQL, or manually.
• Example: Count word appearances in a corpus of documents.
• With SQL, the rough query might be:
• Very expressive, convenient to program, but no one knew how
to scale SQL execution!
SELECT COUNT(*)
FROM (
SELECT UNNEST(string_to_array(doc_content, ‘ ’)) as word
FROM Corpus
)
GROUP BY word
6. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 2: Manual
6
• Example: Count word appearances
in a corpus of documents.
Docs
net-
work
M1 M2
M5 M4
M3
Mn
7. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 2: Manual
7
• Example: Count word appearances
in a corpus of documents.
• Phase 1: Assign documents to
different machines/nodes.
– Each computes a dictionary:
{word: local_freq}.
net-
work
M1 M2
M5 M4
M3
Mn
Docs
8. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 2: Manual
8
• Example: Count word appearances
in a corpus of documents.
• Phase 1: Assign documents to
different machines/nodes.
– Each computes a dictionary:
{word: local_freq}.
• Phase 2: Nodes exchange
dictionaries (how?) to aggregate
local_freq’s.
Docs
net-
work
M1 M2
M5 M4
M3
Mn
9. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 2: Manual
9
• Example: Count word appearances
in a corpus of documents.
• Phase 1: Assign documents to
different machines/nodes.
– Each computes a dictionary:
{word: local_freq}.
• Phase 2: Nodes exchange
dictionaries (how?) to aggregate
local_freq’s.
– But how to make this scale??
Docs
net-
work
M1 M2
M5 M4
M3
Mn
10. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 2: Manual (cont’ed)
10
• Phase 2, Option a: Send all
{word: local_freq} dictionaries to
one node, who aggregates.
– But what if it’s too much
data for one node?
• Phase 2, Option b: Each node
sends (word, local_freq) to a
designated node, e.g., node
with ID hash(word) % N.
net-
work
M1 M2
M5 M4
M3
Mn
Docs
11. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 2: Manual (cont’ed)
11
• Phase 2, Option a: Send all
{word: local_freq} dictionaries to
one node, who aggregates.
– But what if it’s too much
data for one node?
• Phase 2, Option b: Each node
sends (word, local_freq) to a
designated node, e.g., node
with ID hash(word) % N.
net-
work
M1 M2
M5 M4
M3
Mn
Docs
We’ve roughly discovered an app-specific version of MapReduce!
12. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 2: Challenges
12
• How to generalize to other
applications?
• How to deal with failures?
• How to deal with slow nodes?
• How to deal with load balancing
(some docs are very large,
others small)?
• How to deal with skew (some
words are very frequent, so
nodes designated to aggregate
them will be pounded)?
• …
net-
work
M1 M2
M5 M4
M3
Mn
Docs
13. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
MapReduce
13
• Parallelizable programming model:
– Applies to a broad class of analytics applications.
– Isn’t as expressive as SQL but it is easier to scale.
– Consists of two phases, each intrinsically parallelizable:
• Map: processes input elements independently to emit relevant
(key, value) pairs from each.
• Transparently, the system groups all the values for each key
together: (key, [list of values]).
• Reduce: aggregates all the values for each key to emit a global
value for each key.
• Scalable, efficient, fault tolerant runtime system.
– Addresses preceding challenges (and more).
• Technical paper: please read it!
14. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
How it works
14
• Input: a collection of elements of (key, value) pair type.
• Programmer defines two functions:
– Map(key, value) → 0, 1, or more (key’, value’) pairs
– Reduce(key, value-list) → output
• Execution:
– Apply Map to each input key-value pair, in parallel for different keys.
– Sort emitted (key’, value’) pairs to produce (key, values-list) pairs.
– Apply Reduce to each (key, value-list) pair, in parallel for different
keys.
• Output is the union of all Reduce invocations’ outputs.
15. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Workflow
15
Reducer
Reducer
Mapper
Mapper
Mapper
read
local
write
system does
remote read, sort
Output
File 0
Output
File 1
write
Split 0
Split 1
Split 2
Input data
(in a DFS)
Output data
(in a DFS)
Map phase:
extract something you care
about from each record
Reduce phase:
aggregate
Workers Workers
Tmp data
(local FS)
16. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
• We have a directory, which contains many documents.
• The documents contain words separated by whitespace
and punctuation.
• Goal: Count the number of times each distinct word
appears in the file.
Example: Word count
17. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Word count with MapReduce
Map(key, value):
// key: document ID; value: document content
FOR (each word w IN value)
emit(w, 1);
Reduce(key, value-list):
// key: a word; value-list: a list of integers
result = 0;
FOR (each integer v on value-list)
result += v;
emit(key, result);
18. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Word count with MapReduce
Map(key, value):
// key: document ID; value: document content
FOR (each word w IN value)
emit(w, 1);
Reduce(key, value-list):
// key: a word; value-list: a list of integers
result = 0;
FOR (each integer v on value-list)
result += v;
emit(key, result);
Expect to be all 1’s, but
“combiners” allow local
summing of
integers with the same
key before passing to
reducers.
19. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Map Phase
• Mapper is given key: document ID; value: document content, say:
(D1,“The teacher went to the store. The store was closed; the
store opens in the morning. The store opens at 9am.”)
• It will emit the following pairs:
<The, 1> <teacher, 1> <went, 1> <to, 1> <the, 1> <store,1>
<the, 1> <store, 1> <was, 1> <closed, 1> <the, 1> <store,1>
<opens, 1> <in, 1> <the, 1> <morning, 1> <the 1> <store, 1>
<opens, 1> <at, 1> <9am, 1>
• Normally, there will be many documents, hence many Mappers that
emit such pairs in parallel, but for simplicity, let’s say that these are
all the pairs emitted from the Map phase. 19
22. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
• For each unique key emitted from the Map Phase, function
Reduce(key, value-list) is invoked on Reducer 1 or Reducer 2.
• Across their invocations, these Reducers will emit:
22
Reduce Phase
<9am, 1>
<at, 1>
<closed, 1>
<in, 1>
<morning, 1>
<opens, 2>
<store, 4>
<teacher, 1>
<the, 5>
<to, 1>
<went, 1>
<was, 1>
<The, 1>
Reducer 1 Reducer 2
Output of the entire program
23. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Chaining MapReduce
• The programming model seems pretty restrictive.
• But quite a few analytics applications can be written with it,
especially with a technique called chaining.
• If the output of reducers is (key, value) pairs, then their output
can be passed onto other Map/Reduce processes.
• This chaining can support a variety of analytics (though
certainly not all types of analytics, e.g., no ML b/c no loops).
23
24. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Example: Word Frequency
• Suppose instead of word count, we wanted to compute word
frequency: the probability that a word would appear in a document.
• This means computing the fraction of times a word appears, out of
the total number of words in the corpus.
24
25. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Solution: Chain two MapReduce’s
• First Map/Reduce: Word Count (like before)
– Map: process documents and output <word, 1> pairs.
– Multiple Reducers: emit <word, word_count> for each word.
• Second MapReduce:
– Map: processes <word, word_count> and outputs (1, (word, word_count)).
– 1 Reducer: does two passes:
• In first pass, sums up all word_count’s to calculate overall_count.
• In second pass calculates fractions. Emits multiple <word,
word_count/overall_count>.
25
26. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Option 2: Challenges (recall)
26
• How to generalize to other applications?
– See original MapReduce paper for more examples.
• How to deal with failures?
• How to deal with slow nodes?
• How to deal with load balancing?
• How to deal with skew?
• The MapReduce runtime tries to hide these challenges as
much as possible. We’ll talk about a few of the performance
and fault tolerance challenges here.
27. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Architecture
27
Reducer
Reducer
Mapper
Mapper
Mapper
read
local
write
system does
remote read, sort
Output
File 0
Output
File 1
write
Split 0
Split 1
Split 2
Input data
(in a DFS)
Output data
(in a DFS)
Tmp data
(local FS)
Master
assign
map
task
assign
reduce
task
DFS
Data
Nodes
DFS
Data
Nodes
DFS
Name
Server
28. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Data locality for performance
• Master scheduling policy:
– Asks DFS for locations of replicas of input file blocks.
– Map tasks are scheduled so DFS input block replica are
on the same machine or on the same rack.
• Effect: Thousands of machines read input at local
disk speed.
– Don’t need to transfer input data all over the cluster over
the network: eliminate network bottleneck!
28
29. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Heartbeats, replication for fault tolerance
• Failures are the norm in data centers.
• Worker failure:
– Master detects if workers failed by periodically pinging
them (this is called a “heartbeat”).
– Re-execute in-progress map/reduce tasks.
• Master failure:
– Initially, was single point of failure; Resume from Execution
Log. Subsequent versions used replication and consensus.
• Effect: From Google’s paper: lost 1600 of 1800 machines once, but
finished fine.
29
30. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Redundant execution for performance
• Slow workers or stragglers significantly lengthen completion time.
• Slowest worker can determine the total latency!
– This is why many systems measure 99th
percentile latency.
– Other jobs consuming resources on machine.
– Bad disks with errors transfer data very slowly.
• Solution: spawn backup copies of tasks.
– Whichever one finishes first "wins.”
– I.e., treat slow executions as failed executions!
30
31. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Take-Aways
• MapReduce is a programming model and runtime for scalable, fault
tolerant, and efficient analytics.
– Well, some types of analytics, it’s not very expressive.
• It hides common at-scale challenges under convenient abstractions.
– Programmers need only implement Map and Reduce and the system
takes care of running those at scale on lots of data.
– Failures raise semantic/performance challenges for MapReduce, which
it typically handles through redundancy.
• There exist open-source implementations, including Hadoop, Flink.
• Spark, a popular data analytics framework, also supports
MapReduce but also a wider range of programming models.
31
32. Distributed Systems 1, Columbia Course 4113, Instructor: Roxana Geambasu
Acknowledgement
• Slides prepared based on Asaf Cidon’s 2020 DS-1
invited lecture.
32