We present the ED-Tree, a distributed pool structure based on a combination of the elimination-tree and diffracting-tree paradigms, allowing high degrees of parallelism with reduced contention
This document discusses data types in Java. There are two main types: primitive data types (boolean, char, byte, etc.) and non-primitive types (classes, interfaces, arrays). It explains each of the eight primitive types and provides examples of non-primitive types like classes and arrays. The document also covers type casting (converting between data types), autoboxing/unboxing of primitive types to their corresponding wrapper classes, and the differences between implicit and explicit type casting.
Sv data types and sv interface usage in uvmHARINATH REDDY
SystemVerilog provides several data types for modeling hardware including basic types like reg, wire, integer, real, time and logic. It also introduces user-defined types like enum, struct, union, typedef and class. Enum allows defining a set of named values. Struct packs different data types together. Union shares the same storage for different types. Typedef defines custom type names. Class defines user-defined objects. Operators allow performing arithmetic, relational, equality and logical operations on data types. Assignment, increment/decrement operators are also supported.
This document discusses perceptrons and artificial neural networks. It begins by introducing the XOR problem, which is used to demonstrate the limitations of single layer perceptrons. The XOR problem involves predicting the output of the XOR function for different binary inputs, which cannot be solved with a single layer perceptron. The document then introduces multi-layer perceptrons, which can solve the XOR problem by adding a hidden layer. It describes the forward propagation process that occurs through both the input and hidden layers to generate an output classification.
This document discusses data types and variables in Java. It explains that there are two types of data types in Java - primitive and non-primitive. Primitive types include numeric types like int and float, and non-primitive types include classes, strings, and arrays. It also describes different types of variables in Java - local, instance, and static variables. The document provides examples of declaring variables and assigning literals. It further explains concepts like casting, immutable strings, StringBuffer/StringBuilder classes, and arrays.
There are three types of variables in Java: local variables, instance variables, and class/static variables. Local variables are declared within methods, constructors, or blocks and exist only within their scope. Instance variables are declared within a class but outside of methods and constructors, and each object instance has its own copy. Class/static variables are declared with the static keyword, and there is only one copy per class regardless of instances. Each variable type has different scopes, lifetimes, and ways of accessing them.
The counterpropagation network consists of three layers - an input layer, a hidden Kohonen layer, and an output Grossberg layer. The Kohonen layer uses competitive learning to categorize input patterns in an unsupervised manner. During operation, the input pattern activates a single node in the Kohonen layer, which then activates the appropriate output pattern in the Grossberg layer. Effectively, the counterpropagation network acts as a lookup table to map input patterns to associated output patterns by determining which stored pattern category the input belongs to.
The document discusses object-oriented design principles like encapsulation, abstraction, cohesion and coupling. It provides examples to illustrate high and low coupling between classes. Encapsulation is shown through rewriting a selection sort function to abstract out logical steps into reusable functions. The difference between function-oriented and object-oriented design is explained, with object-oriented focusing on both functionality and data through decentralized control.
The document discusses the different primitive and reference data types in Java, including their sizes, value ranges, and default values. It explains that variables are reserved memory locations used to store values and that reference variables are used to access objects of a specific class. The key Java primitive data types are byte, short, int, long, float, double, boolean, and char, each with their own characteristics for storing integer, floating point, boolean, or character values.
This document discusses data types in Java. There are two main types: primitive data types (boolean, char, byte, etc.) and non-primitive types (classes, interfaces, arrays). It explains each of the eight primitive types and provides examples of non-primitive types like classes and arrays. The document also covers type casting (converting between data types), autoboxing/unboxing of primitive types to their corresponding wrapper classes, and the differences between implicit and explicit type casting.
Sv data types and sv interface usage in uvmHARINATH REDDY
SystemVerilog provides several data types for modeling hardware including basic types like reg, wire, integer, real, time and logic. It also introduces user-defined types like enum, struct, union, typedef and class. Enum allows defining a set of named values. Struct packs different data types together. Union shares the same storage for different types. Typedef defines custom type names. Class defines user-defined objects. Operators allow performing arithmetic, relational, equality and logical operations on data types. Assignment, increment/decrement operators are also supported.
This document discusses perceptrons and artificial neural networks. It begins by introducing the XOR problem, which is used to demonstrate the limitations of single layer perceptrons. The XOR problem involves predicting the output of the XOR function for different binary inputs, which cannot be solved with a single layer perceptron. The document then introduces multi-layer perceptrons, which can solve the XOR problem by adding a hidden layer. It describes the forward propagation process that occurs through both the input and hidden layers to generate an output classification.
This document discusses data types and variables in Java. It explains that there are two types of data types in Java - primitive and non-primitive. Primitive types include numeric types like int and float, and non-primitive types include classes, strings, and arrays. It also describes different types of variables in Java - local, instance, and static variables. The document provides examples of declaring variables and assigning literals. It further explains concepts like casting, immutable strings, StringBuffer/StringBuilder classes, and arrays.
There are three types of variables in Java: local variables, instance variables, and class/static variables. Local variables are declared within methods, constructors, or blocks and exist only within their scope. Instance variables are declared within a class but outside of methods and constructors, and each object instance has its own copy. Class/static variables are declared with the static keyword, and there is only one copy per class regardless of instances. Each variable type has different scopes, lifetimes, and ways of accessing them.
The counterpropagation network consists of three layers - an input layer, a hidden Kohonen layer, and an output Grossberg layer. The Kohonen layer uses competitive learning to categorize input patterns in an unsupervised manner. During operation, the input pattern activates a single node in the Kohonen layer, which then activates the appropriate output pattern in the Grossberg layer. Effectively, the counterpropagation network acts as a lookup table to map input patterns to associated output patterns by determining which stored pattern category the input belongs to.
The document discusses object-oriented design principles like encapsulation, abstraction, cohesion and coupling. It provides examples to illustrate high and low coupling between classes. Encapsulation is shown through rewriting a selection sort function to abstract out logical steps into reusable functions. The difference between function-oriented and object-oriented design is explained, with object-oriented focusing on both functionality and data through decentralized control.
The document discusses the different primitive and reference data types in Java, including their sizes, value ranges, and default values. It explains that variables are reserved memory locations used to store values and that reference variables are used to access objects of a specific class. The key Java primitive data types are byte, short, int, long, float, double, boolean, and char, each with their own characteristics for storing integer, floating point, boolean, or character values.
A class definition consists of two parts: header and body. The class header specifies the class name and its base classes. (The latter relates to derived classes and is discussed in Chapter 8.) The class body defines the class members. Two types of members are supported:
Data members have the syntax of variable definitions and specify the representation of class objects.
Member functions have the syntax of function prototypes and specify the class operations, also called the class interface.
Class members fall under one of three different access permission categories:
Public members are accessible by all class users.
Private members are only accessible by the class members.
Protected members are only accessible by the class members and the members of a derived class.
The data type defined by a class is used in exactly the same way as a built-in type.
This document discusses expressions in Java programming. It defines an expression as consisting of terms joined by operators, where terms can be constants, variables, method calls, or parenthesized expressions. It describes Java's primitive data types and the common arithmetic, relational, and logical operators used to build expressions. It also covers topics like variable declarations, assignment statements, precedence rules, and boolean expressions. The goal is to introduce fundamental concepts for writing clear and maintainable Java code.
The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.
This document discusses abstract data types (ADTs) and their implementation in various programming languages. It covers the key concepts of ADTs including data abstraction, encapsulation, information hiding, and defining the public interface separately from the private implementation. It provides examples of ADTs implemented using modules in Modula-2, packages in Ada, classes in C++, generics in Java and C#, and classes in Ruby. Parameterized and encapsulation constructs are also discussed as techniques for implementing and organizing ADTs.
The document discusses object-oriented programming and class-based design. It explains that object-oriented programming focuses on modeling real-world objects as software objects with both data fields (attributes) and methods to operate on that data. A class defines a blueprint for objects, describing their attributes and methods. Objects are instances of classes that package both data and behaviors together. The document outlines key concepts like encapsulation, inheritance, polymorphism, and UML class diagrams.
- Cloudify allows running and managing applications on private and public clouds. It provides end-to-end application scaling across web, business logic, data, and messaging tiers.
- Cloudify uses recipes and DSL scripts to automate the deployment and management of applications and their infrastructure on clouds. This includes automatic scaling of instances.
- Cloudify was used to deploy a real-life telco application on clouds, demonstrating its ability to deploy complex, production applications on various cloud environments.
This document discusses using Big Data applications on OpenStack. It notes that Big Data needs large clusters of servers, which clouds can provide. OpenStack is a popular choice due to its support from major companies and ability to work across public and private clouds. The document outlines common components of Big Data applications, such as HDFS, MapReduce, and data access tools. It introduces Cloudify as an open source platform as a service (PaaS) that can manage and orchestrate all the pieces of a Big Data application deployed on an OpenStack cloud.
This document discusses techniques for building efficient software transactional memory (STM) systems. It begins by demonstrating how STM avoids problems like lost updates and deadlocks that can occur with traditional locking. It then reviews several existing STM implementations and their approaches. Benchmark results showing the performance of STM systems are presented, along with analysis of overhead sources. The document concludes by discussing optimizations like static analysis that can reduce STM overhead and make data structures more friendly to concurrent transactions.
Paractical Solutions for Multicore ProgrammingGuy Korland
The document discusses practical solutions for multicore programming. It describes some challenges with concurrent programming such as lost updates, deadlocks, and lack of performance. It then presents several solutions to address these challenges, including software transactional memory (STM), lock-free data structures, and relaxed consistency models. STM approaches discussed include DSTM2, JVSTM, Atom-Java, and Deuce STM. It also discusses the need for fine-grained concurrent data structures like a lock-free pool. The document concludes by discussing how relaxing linearizability requirements through approaches like quasi-linearizability can improve concurrency.
Counting and sorting are basic tasks that distributed systems rely on. The document discusses different approaches for distributed counting and sorting, including software combining trees, counting networks, and sorting networks. Counting networks like bitonic and periodic networks have depth of O(log2w) where w is the network width. Sorting networks can sort in the same time complexity by exploiting an isomorphism between counting and sorting networks. Sample sorting is also discussed as a way to sort large datasets across multiple threads.
- In 1975, Kunihiko Fukushima introduced the Cognitron network, which was an extension of the original perceptron and was able to handle pattern recognition problems better than the perceptron.
- The Cognitron used multiple layers of convergent subcircuits that allowed it to discriminate between patterns to some degree, unlike the perceptron.
- Fukushima later modified the Cognitron into the Neocognitron in 1980 by adding additional summation nodes, which made the network able to recognize patterns regardless of their position in the visual field.
This document contains questions and prompts related to data structures and algorithms topics like arrays, sorting, searching, hash tables, trees, and tree traversals. It asks the reader to analyze, draw, summarize, implement, or describe various concepts through examples, diagrams, pseudocode, or clear written explanations.
This document presents Alacart, a SAS macro system for generating classification trees similar to Breiman's CART methodology. It summarizes the core CART tree classification methodology, which involves recursively splitting data into purer subsets based on minimizing impurity at each node. Alacart generates the maximal tree on a training set and then prunes it back using either cross-validation or a test set to select the optimal size tree. An example application to customer classification is provided, showing the maximal 21-node tree and optimal 8-node pruned tree.
Here are 3 sentences summarizing the Java best practices document:
The document outlines several Java best practices such as avoiding magic numbers, using enums over constants, and preferring primitive types over wrapper classes to improve performance. It also recommends lazy initialization, using abstract classes when code needs to be shared among related classes, and using interfaces when unrelated classes need to implement common behaviors. The document provides guidance on optimizing loops, choosing between different collection types like sets and maps, and modifying strings efficiently using StringBuilder.
The document discusses fractal tree indexes, which are a data structure that can be used in databases like MySQL and MongoDB for indexing and retrieving data. Fractal tree indexes execute the same operations as B-trees but have faster insertion and deletion performance due to buffering techniques. They are highly optimized for large writes by scheduling disk writes to perform many operations at once. Fractal tree indexes also have better performance than B-trees due to lower fragmentation and faster searching enabled by forward pointers between index rows.
Using Machine Learning to Measure the Cross Section of Top Quark Pairs in the...m.a.kirn
Malina Kirn's 2011-09-06 University of Maryland Scientific Computation dissertation defense. Using neural networks and grid computing to measure top quark pair production cross section at the Compact Muon Solenoid detector at the Large Hadron Collider.
Coding Assignment 3CSC 330 Advanced Data Structures, Spri.docxmary772
Coding Assignment 3
CSC 330: Advanced Data Structures, Spring 2019
Released Monday, April 15, 2019
Due on Canvas on Wednesday, May 1, at 11:59pm
Overview
In this assignment, you’ll implement another variant of a height-balancing tree known as a
splay tree. The assignment will also give you an opportunity to work with Java inheritance;
in particular, the base code that you’ll amend is structured so that your SplayTree class
extends from an abstract class called HeightBalancingTree, which gives a general template
for how a height-balancing tree should be defined.
As always, please carefully read the entire write-up before you begin coding your submission.
Splay Trees
As mentioned above, a splay tree is another example of a height-balancing tree — a binary
search tree that, upon either an insertion or deletion, modifies the tree through a sequence
of rotations in order to reduce the overall height of the tree.
However, splay trees differ from the other height-balancing trees we’ve seen (AVL trees,
red-black trees) in terms of the type of guarantees that they provide. In particular, recall
that both AVL trees and red-black trees maintain the property that after any insertion or
deletion, the height of the tree is O(log n), where n is the number of elements in the tree.
Splay trees unfortunately do not provide this (fairly strong) guarantee; namely, it is possible
for the height of a splay tree to become greater than O(log n) over a sequence of insertions
and deletions.
Instead, splay trees provide a slightly weaker (though still meaningful) guarantee known as
an amortized bound, which is essentially just a bound on the average time of a single opera-
tion over the course of several operations. In the context of splay trees, one can show that
over the course of, say, n insertions to build a tree with n elements, the average time of each
of these operations is O(log n) (but again, keeping in mind it is possible for any single one
of these operations to take much longer than this).
Showing this guarantee is beyond the scope of this course (although the details of the analy-
sis can be found in your textbook). Instead, in this assignment, we will just be in interested
1
r splay:
N
root
root
2
1
1
2
l splay:
N
1
2
rr splay:
N
N
N
ll splay:
rl splay:
1
2
N
lr splay:
Figure 1: Illustration of the six possible cases for on a given step of a splay operation.
in writing an implementation of a splay tree in Java that is structured using inheritance.
Splay Tree Insertions and Deletions
To insert or delete an element from the tree, splay trees use the same approach as the other
height-balancing trees we’ve discussed in class — first we insert/deletion an element using
standard BST procedures, and then perform a “height-fixing” procedure that rebalances the
tree. Thus, what distinguishes each of these height-balancing trees from one another is how
they define their height-fixing procedures.
To fix the tree after both inser.
The document describes experiments to be conducted in the VLSI Design laboratory at K J Somaiya College of Engineering. The experiments include SPICE simulation of various NMOS inverter circuits, layout and simulation of CMOS inverter, NAND/NOR gates using Magic and SPICE, Boolean expression and transmission gate layout using Microwind, and Verilog programming and simulation of multiplexers, decoders, flip-flops, counters and state machines. The document also provides theory and methodology for each experiment.
A class definition consists of two parts: header and body. The class header specifies the class name and its base classes. (The latter relates to derived classes and is discussed in Chapter 8.) The class body defines the class members. Two types of members are supported:
Data members have the syntax of variable definitions and specify the representation of class objects.
Member functions have the syntax of function prototypes and specify the class operations, also called the class interface.
Class members fall under one of three different access permission categories:
Public members are accessible by all class users.
Private members are only accessible by the class members.
Protected members are only accessible by the class members and the members of a derived class.
The data type defined by a class is used in exactly the same way as a built-in type.
This document discusses expressions in Java programming. It defines an expression as consisting of terms joined by operators, where terms can be constants, variables, method calls, or parenthesized expressions. It describes Java's primitive data types and the common arithmetic, relational, and logical operators used to build expressions. It also covers topics like variable declarations, assignment statements, precedence rules, and boolean expressions. The goal is to introduce fundamental concepts for writing clear and maintainable Java code.
The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.
This document discusses abstract data types (ADTs) and their implementation in various programming languages. It covers the key concepts of ADTs including data abstraction, encapsulation, information hiding, and defining the public interface separately from the private implementation. It provides examples of ADTs implemented using modules in Modula-2, packages in Ada, classes in C++, generics in Java and C#, and classes in Ruby. Parameterized and encapsulation constructs are also discussed as techniques for implementing and organizing ADTs.
The document discusses object-oriented programming and class-based design. It explains that object-oriented programming focuses on modeling real-world objects as software objects with both data fields (attributes) and methods to operate on that data. A class defines a blueprint for objects, describing their attributes and methods. Objects are instances of classes that package both data and behaviors together. The document outlines key concepts like encapsulation, inheritance, polymorphism, and UML class diagrams.
- Cloudify allows running and managing applications on private and public clouds. It provides end-to-end application scaling across web, business logic, data, and messaging tiers.
- Cloudify uses recipes and DSL scripts to automate the deployment and management of applications and their infrastructure on clouds. This includes automatic scaling of instances.
- Cloudify was used to deploy a real-life telco application on clouds, demonstrating its ability to deploy complex, production applications on various cloud environments.
This document discusses using Big Data applications on OpenStack. It notes that Big Data needs large clusters of servers, which clouds can provide. OpenStack is a popular choice due to its support from major companies and ability to work across public and private clouds. The document outlines common components of Big Data applications, such as HDFS, MapReduce, and data access tools. It introduces Cloudify as an open source platform as a service (PaaS) that can manage and orchestrate all the pieces of a Big Data application deployed on an OpenStack cloud.
This document discusses techniques for building efficient software transactional memory (STM) systems. It begins by demonstrating how STM avoids problems like lost updates and deadlocks that can occur with traditional locking. It then reviews several existing STM implementations and their approaches. Benchmark results showing the performance of STM systems are presented, along with analysis of overhead sources. The document concludes by discussing optimizations like static analysis that can reduce STM overhead and make data structures more friendly to concurrent transactions.
Paractical Solutions for Multicore ProgrammingGuy Korland
The document discusses practical solutions for multicore programming. It describes some challenges with concurrent programming such as lost updates, deadlocks, and lack of performance. It then presents several solutions to address these challenges, including software transactional memory (STM), lock-free data structures, and relaxed consistency models. STM approaches discussed include DSTM2, JVSTM, Atom-Java, and Deuce STM. It also discusses the need for fine-grained concurrent data structures like a lock-free pool. The document concludes by discussing how relaxing linearizability requirements through approaches like quasi-linearizability can improve concurrency.
Counting and sorting are basic tasks that distributed systems rely on. The document discusses different approaches for distributed counting and sorting, including software combining trees, counting networks, and sorting networks. Counting networks like bitonic and periodic networks have depth of O(log2w) where w is the network width. Sorting networks can sort in the same time complexity by exploiting an isomorphism between counting and sorting networks. Sample sorting is also discussed as a way to sort large datasets across multiple threads.
- In 1975, Kunihiko Fukushima introduced the Cognitron network, which was an extension of the original perceptron and was able to handle pattern recognition problems better than the perceptron.
- The Cognitron used multiple layers of convergent subcircuits that allowed it to discriminate between patterns to some degree, unlike the perceptron.
- Fukushima later modified the Cognitron into the Neocognitron in 1980 by adding additional summation nodes, which made the network able to recognize patterns regardless of their position in the visual field.
This document contains questions and prompts related to data structures and algorithms topics like arrays, sorting, searching, hash tables, trees, and tree traversals. It asks the reader to analyze, draw, summarize, implement, or describe various concepts through examples, diagrams, pseudocode, or clear written explanations.
This document presents Alacart, a SAS macro system for generating classification trees similar to Breiman's CART methodology. It summarizes the core CART tree classification methodology, which involves recursively splitting data into purer subsets based on minimizing impurity at each node. Alacart generates the maximal tree on a training set and then prunes it back using either cross-validation or a test set to select the optimal size tree. An example application to customer classification is provided, showing the maximal 21-node tree and optimal 8-node pruned tree.
Here are 3 sentences summarizing the Java best practices document:
The document outlines several Java best practices such as avoiding magic numbers, using enums over constants, and preferring primitive types over wrapper classes to improve performance. It also recommends lazy initialization, using abstract classes when code needs to be shared among related classes, and using interfaces when unrelated classes need to implement common behaviors. The document provides guidance on optimizing loops, choosing between different collection types like sets and maps, and modifying strings efficiently using StringBuilder.
The document discusses fractal tree indexes, which are a data structure that can be used in databases like MySQL and MongoDB for indexing and retrieving data. Fractal tree indexes execute the same operations as B-trees but have faster insertion and deletion performance due to buffering techniques. They are highly optimized for large writes by scheduling disk writes to perform many operations at once. Fractal tree indexes also have better performance than B-trees due to lower fragmentation and faster searching enabled by forward pointers between index rows.
Using Machine Learning to Measure the Cross Section of Top Quark Pairs in the...m.a.kirn
Malina Kirn's 2011-09-06 University of Maryland Scientific Computation dissertation defense. Using neural networks and grid computing to measure top quark pair production cross section at the Compact Muon Solenoid detector at the Large Hadron Collider.
Coding Assignment 3CSC 330 Advanced Data Structures, Spri.docxmary772
Coding Assignment 3
CSC 330: Advanced Data Structures, Spring 2019
Released Monday, April 15, 2019
Due on Canvas on Wednesday, May 1, at 11:59pm
Overview
In this assignment, you’ll implement another variant of a height-balancing tree known as a
splay tree. The assignment will also give you an opportunity to work with Java inheritance;
in particular, the base code that you’ll amend is structured so that your SplayTree class
extends from an abstract class called HeightBalancingTree, which gives a general template
for how a height-balancing tree should be defined.
As always, please carefully read the entire write-up before you begin coding your submission.
Splay Trees
As mentioned above, a splay tree is another example of a height-balancing tree — a binary
search tree that, upon either an insertion or deletion, modifies the tree through a sequence
of rotations in order to reduce the overall height of the tree.
However, splay trees differ from the other height-balancing trees we’ve seen (AVL trees,
red-black trees) in terms of the type of guarantees that they provide. In particular, recall
that both AVL trees and red-black trees maintain the property that after any insertion or
deletion, the height of the tree is O(log n), where n is the number of elements in the tree.
Splay trees unfortunately do not provide this (fairly strong) guarantee; namely, it is possible
for the height of a splay tree to become greater than O(log n) over a sequence of insertions
and deletions.
Instead, splay trees provide a slightly weaker (though still meaningful) guarantee known as
an amortized bound, which is essentially just a bound on the average time of a single opera-
tion over the course of several operations. In the context of splay trees, one can show that
over the course of, say, n insertions to build a tree with n elements, the average time of each
of these operations is O(log n) (but again, keeping in mind it is possible for any single one
of these operations to take much longer than this).
Showing this guarantee is beyond the scope of this course (although the details of the analy-
sis can be found in your textbook). Instead, in this assignment, we will just be in interested
1
r splay:
N
root
root
2
1
1
2
l splay:
N
1
2
rr splay:
N
N
N
ll splay:
rl splay:
1
2
N
lr splay:
Figure 1: Illustration of the six possible cases for on a given step of a splay operation.
in writing an implementation of a splay tree in Java that is structured using inheritance.
Splay Tree Insertions and Deletions
To insert or delete an element from the tree, splay trees use the same approach as the other
height-balancing trees we’ve discussed in class — first we insert/deletion an element using
standard BST procedures, and then perform a “height-fixing” procedure that rebalances the
tree. Thus, what distinguishes each of these height-balancing trees from one another is how
they define their height-fixing procedures.
To fix the tree after both inser.
The document describes experiments to be conducted in the VLSI Design laboratory at K J Somaiya College of Engineering. The experiments include SPICE simulation of various NMOS inverter circuits, layout and simulation of CMOS inverter, NAND/NOR gates using Magic and SPICE, Boolean expression and transmission gate layout using Microwind, and Verilog programming and simulation of multiplexers, decoders, flip-flops, counters and state machines. The document also provides theory and methodology for each experiment.
The document discusses performing a discrete wavelet transform (DWT) on a 1D signal using MATLAB. It loads a test signal, performs a 5-level DWT decomposition using the coif3 wavelet, then reconstructs the approximation and detail signals at each level. Plots of the original, approximation, and detail signals are generated.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
Slides reviewing the paper:
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." In Advances in Neural Information Processing Systems, pp. 6000-6010. 2017.
The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.
This presentation describes key concepts in Java. I call it The Java Quicky.
This is part of a series of presentations to cover the Java programming language and its new offerings and versions in depth.
This is my attempt to compose a brief and cursory introduction to concepts in Java programming language. I call it Java Quicky.
I plan to extend and enhance it over time.
This document provides a table of contents and overview of topics related to technical aptitude questions, including data structures, C/C++ programming, quantitative aptitude, UNIX concepts, relational database management systems (RDBMS), SQL, computer networks, and operating systems. It discusses key data structure concepts like data structures used in different areas, pointers for heterogeneous linked lists, priority queues, recursion, sorting methods, trees, and hashing functions. It also includes examples of various data structure problems and their solutions.
A STUDY OF METHODS FOR TRAINING WITH DIFFERENT DATASETS IN IMAGE CLASSIFICATIONADEIJ Journal
This research developed a training method of Convolutional Neural Network model with multiple datasets to achieve good performance on both datasets. Two different methods of training with two characteristically different datasets with identical categories, one with very clean images and one with real-world data, were proposed and studied. The model used for the study was a neural network derived from ResNet. Mixed training was shown to produce the best accuracies for each dataset when the dataset is mixed into the training set at the highest proportion, and the best combined performance when the realworld dataset was mixed in at a ratio of around 70%. This ratio produced a top-1 combined performance of 63.8% (no mixing produced 30.8%) and a top-3 combined performance of 83.0% (no mixing produced 55.3%). This research also showed that iterative training has a worse combined performance than mixed training due to the issue of fast forgetting.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
The Collection API provides classes and interfaces that support operations on collections of objects, such as HashSet, HashMap, ArrayList, and LinkedList. It replaces vectors, arrays, and hashtables. Iterator is an interface used to iterate through elements of a Collection. The differences between an abstract class and interface are that interfaces provide multiple inheritance while abstract classes do not, and interfaces only define public methods without implementation.
Similar to Building Scalable Producer-Consumer Pools based on Elimination-Diraction Trees (20)
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
FalkorDB - Fastest way to your KnowledgeGuy Korland
https://www.falkordb.com
An ultra-low latency Graph Database that perfects the Knowledge Graph for GraphRAG.
Effectively overcoming the existing limitations of RAG for Large Language Models (LLM).
Redis Developer Day TLV - Redis Stack & RedisInsightGuy Korland
A breakdown of Redis Stack capabilities and the enhanced developers experience it brings.
Starting from Redis Stack Server, the clients and the new RedisInsight UI for developers.
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Vector Database is a new vertical of databases used to index and measure the similarity between different pieces of data. While it works well with structured data, when utilized for Vector Similarity Search (VSS) it really shines when comparing similarity in unstructured data, such as vector embedding of images, audio, or long pieces of text
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The document outlines the evolution of databases from the 1960s to present day and discusses how Redis provides a multi-model database approach. It presents an example scenario involving querying for restaurant recommendations from friends within a certain radius and criteria. The scenario demonstrates how Redis can handle the various data types and queries involved by combining key-value, graph, time series and search capabilities. It provides an example using Redis modules to model character relationships and biometrics in a movie database application.
From Key-Value to Multi-Model - RedisConf19Guy Korland
This document provides an overview of the evolution of database technologies from the 1960s to the present. It begins with navigational databases in the 1960s and relational databases in the 1970s. SQL databases emerged in the 1980s followed by object-oriented databases in the 1990s. NoSQL databases, including document, key-value, graph and time series databases, arose in the 2000s. The document then discusses using different database types together for various queries and applications. It provides examples of data structures and relationships that can be modeled across database types. Finally, it lists sessions at an upcoming conference focused on Redis technologies including RedisGears, RedisAI, RedisTimeSeries, RedisGraph and RediSearch.
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Quasi-Linearizability: relaxed consistency for improved concurrency.Guy Korland
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The Next Generation Application Server – How Event Based Processing yields s...Guy Korland
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The document summarizes the Deuce software transactional memory (STM) framework for Java. Deuce allows developers to add concurrency to Java applications using atomic blocks without changing code or using reserved keywords. It works by dynamically instrumenting bytecode to enable software transactions over shared fields. Benchmarks show it scales well on multi-core systems compared to other STM approaches like TL2 and LSA that require more intrusive changes.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
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These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
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Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
2. The Pool
Producer-consumer pools, that is, collections of
unordered objects or tasks, are a fundamental
element of modern multiprocessor software and a
target of extensive research and development
Get( )
P1 Put(x)
.
.
P2
C1
.
.
C2
Put(y)
Get( )
Pn Put(z)
Get( )
pool
Cn
3. ED-Tree Pool
We present the ED-Tree, a distributed pool
structure based on a combination of the
elimination-tree and diffracting-tree
paradigms, allowing high degrees of
parallelism with reduced contention
4. Java JDK6.0:
SynchronousQueue/Stack
(Lea, Scott, and Shearer)
- pairing
up function without buffering. Producers and consumers wait for
one another
LinkedBlockingQueue
- Producers put their value and
leave, Consumers wait for a value to become available.
ConcurrentLinkedQueue
- Producers put their value
and leave, Consumers return null if the pool is empty.
5. Drawback
All these structures are based on a centralized
structures like a lock-free queue or a stack,
and thus are limited in their scalability: the
head of the stack or queue is a sequential
bottleneck and source of contention.
6. Some Observations
A
pool does not have to obey neither LIFO or
FIFO semantics.
Therefore, no centralized structure needed,
to hold the items and to serve producers and
consumers requests.
7. New approach
ED-Tree: a combined variant of
the diffracting-tree structure (Shavit and Zemach) and
the elimination-tree structure (Shavit and Touitou)
The basic idea:
Use randomization to distribute the concurrent
requests of threads onto many locations so that they
collide with one another and can exchange values,
thus avoiding using a central place through which all
threads pass.
The result:
A pool that allows both parallelism and reduced
contention.
8. A little history
Both
diffraction and elimination were
presented years ago, and claimed to be
effective through simulation
However, elimination trees and diffracting
trees were never used to implement real
world structures
Elimination and diffraction were never
combined in a single data structure
9. Diffraction trees
A binary tree of objects called balancers [Aspnes-Herlihy-Shavit] with
a single input wire and two output wires
5
4
3
2
1
b
1
3
2
5
4
Threads arrive at a balancer and it repeatedly sends them left and right,
so its top wire always has maximum one more than the bottom one.
11. Diffraction trees
Connect each output wire to a lock free queue
b
b
b
b
b
b
b
To perform a push, threads traverse the balancers from the root to the leaves and
then push the item onto the appropriate queue.
To perform a pop, threads traverse the balancers from the root to the leaves and
then pop from the appropriate queue/block if the queue is empty.
13. Diffraction trees
Observation:
If an even number of threads pass through a balancer, the
outputs are evenly balanced on the top and bottom wires, but
the balancer's state remains unchanged
The approach:
Add a diffraction array in front of each toggle bit
0/1
Prism Array
toggle bit
14. Elimination
At
any point while traversing the tree, if
producer and consumer collide, there is no
need for them to diffract and continue
traversing the tree
Producer
can hand out his item to the
consumer, and both can leave the tree.
16. Using elimination-diffraction balancers
Let the array at balancer each be
a diffraction-elimination array:
If two producer (two consumer) threads meet in the
array, they leave on opposite wires, without a need to
touch the bit, as anyhow it would remain in its original
state.
If producer and consumer meet, they eliminate,
exchanging items.
If a producer or consumer call does not manage to
meet another in the array, it toggles the respective bit of
the balancer and moves on.
18. What about low concurrency
levels?
We
show that elimination and diffraction
techniques can be combined to work well at
both high and low loads
To insure good performance in low loads we use
several techniques, making the algorithm adapt
to the current contention level.
19. Adaptation mechanisms
Use backoff in space:
Randomly choose a cell in a certain range of the array
If the cell is busy (already occupied by two threads), increase the range and
repeat.
Else Spin and wait to collision
If timed out (no collision)
Decrease the range and repeat
If certain amount of timeouts reached, spin on the first cell of the array for a
period, and then move on to the toggle bit and the next level.
If certain amount of timeouts was reached, don’t try to diffract on any of the
next levels, just go straight to the toggle bit
Each thread remembers the last range it used at the current balancer and next
time starts from this range
20. Starvation avoidance
Threads
that failed to eliminate and propagated
all the way to the leaves can wait for a long time
for their requests to complete, while new threads
entering the tree and eliminating finish faster.
To
avoid starvation we limit the time a thread
can be blocked in the queues before it retries
the whole traversal again.
21. Implementation
Each
balancer is composed from
an elimination array, a pair of toggle bits, and
two references one to each of its child nodes.
public class Balancer
{
ToggleBit producerToggle, consumerToggle;
Exchanger[] eliminationArray;
Balancer leftChild , rightChild;
ThreadLocal<Integer> lastSlotRange;
}
23. Implementation
Starting from the root of the tree:
Enter balancer
Choose a cell in the array and try to collide with another thread,
using backoff mechanism described earlier.
If collision with another thread occurred
If both threads are of the same type, leave to the next level balancer
(each to separate direction)
If threads are of different type, exchange values and leave
Else (no collision) use appropriate toggle bit and move to next
level
If one of the leaves reached, go to the appropriate queue and
Insert/Remove an item according to the thread type
24. Performance evaluation
Sun UltraSPARC T2 Plus multi-core machine.
2 processors, each with 8 cores
each core with 8 hardware threads
64 way parallelism on a processor and 128 way
parallelism across the machine.
Most of the tests were done on one processor. i.e.
max 64 hardware threads
25. Performance evaluation
A tree with 3 levels and 8 queues
The queues are
SynchronousBlocking/LinkedBlocking/ConcurrentLinked,
according to the pool specification
b
b
b
b
b
b
b
The theme is building a data structure that is used as a pool, making it scalable and usable for high loads, and not less usable than existing implementations for low loads.
What is a pool? A collection of items, which my be objects or tasks. Resource pool – objects that are used and then returned to the pool, Pool of jobs to perform, etc…
The pool is approached by Producers and Consumers, that perform Put/Get (Push/Pop, Enqueue/Dequeue) actions.
These actions can implement different semantics, be blocking/non-blocking, depends on how the pool was defined (Explanation of blocking
on blocking)
The data structure we present is called ED-Tree and this is a highly scalable pool to, to be used in multithreaded application. We reach high performance and scalability by combining two paradigms: Elimination and diffraction
The Ed-Tree is implemented in Java
If we look in Java JDK for data structures that can be used as pool, we will find the following…
All the mentioned data structures are problematic…. They are based on centralized structures… the head or tail of queue/stack becomes a hot spot and in case large number of threads performance becomes worse, instead of improving
If we think about it, we don’t care about the order in which the items are inserted/removed from the pool. All we want is to avoid starvation (if item is inserted to the pool, eventually it will be removed).
Therefore we can avoid using centralized structure and distribute the pool in memory.
A single level of an elimination array was also used in implementing shared concurrent stacks. However, elimination trees and diffracting trees were never used to implement real world structures. This is
mostly due the fact that there was no need for them: machines with a sufficient level of concurrency and low enough interconnect latency to benefit from them did not exist. Today, multi-core machines present the necessary combination of high levels of parallelism and low interconnection costs. Indeed, this paper is the first to show that that ED-Tree based implementations of data structures from the java.util.concurrent
scale impressively on a real machine (a Sun Maramba multicore machine with 2x8 cores and 128 hardware threads), delivering throughput that is at high concurrency levels 10 times that of the new proposed JDK6.0 algorithms.
A balancer is usually implemented as a toggle bit: a bit that holds a binary value. Each thread change the value to the opposite one and picks a direction to exit, according to the bit value. For example 0 – go left, 1 – go right.
The diffraction tree constructed from a set of balancers…. You can say that the tree counts the elements, i.e. distributes them equally across the leafs…
If we connect a lock free queue/stack to each leaf and use two toggle bits in each balancer, we get a data structure which obeys a pool semantics…
We can see that we just moved our contention source from a single queue/stack to the balancers, starting from the entrance to the tree
The problem is solved by diffraction… what we get eventually is that each thread that approaches the pool, traverses the whole tree and eventually reaches one of the queues at the leafs.
Actually, if at some point during the tree traversal a producer and consumer threads meet each other, they don’t have to continue traversing the tree. The consumer can take the producers value, and they both can leave the tree.
In high loads, according to our statistics 50% of the threads are successfully eliminated on each level. I.e. if we use 3-level tree, 50% are eliminated at the first level, another 25% on the second, and 12.5% on the third, meaning, only about 10% of the requests survive till reaching the leaves.
We also use two toggle bits at each balancer – one for producers and one for consumers, to assure fair distribution
In the described implementation, another problem we can encounter is starvation…
Each balancer is composed from an EliminationArray, a pair of toggle bits, and two references one to each of its child nodes.
The implementation of an eliminationArray is based on an array of Exchangers. Each exchanger contains a single AtomicReference which is used as an Atomic placeholder for exchanging ExchangerPackage, where the ExchangerPackage is an object used to wrap the actual data and to mark its state and type.
At its peak at 64 threads the ED-Tree delivers more than 10 times the performance of the JDK.
Beyond 64 threads the threads are no longer bound to a single CPU, and traffic across the interconnect causes a moderate performance decline for the ED-Tree version
(the performance of the JDK is already very low).