This document discusses various techniques for code optimization at the compiler level. It begins by defining code optimization and explaining that it aims to make a program more efficient by reducing resources like time and memory usage. Several common optimization techniques are then described, including common subexpression elimination, dead code elimination, and loop optimization. Common subexpression elimination removes redundant computations. Dead code elimination removes code that does not affect program output. Loop optimization techniques like removing loop invariants and induction variables can improve loop performance. The document provides examples to illustrate how each technique works.
The document discusses the different phases of a compiler including lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation. It provides details on each phase and the techniques involved. The overall structure of a compiler is given as taking a source program through various representations until target machine code is generated. Key terms related to compilers like tokens, lexemes, and parsing techniques are also introduced.
The document discusses lexical analysis in compilers. It describes how the lexical analyzer reads source code characters and divides them into tokens. Regular expressions are used to specify patterns for token recognition. The lexical analyzer generates a finite state automaton to recognize these patterns. Lexical analysis is the first phase of compilation that separates the input into tokens for the parser.
The document discusses concepts related to sequence control and subprograms in programming languages. It covers conditional statements, loops, exception handling, subprogram definition and activation, and subprogram environments. Key points include implicit and explicit sequence control using statements, precedence and associativity rules for expressions, stack-based implementation of subprogram calls, and static versus dynamic scoping of identifiers through referencing environments.
This document discusses Fermat's and Euler's theorems regarding prime numbers and their applications in cryptography. It begins by defining prime numbers, prime factorization, and greatest common divisors. It then explains Fermat's theorem that any integer to the power of a prime number minus one is congruent to one modulo that prime number. Next, it defines Euler's totient function and proves Euler's theorem, which generalizes Fermat's theorem. It concludes by providing an example of how these theorems can be applied to encrypt and decrypt messages in a public-key cryptography system.
The document discusses different types of schedules for transactions in a database including serial, serializable, and equivalent schedules. A serial schedule requires transactions to execute consecutively without interleaving, while a serializable schedule allows interleaving as long as the schedule is equivalent to a serial schedule. Equivalence is determined based on conflicts, views, or results between the schedules. Conflict serializable schedules can be tested for cycles in a precedence graph to determine if interleaving introduces conflicts, while view serializable schedules must produce the same reads and writes as a serial schedule.
The document discusses code optimization techniques in compilers. It covers the following key points:
1. Code optimization aims to improve code performance by replacing high-level constructs with more efficient low-level code while preserving program semantics. It occurs at various compiler phases like source code, intermediate code, and target code.
2. Common optimization techniques include constant folding, propagation, algebraic simplification, strength reduction, copy propagation, and dead code elimination. Control and data flow analysis are required to perform many optimizations.
3. Optimizations can be local within basic blocks, global across blocks, or inter-procedural across procedures. Representations like flow graphs, basic blocks, and DAGs are used to apply optimizations at
This document discusses various techniques for code optimization at the compiler level. It begins by defining code optimization and explaining that it aims to make a program more efficient by reducing resources like time and memory usage. Several common optimization techniques are then described, including common subexpression elimination, dead code elimination, and loop optimization. Common subexpression elimination removes redundant computations. Dead code elimination removes code that does not affect program output. Loop optimization techniques like removing loop invariants and induction variables can improve loop performance. The document provides examples to illustrate how each technique works.
The document discusses the different phases of a compiler including lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation. It provides details on each phase and the techniques involved. The overall structure of a compiler is given as taking a source program through various representations until target machine code is generated. Key terms related to compilers like tokens, lexemes, and parsing techniques are also introduced.
The document discusses lexical analysis in compilers. It describes how the lexical analyzer reads source code characters and divides them into tokens. Regular expressions are used to specify patterns for token recognition. The lexical analyzer generates a finite state automaton to recognize these patterns. Lexical analysis is the first phase of compilation that separates the input into tokens for the parser.
The document discusses concepts related to sequence control and subprograms in programming languages. It covers conditional statements, loops, exception handling, subprogram definition and activation, and subprogram environments. Key points include implicit and explicit sequence control using statements, precedence and associativity rules for expressions, stack-based implementation of subprogram calls, and static versus dynamic scoping of identifiers through referencing environments.
This document discusses Fermat's and Euler's theorems regarding prime numbers and their applications in cryptography. It begins by defining prime numbers, prime factorization, and greatest common divisors. It then explains Fermat's theorem that any integer to the power of a prime number minus one is congruent to one modulo that prime number. Next, it defines Euler's totient function and proves Euler's theorem, which generalizes Fermat's theorem. It concludes by providing an example of how these theorems can be applied to encrypt and decrypt messages in a public-key cryptography system.
The document discusses different types of schedules for transactions in a database including serial, serializable, and equivalent schedules. A serial schedule requires transactions to execute consecutively without interleaving, while a serializable schedule allows interleaving as long as the schedule is equivalent to a serial schedule. Equivalence is determined based on conflicts, views, or results between the schedules. Conflict serializable schedules can be tested for cycles in a precedence graph to determine if interleaving introduces conflicts, while view serializable schedules must produce the same reads and writes as a serial schedule.
The document discusses code optimization techniques in compilers. It covers the following key points:
1. Code optimization aims to improve code performance by replacing high-level constructs with more efficient low-level code while preserving program semantics. It occurs at various compiler phases like source code, intermediate code, and target code.
2. Common optimization techniques include constant folding, propagation, algebraic simplification, strength reduction, copy propagation, and dead code elimination. Control and data flow analysis are required to perform many optimizations.
3. Optimizations can be local within basic blocks, global across blocks, or inter-procedural across procedures. Representations like flow graphs, basic blocks, and DAGs are used to apply optimizations at
Relationship Among Token, Lexeme & PatternBharat Rathore
Relationship among Token, Lexeme and Pattern
Outline
Token
Lexeme
Pattern
Relationship
Tokens : A token is sequence of characters that can be treated
as a unit/single logical entity.
Examples
Keywords
Examples : for, while, if etc.
Identifier
Examples : Variable name, function name, etc.
Operators
Examples : '+', '++', '-' etc.
Separators
Examples : ',' ';' etc.
Pattern
Pattern is a rule describing all those lexemes that can represent a particular token in a source language.
Lexeme
It is a sequence of characters in the source program that is matched by the pattern for a token.
Example : “float”, “=“, “223”, “;”
This document discusses parameter passing mechanisms in programming languages. It explains different parameter passing techniques like call by value, call by reference, call by name. It also discusses formal and actual parameters, how they are associated during a subprogram call, and how their values are copied or linked during the subprogram entry and exit. Implementation of formal parameters involves storage in the activation record and handling input/output types by copying or using pointers.
This document discusses semantic analysis in compilers. It begins by defining semantics and semantic analysis, and provides an example of a syntactically valid but semantically invalid statement. It then discusses how semantic rules are associated with a context-free grammar to perform semantic analysis. It describes the annotated parse tree output of semantic analysis and how semantic rules are associated with grammar productions. The document discusses different ways to represent semantic rules like syntax-directed definitions and attribute grammars. It also covers different types of attributes like synthesized and inherited attributes. Finally, it discusses applications of semantic analysis like type checking and generating intermediate code.
This slide is prepared By these following Students of Dept. of CSE JnU, Dhaka. Thanks To: Nusrat Jahan, Arifatun Nesa, Fatema Akter, Maleka Khatun, Tamanna Tabassum.
This document discusses bottom-up parsing and LR parsing. Bottom-up parsing starts from the leaf nodes of a parse tree and works upward to the root node by applying grammar rules in reverse. LR parsing is a type of bottom-up parsing that uses shift-reduce parsing with two steps: shifting input symbols onto a stack, and reducing grammar rules on the stack. The document describes LR parsers, types of LR parsers like SLR(1) and LALR(1), and the LR parsing algorithm. It also compares bottom-up LR parsing to top-down LL parsing.
The document discusses different types of parsing including:
1) Top-down parsing which starts at the root node and builds the parse tree recursively, requiring backtracking for ambiguous grammars.
2) Bottom-up parsing which starts at the leaf nodes and applies grammar rules in reverse to reach the start symbol using shift-reduce parsing.
3) LL(1) and LR parsing which are predictive parsing techniques using parsing tables constructed from FIRST and FOLLOW sets to avoid backtracking.
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Performance analysis(Time & Space Complexity)swapnac12
The document discusses algorithms analysis and design. It covers time complexity and space complexity analysis using approaches like counting the number of basic operations like assignments, comparisons etc. and analyzing how they vary with the size of the input. Common complexities like constant, linear, quadratic and cubic are explained with examples. Frequency count method is presented to determine tight bounds of time and space complexity of algorithms.
Functional dependency defines a relationship between attributes in a table where a set of attributes determine another attribute. There are different types of functional dependencies including trivial, non-trivial, multivalued, and transitive. An example given is a student table with attributes Stu_Id, Stu_Name, Stu_Age which has the functional dependency of Stu_Id->Stu_Name since the student ID uniquely identifies the student name.
This document discusses stacks and queues as linear data structures. It defines stacks as last-in, first-out (LIFO) collections where the last item added is the first removed. Queues are first-in, first-out (FIFO) collections where the first item added is the first removed. Common stack and queue operations like push, pop, insert, and remove are presented along with algorithms and examples. Applications of stacks and queues in areas like expression evaluation, string reversal, and scheduling are also covered.
This document provides an overview of object-oriented analysis and design. It defines key terms and concepts in object-oriented modeling like use cases, class diagrams, states, sequences. It describes developing requirements models using use cases and class diagrams. It also explains modeling object behavior through state and sequence diagrams and transitioning analysis models to design.
This document discusses top-down parsing and different types of top-down parsers, including recursive descent parsers, predictive parsers, and LL(1) grammars. It explains how to build predictive parsers without recursion by using a parsing table constructed from the FIRST and FOLLOW sets of grammar symbols. The key steps are: 1) computing FIRST and FOLLOW, 2) filling the predictive parsing table based on FIRST/FOLLOW, 3) using the table to parse inputs in a non-recursive manner by maintaining the parser's own stack. An example is provided to illustrate constructing the FIRST/FOLLOW sets and parsing table for a sample grammar.
what is Parsing
different types of parsing
what is parser and role of parser
what is top-down parsing and bottom-up parsing
what is the problem in top-down parsing
design of top-down parsing and bottom-up parsing
examples of top-down parsing and bottom-up parsing
This document discusses parsing and context-free grammars. It defines parsing as verifying that tokens generated by a lexical analyzer follow syntactic rules of a language using a parser. Context-free grammars are defined using terminals, non-terminals, productions and a start symbol. Top-down and bottom-up parsing are introduced. Techniques for grammar analysis and improvement like left factoring, eliminating left recursion, calculating first and follow sets are explained with examples.
The document describes the steps of SLR parsing:
1. Create an augmented grammar by adding a new start symbol S' and the production S' -> S.
2. Generate kernel items by introducing dots in productions.
3. Find the closure of kernel items.
4. Compute the goto table from the closure sets.
5. Construct the parsing table from the goto table and closure sets.
6. Parse input strings using the parsing table and stack.
The document discusses scanning (lexical analysis) in compiler construction. It covers the scanning process, regular expressions, and finite automata. The scanning process identifies tokens from source code by categorizing characters as reserved words, special symbols, or other tokens. Regular expressions are used to represent patterns of character strings and define the language of tokens. Finite automata are mathematical models for describing scanning algorithms using states, transitions, and acceptance.
Relationship Among Token, Lexeme & PatternBharat Rathore
Relationship among Token, Lexeme and Pattern
Outline
Token
Lexeme
Pattern
Relationship
Tokens : A token is sequence of characters that can be treated
as a unit/single logical entity.
Examples
Keywords
Examples : for, while, if etc.
Identifier
Examples : Variable name, function name, etc.
Operators
Examples : '+', '++', '-' etc.
Separators
Examples : ',' ';' etc.
Pattern
Pattern is a rule describing all those lexemes that can represent a particular token in a source language.
Lexeme
It is a sequence of characters in the source program that is matched by the pattern for a token.
Example : “float”, “=“, “223”, “;”
This document discusses parameter passing mechanisms in programming languages. It explains different parameter passing techniques like call by value, call by reference, call by name. It also discusses formal and actual parameters, how they are associated during a subprogram call, and how their values are copied or linked during the subprogram entry and exit. Implementation of formal parameters involves storage in the activation record and handling input/output types by copying or using pointers.
This document discusses semantic analysis in compilers. It begins by defining semantics and semantic analysis, and provides an example of a syntactically valid but semantically invalid statement. It then discusses how semantic rules are associated with a context-free grammar to perform semantic analysis. It describes the annotated parse tree output of semantic analysis and how semantic rules are associated with grammar productions. The document discusses different ways to represent semantic rules like syntax-directed definitions and attribute grammars. It also covers different types of attributes like synthesized and inherited attributes. Finally, it discusses applications of semantic analysis like type checking and generating intermediate code.
This slide is prepared By these following Students of Dept. of CSE JnU, Dhaka. Thanks To: Nusrat Jahan, Arifatun Nesa, Fatema Akter, Maleka Khatun, Tamanna Tabassum.
This document discusses bottom-up parsing and LR parsing. Bottom-up parsing starts from the leaf nodes of a parse tree and works upward to the root node by applying grammar rules in reverse. LR parsing is a type of bottom-up parsing that uses shift-reduce parsing with two steps: shifting input symbols onto a stack, and reducing grammar rules on the stack. The document describes LR parsers, types of LR parsers like SLR(1) and LALR(1), and the LR parsing algorithm. It also compares bottom-up LR parsing to top-down LL parsing.
The document discusses different types of parsing including:
1) Top-down parsing which starts at the root node and builds the parse tree recursively, requiring backtracking for ambiguous grammars.
2) Bottom-up parsing which starts at the leaf nodes and applies grammar rules in reverse to reach the start symbol using shift-reduce parsing.
3) LL(1) and LR parsing which are predictive parsing techniques using parsing tables constructed from FIRST and FOLLOW sets to avoid backtracking.
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Performance analysis(Time & Space Complexity)swapnac12
The document discusses algorithms analysis and design. It covers time complexity and space complexity analysis using approaches like counting the number of basic operations like assignments, comparisons etc. and analyzing how they vary with the size of the input. Common complexities like constant, linear, quadratic and cubic are explained with examples. Frequency count method is presented to determine tight bounds of time and space complexity of algorithms.
Functional dependency defines a relationship between attributes in a table where a set of attributes determine another attribute. There are different types of functional dependencies including trivial, non-trivial, multivalued, and transitive. An example given is a student table with attributes Stu_Id, Stu_Name, Stu_Age which has the functional dependency of Stu_Id->Stu_Name since the student ID uniquely identifies the student name.
This document discusses stacks and queues as linear data structures. It defines stacks as last-in, first-out (LIFO) collections where the last item added is the first removed. Queues are first-in, first-out (FIFO) collections where the first item added is the first removed. Common stack and queue operations like push, pop, insert, and remove are presented along with algorithms and examples. Applications of stacks and queues in areas like expression evaluation, string reversal, and scheduling are also covered.
This document provides an overview of object-oriented analysis and design. It defines key terms and concepts in object-oriented modeling like use cases, class diagrams, states, sequences. It describes developing requirements models using use cases and class diagrams. It also explains modeling object behavior through state and sequence diagrams and transitioning analysis models to design.
This document discusses top-down parsing and different types of top-down parsers, including recursive descent parsers, predictive parsers, and LL(1) grammars. It explains how to build predictive parsers without recursion by using a parsing table constructed from the FIRST and FOLLOW sets of grammar symbols. The key steps are: 1) computing FIRST and FOLLOW, 2) filling the predictive parsing table based on FIRST/FOLLOW, 3) using the table to parse inputs in a non-recursive manner by maintaining the parser's own stack. An example is provided to illustrate constructing the FIRST/FOLLOW sets and parsing table for a sample grammar.
what is Parsing
different types of parsing
what is parser and role of parser
what is top-down parsing and bottom-up parsing
what is the problem in top-down parsing
design of top-down parsing and bottom-up parsing
examples of top-down parsing and bottom-up parsing
This document discusses parsing and context-free grammars. It defines parsing as verifying that tokens generated by a lexical analyzer follow syntactic rules of a language using a parser. Context-free grammars are defined using terminals, non-terminals, productions and a start symbol. Top-down and bottom-up parsing are introduced. Techniques for grammar analysis and improvement like left factoring, eliminating left recursion, calculating first and follow sets are explained with examples.
The document describes the steps of SLR parsing:
1. Create an augmented grammar by adding a new start symbol S' and the production S' -> S.
2. Generate kernel items by introducing dots in productions.
3. Find the closure of kernel items.
4. Compute the goto table from the closure sets.
5. Construct the parsing table from the goto table and closure sets.
6. Parse input strings using the parsing table and stack.
The document discusses scanning (lexical analysis) in compiler construction. It covers the scanning process, regular expressions, and finite automata. The scanning process identifies tokens from source code by categorizing characters as reserved words, special symbols, or other tokens. Regular expressions are used to represent patterns of character strings and define the language of tokens. Finite automata are mathematical models for describing scanning algorithms using states, transitions, and acceptance.
This document discusses regular languages and finite automata. It begins with an overview of regular expressions and the introduction to the theory of finite automata. It then explains that finite automata are used in text processing, compilers, and hardware design. The document goes on to define automata theory and discusses deterministic finite automata (DFA) and nondeterministic finite automata (NFA). It notes that while NFA allow epsilon transitions and multiple state transitions, DFA and NFA have equivalent computational power.
This document discusses context-free grammars and parsing. It defines context-free grammars and how they are used to specify the syntactic structure of programming languages. Key points include:
- Context-free grammars use recursive rules and regular expressions to define a language's syntax.
- Parsing involves using a context-free grammar to derive a syntax tree from a sequence of tokens.
- Derivations show how strings of tokens are generated from a grammar's start symbol through replacements.
- The language defined by a grammar consists of all strings that can be derived from the start symbol.
The document provides information about regular expressions and finite automata. It discusses how regular expressions are used to describe programming language tokens. It explains how regular expressions map to languages and the basic operations used to build regular expressions like concatenation, alternation, and Kleene closure. The document also discusses deterministic finite automata (DFAs), non-deterministic finite automata (NFAs), and algorithms for converting regular expressions to NFAs and DFAs. It covers minimizing DFAs and using finite automata for lexical analysis in scanners.
This document summarizes a lecture on lexical analysis in compiler design. It discusses the role of the lexical analyzer in separating a compiler into lexical analysis and parsing phases. Lexical analyzers tokenize input strings by matching lexemes to patterns, producing a sequence of tokens. Regular expressions are used to specify patterns and define languages of valid tokens. Transition diagrams are constructed to represent the patterns and guide recognition of tokens in the input. The lecture also covers topics like lexical errors, input buffering, and techniques for recognizing reserved words and identifiers.
The document discusses lexical analysis in compiler design. It covers the role of the lexical analyzer, tokenization, and representation of tokens using finite automata. Regular expressions are used to formally specify patterns for tokens. A lexical analyzer generator converts these specifications into a finite state machine (FSM) implementation to recognize tokens in the input stream. The FSM is typically a deterministic finite automaton (DFA) for efficiency, even though a nondeterministic finite automaton (NFA) may require fewer states.
The document discusses lexical analysis and how it relates to parsing in compilers. It introduces basic terminology like tokens, patterns, lexemes, and attributes. It describes how a lexical analyzer works by scanning input, identifying tokens, and sending tokens to a parser. Regular expressions are used to specify patterns for token recognition. Finite automata like nondeterministic and deterministic finite automata are constructed from regular expressions to recognize tokens.
The document contains questions and answers related to compiler design topics such as parsing, grammars, syntax analysis, error handling, derivation, sentential forms, parse trees, ambiguity, left and right recursion elimination etc. Key points discussed are:
1. The role of parser is to verify the string of tokens generated by lexical analyzer according to the grammar rules and detect syntax errors. It outputs a parse tree.
2. Common parsing methods are top-down, bottom-up and universal. Top-down methods include LL, LR. Bottom-up methods include LR, LALR.
3. Errors can be lexical, syntactic, semantic and logical detected by different compiler phases. Error recovery strategies include panic mode
The document discusses the role of the parser in compiler design. It explains that the parser takes a stream of tokens from the lexical analyzer and checks if the source program satisfies the rules of the context-free grammar. If so, it creates a parse tree representing the syntactic structure. Parsers are categorized as top-down or bottom-up based on the direction they build the parse tree. The document also covers context-free grammars, derivations, parse trees, ambiguity, and techniques for eliminating left-recursion from grammars.
The document discusses compilers and their components. It begins by defining a compiler as a program that translates programs written in one language into another language. It then describes the main components of a compiler: lexical analysis, syntax analysis, semantic analysis, code generation, and code optimization. The document provides details on each component and how they work together in the compilation process from a source language to a target language.
This document provides an overview of lexical analysis, which is the first phase of a compiler. It discusses what lexical analysis involves, defines important terms like tokens and lexemes, and describes how regular expressions are used to specify patterns for token recognition. Finite state automata are used to implement the recognition of tokens from patterns. The document also introduces Lexical Analyzer Generator (Lex) as a tool for automatically generating scanners/lexers from token specifications written as regular expressions. Key aspects of the Lex tool like its structure and use of rules/actions are outlined.
The document discusses lexical analysis and lexical analyzer generators. It begins by explaining that lexical analysis separates a program into tokens, which simplifies parser design and implementation. It then covers topics like token attributes, patterns and lexemes, regular expressions for specifying patterns, converting regular expressions to nondeterministic finite automata (NFAs) and then deterministic finite automata (DFAs). The document provides examples and algorithms for these conversions to generate a lexical analyzer from token specifications.
compiler Design course material chapter 2gadisaAdamu
The document provides an overview of lexical analysis in compiler design. It discusses the role of the lexical analyzer in reading characters from a source program and grouping them into lexemes and tokens. Regular expressions are used to specify patterns for tokens. Non-deterministic finite automata (NFA) and deterministic finite automata (DFA) are used to recognize patterns and languages. Thompson's construction is used to translate regular expressions to NFAs, and subset construction is used to translate NFAs to equivalent DFAs. This process is used in lexical analyzer generators to automate the recognition of language tokens from regular expressions.
- Lexical analyzer reads source program character by character to produce tokens. It returns tokens to the parser one by one as requested.
- A token represents a set of strings defined by a pattern and has a type and attribute to uniquely identify a lexeme. Regular expressions are used to specify patterns for tokens.
- A finite automaton can be used as a lexical analyzer to recognize tokens. Non-deterministic finite automata (NFA) and deterministic finite automata (DFA) are commonly used, with DFA being more efficient for implementation. Regular expressions for tokens are first converted to NFA and then to DFA.
- Lexical analyzer reads source program character by character to produce tokens. It returns tokens to the parser one by one as requested.
- A token represents a set of strings defined by a pattern and has a type and attribute to uniquely identify a lexeme. Regular expressions are used to specify patterns for tokens.
- A finite automaton can be used as a lexical analyzer to recognize tokens. Non-deterministic finite automata (NFA) and deterministic finite automata (DFA) are commonly used, with DFA being more efficient for implementation. Regular expressions for tokens are first converted to NFA and then to DFA.
The document discusses the role and implementation of a lexical analyzer. It can be summarized as:
1. A lexical analyzer scans source code, groups characters into lexemes, and produces tokens which it returns to the parser upon request. It handles tasks like removing whitespace and expanding macros.
2. It implements buffering techniques to efficiently scan large inputs and uses transition diagrams to represent patterns for matching tokens.
3. Regular expressions are used to specify patterns for tokens, and flex is a common language for implementing lexical analyzers based on these specifications.
The document discusses the structure and process of a compiler. It has two major phases - the front-end and back-end. The front-end performs analysis of the source code by recognizing legal/illegal programs, understanding semantics, and producing an intermediate representation. The back-end translates the intermediate representation into target code. The general structure includes lexical analysis, syntax analysis, semantic analysis, code generation and optimization phases.
Similar to A simple approach of lexical analyzers (20)
1) Data transfer instructions move data without changing it between memory and registers, between registers, and between registers and input/output devices. Common instructions include load, store, move, input, and output.
2) Data manipulation instructions perform operations on data to provide computational capabilities. These include arithmetic instructions like add and subtract, logical and bitwise instructions like AND and OR, and shift instructions.
3) Program control instructions alter program flow, like branches, jumps, calls, and returns. They use status bits set by operations to determine conditional branches. Subroutines use call and return instructions to branch to and from the main program.
Instruction formats can come in different types. R. Archana is an assistant professor in the computer science department at SACWC. She thanks the reader for their time.
The document discusses the role and process of lexical analysis using LEX. LEX is a tool that generates a lexical analyzer from regular expression rules. A LEX source program consists of auxiliary definitions for tokens and translation rules that match regular expressions to actions. The lexical analyzer created by LEX reads input one character at a time and finds the longest matching prefix, executes the corresponding action, and places the token in a buffer.
Notation, Regular Expressions in Lexical Specification, Error Handling, Finite Automata State Graphs, Epsilon Moves, Deterministic and Non-Deterministic Automata, Table Implementation of a DFA
Lexical Analysis, Tokens, Patterns, Lexemes, Example pattern, Stages of a Lexical Analyzer, Regular expressions to the lexical analysis, Implementation of Lexical Analyzer, Lexical analyzer: use as generator.
This document discusses minimizing the number of states in a deterministic finite automaton (DFA). It begins with an introduction to DFAs, defining their components. It then explains that states can be redundant and provides an example of two equivalent DFAs where one state is unnecessary. The task of DFA minimization is to automatically transform a given DFA into an equivalent state-minimized DFA. An algorithm for DFA minimization is described that works by partitioning states into groups based on whether their transitions lead to the same groups for each input symbol, and merging states that are not distinct. Examples are provided to illustrate the algorithm. In conclusion, it notes that DFA minimization is useful for applications like regular expression matching and compiler optimizations, and that the
The document discusses MapReduce, a programming model for distributed computing. It describes how MapReduce works like a Unix pipeline to efficiently process large amounts of data in parallel across clusters of computers. Key aspects covered include mappers and reducers, locality optimizations, input/output formats, and tools like counters, compression, and partitioners that can improve performance. An example word count program is provided to illustrate how MapReduce jobs are defined and executed.
This document discusses business intelligence and related topics. It begins by defining key terms like business analytics, BI, big data, and data mining. It then explains that businesses need support for decision making due to uncertainties and competition. The document outlines characteristics of good data for decision making and describes data mining as finding patterns in large datasets. It provides examples of BI applications and initiatives and discusses how the field of BI has evolved with the rise of data warehousing and data marts. Finally, it briefly covers some common data mining techniques like market basket analysis and cluster analysis.
The document provides an overview of Hadoop Distributed File System (HDFS):
1) HDFS is designed to reliably store very large files across commodity servers and is optimized for batch processing of huge datasets. It distributes data across clusters in a fault-tolerant way to handle hardware failures.
2) HDFS has a master/slave architecture with a NameNode that manages file system metadata and DataNodes that store file data blocks. Files are broken into blocks and replicated across DataNodes for reliability.
3) The NameNode tracks metadata like file locations and DataNode statuses, while DataNodes store and retrieve blocks. HDFS provides a unified namespace and facilitates reliable and high-throughput access to data.
R is an open source programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and tools. R was originally developed in the late 1980s and is currently maintained by an international volunteer team. The presentation introduces basic R syntax for performing calculations, storing results, creating and manipulating data vectors and matrices, and defining functions.
This document discusses different types of decision making and branching statements in C programming, including if, switch, conditional operator, and goto statements. It focuses on explaining the if statement in more detail. The if statement allows for conditional execution of code blocks depending on the evaluation of a test expression. There are several types of if statements including simple if, if-else, nested if-else, and else-if ladder statements. Flowcharts and examples are provided to illustrate the syntax and logic flow for each type of if statement.
Unguided media, also known as wireless transmission, transmits electromagnetic waves without using a physical medium. It includes three main categories: radio waves, which transmit signals omnidirectionally and are used for applications like FM radio; microwaves, which transmit focused beams between aligned antennas and are used for terrestrial and satellite communication; and infrared, which supports short-range communication between devices in closed areas. Each type has advantages like coverage area but also disadvantages such as susceptibility to environmental interference.
The document discusses different types of computer memory. It describes cache memory as very high speed memory between the CPU and main memory used to store frequently accessed data and programs. Primary/main memory is volatile semiconductor memory that holds currently running programs and data. RAM and ROM are types of main memory. RAM is read/write memory that stores data temporarily while power is on, while ROM is read-only memory that permanently stores basic input/output instructions. The document outlines characteristics and types of each memory including static RAM, dynamic RAM, programmable ROM, erasable programmable ROM, and electrically erasable programmable ROM.
The life cycle of a thread involves several stages: being born when created, started, running, and eventually dying. A thread can be in one of five states: newborn, runnable, running, blocked, or dead. Creating a thread involves implementing the Runnable interface, instantiating a Thread object with the Runnable object, and calling the start() method.
This document provides an overview of advanced Java programming concepts covered in the unit, including object-oriented programming, data types, variables, arrays, operators, inheritance, and control statements. It defines key concepts like classes, objects, encapsulation, polymorphism, and inheritance. For data types, it covers primitive types like int, float, boolean and char, as well as arrays. Operators covered include unary, arithmetic, relational, logical, and assignment operators. The document also discusses variables, arrays, and control statements like selection, iteration, and jump statements.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
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Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
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How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
1. A SIMPLE APPROACH TO THE DESIGN OF
LEXICAL ANALYZERS
Myself Archana R
Assistant Professor In
Department Of Computer Science
SACWC.
I am here because I love to give presentations.
2. Simple Approach
Construct a diagram that illustrates the structure of the tokens of the
source language , and then to hand-translate the diagram
the diagram into a program for finding tokens.
Notes:
Efficient lexical analyzers can be produced in this manner.
3. Simple Approaches To Implement A
Lexical Analyzer
Pattern-directed programming approach
Pattern Matching technique.
Specify and design program that execute actions triggered by
patterns in strings.
Introduce a pattern-action language called Lexical for specifying
lexical analyzers .
Patterns are specified by regular expressions .
A compiler for Lexical can generate an efficient finite
automation recognizer for the regular expressions.
4. First phase of a compiler
1. Main task
- To read the input characters.
- To produce a sequence of tokens used by the parser for
syntax analysis.
- As an assistant of parser.
5. Lexical analyzer
Parser
Symbol table
Source program
token
Get next token
Interaction of lexical analyzer with parser
6. Processes in lexical analyzers
Scanning
*Pre-processing
-Strip out comments and white space
-Macro functions
Correlating error messages from compiler with source
program
*A line number can be associated with an error
message.
Lexical analysis
7. Regular Expression & Regular language
Regular Expression
A notation that allows us to define a pattern in a high
level language.
Regular language
Each regular expression r denotes language L(r) (the
set of sentences relating to the regular expression r) .
Notes:
Each word in a program can be expressed in a regular
expression.
8. Specification of Tokens
1) The rule of regular expression over alphabet ∑
* ε is a regular expression that denote {ε}
ε is regular expression .
{ε} is the related regular language.
2) If a is a symbol in ∑, then a is a regular expression that
denotes {a}
a is regular expression .
{a} is the related regular language.
9. 3) Suppose α and β are regular expressions, then α| β, α β, α * , β *
is also a regular expression.
Notes : Rules 1) and 2) form the basis of the definition; rule 3)
provides the inductive step.
4) Algebraic laws of regular expressions
1) α| β = β | α 2) α |(β |γ)=(α | β)| γ α(β γ) =(α β) γ
3)εα = αε= α 5)(α *)*= α *
6) α *= α +|ε α + = α α * = α * α
7) (α | β)*= (α * | β *)*= (α * β *)*
10. Notational Short-hands
a)One or more instances
( r ) digit+
b)Zero or one instance
r? is a shorthand for r| (E(+|-)?digits)?
c)Character classes
[a-z] denotes a|b|c|…|z
[A-Za-z] [A-Za-z0-9]
11. Implementing a Transition Diagram
Each state gets a segment of code.
If there are edges leaving a state, then its code reads a character and
selects an edge to follow, if possible.
Use next char() to read next character from the input buffer.
A generalized transition diagram Finite Automation
Deterministic or non-deterministic FA.
Non-deterministic means that more than one transition out of a state
may be possible on the same input symbol.
12. The model of recognition of
tokens
INPUT BUFFER
Lexeme being
FA simulator
i f d 2 =……..
13. The FA simulator for Identifiers is
Letter
Letter
digit
Which represent the rule:
identifier=letter(letter | digit)*
14. Deterministic FA (DFA)
1) In a DFA, no state has an transition.
2)In a DFA, for each state s and input symbol a,
there is at most one edge labeled a leaving s.
3)To describe a FA , we use the transition graph or
transition table .
4)A DFA accepts an input string x if and only if
there is some path in the transition graph from start
state to some accepting state.
15. So ,the DFA is
M=({0,1,2,3,},{a,b,c}, move,0,{1,2,3})
move:move(0,a)=1
move(0,b)=1 move(0,c)=1
move(1,a)=1 move(1,b)=1
move(1,c)=1 move(2,a)=3
move(2,b)=2 move(2,c)=2
move(3,b)=3 move(3,c)=3 32
16. A generalized transition diagram
Finite Automation
Deterministic or non-deterministic FA
Non-deterministic means that
more than one transition out of a state may be
possible on the same input symbol.
17. Non-deterministic FA (NFA)
1) In a NFA the same character can label two or more transitions out
of one state.
2) In a NFA is a legal input symbol.
3) A DFA is a special case of a NFA.
4)A NFA accepts an input string x if and only if there is some path in
the transition graph from start state to some accepting state. A
path can be represented by a sequence of state transitions called
moves.
5) It accepts the language defined by a NFA is the set of input strings.