21CSC203P

Advanced Programming Practice

UNIT 1 PPT
(2021 regulation)

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Outline of the Presentation
S-3 What is Programming Languages
S-5 Elements of Programming languages
S-7 Programming Language Theory
S-9 Bohm- Jacopini structured program theorem
S-13 Multiple Programming Paradigm
S-15 Programming Paradigm hierarchy
S-18 Imperative Paradigm: Procedural, Object-Oriented and Parallel processing
S-22 Declarative programming paradigm: Logic, Functional and Database
processing - Machine Codes-Procedural and Object-Oriented Programming
S-24 Suitability of Multiple paradigms in the programming language
S-27 Subroutine, method call overhead and Dynamic memory allocation for
message and object storage
S-30 Dynamically dispatched message calls and direct procedure call overheads
S-33 Object Serialization
S-35 parallel Computing

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 1. What is Programming Languages ?
• Programming languages are formal languages designed to communicate instructions to a computer
system or a computing device. They serve as a means for humans to write programs and develop
software applications that can be executed by computers. Each programming language has its own
syntax and set of rules that define how programs should be written.

• Programming languages can be classified into different types, including:

1.Low-Level Languages: These languages are close to machine code and provide little or no
abstraction from the computer's hardware. Examples include assembly languages and machine
languages.

2.High-Level Languages: These languages are designed to be closer to human language and provide a
higher level of abstraction. They offer built-in functions, libraries, and data structures that simplify
programming tasks. Examples include Python, Java, C++, C#, Ruby, and JavaScript.

3.Scripting Languages: These languages are often interpreted rather than compiled and are used to
automate tasks or perform specific functions within a larger program. Examples include Python,
Perl, Ruby, and JavaScript.

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 What is Programming Languages

4. Object-Oriented Languages: These languages emphasize the concept of objects, which

encapsulate both data and the functions (methods) that operate on that data. Examples
include Java, C++, C#, and Python.
5. Functional Languages: These languages treat computation as the evaluation of
mathematical functions and avoid changing state or mutable data. Examples include
Haskell, Lisp, and Erlang.
6. Domain-Specific Languages (DSLs): These languages are designed for specific domains or
problem areas, with specialized syntax and features tailored to those domains. Examples
include SQL for database management, HTML/CSS for web development, and MATLAB for
numerical computing.
Programming languages have different strengths and weaknesses, and developers choose a
language based on factors such as project requirements, performance needs, development
speed, community support, and personal preference. Learning multiple languages can give
programmers flexibility and allow them to solve different types of problems more effectively.

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 2. Elements of Programming languages
• Here are the fundamental elements commonly found in programming languages:
1.Variables: Variables are used to store and manipulate data during program
execution. They have a name, a type, and a value associated with them.
Programming languages may have different rules for variable declaration,
initialization, and scoping.
2.Data Types: Programming languages support various data types, such as integers,
floating-point numbers, characters, strings, booleans, arrays, and more. Data types
define the kind of values that can be stored and manipulated in variables.
3.Operators: Operators perform operations on data, such as arithmetic operations
(addition, subtraction, etc.), comparison operations (equal to, greater than, etc.),
logical operations (AND, OR), and assignment operations (assigning values to
variables).
4.Control Structures: Control structures allow programmers to control the flow of
execution in a program. Common control structures include conditionals (if-else
statements, switch statements), loops (for loops, while loops), and branching (goto
statements).
5.Functions and Procedures: Functions and procedures are reusable blocks of code
that perform a specific task. They take input parameters, perform computations, and
optionally return values. Functions and procedures facilitate modular and organized
programming.

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5. Expressions: Expressions are combinations of variables, constants, operators,
and function calls that evaluate to a value. They are used to perform
calculations, make decisions, and manipulate data.
6.Statements: Statements are individual instructions or commands in a
programming language. They perform specific actions or control the program's
behavior. Examples include variable assignments, function calls, and control
flow statements.
7.Syntax: Syntax defines the rules and structure of a programming language. It
specifies how programs should be written using a specific set of symbols,
keywords, and rules. Syntax determines the correctness and readability of the
code.
8.Comments: Comments are used to add explanatory or descriptive text within the
code. They are ignored by the compiler or interpreter and serve as
documentation for programmers or readers of the code.
9.Libraries and Modules: Libraries or modules are prewritten collections of code
that provide additional functionality to a programming language. They contain
reusable functions, classes, or other components that can be imported into
programs to extend their capabilities.
• These are some of the core elements of programming languages. Different
programming languages may have additional features, syntax rules, or concepts
specific to their design and purpose.

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 3. Programming Language Theory
• Programming Language Theory is a field of computer science that studies the
design, analysis, and implementation of programming languages. It focuses on
understanding the principles, concepts, and foundations that underlie
programming languages and their use.

• Programming Language Theory covers a broad range of topics, including:

• Syntax and Semantics: This area deals with the formal representation and
interpretation of programming language constructs. It involves defining the syntax
(grammar) of a language and specifying the meaning (semantics) of its constructs.

• Type Systems: Type systems define and enforce the rules for assigning types to
expressions and variables in a programming language. They ensure type safety and
help catch errors at compile-time.

• Programming Language Design and Implementation: This aspect involves the

process of creating new programming languages or extending existing ones. It
explores language features, constructs, and paradigms, and how they can be
efficiently implemented.

• Programming Language Semantics: Semantics concerns the meaning and behavior

of programs. It involves defining mathematical models or operational semantics to
formally describe program execution and behavior.

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• Programming Language Analysis: This area focuses on static and dynamic analysis
of programs, including type checking, program verification, optimization
techniques, and program understanding.

• Formal Methods: Formal methods involve using mathematical techniques to

analyze and prove properties of programs and programming languages. It aims to
ensure correctness, safety, and reliability of software systems.

• Language Paradigms: Programming Language Theory explores different

programming paradigms, such as procedural, object-oriented, functional, logic, and
concurrent programming. It investigates the principles, strengths, and limitations of
each paradigm.
• Language Implementation Techniques: This aspect covers compiler design,
interpretation, code generation, runtime systems, and virtual machines. It
investigates efficient strategies for executing programs written in various
programming languages.

• Language Expressiveness: Language expressiveness refers to the power and

flexibility of a programming language in expressing different computations,
algorithms, and abstractions. It explores the trade-offs between expressiveness and
other factors such as performance and readability.

• Programming Language Theory provides the foundation for understanding and

reasoning about programming languages. It helps in the development of new
languages, designing better programming constructs, improving software quality,
and building efficient and reliable software systems

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4. BOhm-Jacopini theorem/ Structured Program
Theorem

The Böhm-Jacopini theorem, formulated independently by Corrado

Böhm and Giuseppe Jacopini, is a fundamental result in programming
language theory.
It states that any computation can be performed using only three basic
control structures: sequence, selection (if-then-else), and iteration (while
or for loops). This means that any program, regardless of its complexity,
can be expressed using these three control structures alone.

The theorem is significant because it establishes that more complex

control structures, such as goto statements or multiple exit points, are
not necessary to express any algorithm.
By limiting the control structures to sequence, selection, and
iteration, the theorem promotes structured programming, which
emphasizes readable and modular code.

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10
To understand the Böhm-Jacopini theorem, let's look at the three
basic control structures it allows:
i) Sequence: This control structure allows a series of statements to be executed
in a specific order, one after another.
Executing one subprogram, and then another subprogram (sequence)
For example:
Statement 1;
Statement 2;
Statement 3;
To review, sequence performs some operations S1;
S2 meaning perform S1; then perform S2.

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Java Program for sequence
import java .io.*;
class sample
{
public static void main(String args[])
{
System.out.println(“Hai”);
}
}
Save : sample.java
Compile: javac sample.java
Run: java sample

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ii) Selection (if-then-else): This control structure enables a program to
make decisions based on certain conditions. It executes one set of statements if a
condition is true and another set of statements if the condition is false.
Executing one of two subprograms according to the value of a boolean expression
(selection).
Selection says, if Q is true, then perform S1, else perform S2.
For example: (if, if-else, if-elseif-else, nested-if)
if (condition) {
Statement 1;
} else {
Statement 2;
}

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Selection Statement

If else
Syntax:
if test expression:
statement(s)

If else
Syntax:
if test expression:
Body of if
else:
Body of else

If elif else
Syntax:
if test expression:
Body of if
elif test expression:
Body of elif
else:
Body of else
Arun, Asst Prof, Dept of SWE, SRMIST
Python program for Selection:
num = float(input("Enter a number: "))
if num > 0:
print("Positive number")
print("This is always printed")

Example:
num = float(input("Enter a number: "))
if num >= 0:
print("Positive or Zero")
else:
print("Negative number")

Example:
num = float(input("Enter a number: "))
if num > 0:
print("Positive number")
elif num == 0:
print("Zero")
else:
print("Negative number") 15
Java Program for selection
import java .io.*;
class sample
{
public static void main(String args[])
{
int a=5, b=3, c>2;
if(a>b)&&(a>c)
System.out.println(“a is greater than b and c”);
elseif(b>a)&&(b>c)
System.out.println(“b is greater than a and c”);
else
System.out.println(“c is greater than a and b”);
}
}
Save : sample.java
Compile: javac sample.java
Run: java sample
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iii) Iteration (while or for loops): This control structure allows a set of
statements to be repeated until a certain condition is satisfied. It executes the
statements repeatedly as long as the condition holds true.
Example (for, while, do-while)
Loop says while Q is true, do S.
For example:
i) while (condition) {
Statement; }
program until a Boolean expression is true (iteration)
ii) do{
Statement;
}while (condition);
iii) for(initialization; condition; increment/decrement)
{
statement;}

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Java Program for iteration
import java .io.*;
class sample
{
public static void main(String args[])
{
int sum=0,i;
for(i=0;i<10;i++)
{
sum=sum+i;
}
System.out.println(sum);
}
}
Save : sample.java
Compile: javac sample.java
Run: java sample

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Python Program for Iteration
while loop
Syntax:
while expression:
statement(s)
Example:
count = 0
while (count < 9):
print 'The count is:', count
count = count + 1
print "Good bye!”
For Loop
Syntax:
for iterating_var in sequence:
statements(s)
Example:
for letter in 'Python': # First Example
print 'Current Letter :', letter

fruits = ['banana', 'apple', 'mango']

for index in range(len(fruits)):
print 'Current fruit :', fruits[index]
print "Good bye!“

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• The Böhm-Jacopini theorem states that any program can be structured
using these three control structures alone. This means that complex
programs with loops, conditionals, and multiple branches can be
rewritten using only sequence, selection, and iteration constructs.
• The theorem assures that these basic structures are sufficient to
express any algorithm or computation, promoting clarity and simplicity
in program design.
• While the Böhm-Jacopini theorem advocates for the use of structured
programming principles, it is important to note that modern
programming languages often provide additional control structures and
abstractions to enhance code readability and maintainability.
• These higher-level constructs build upon the foundations established by
the theorem but allow for more expressive and efficient programming
• The Böhm-Jacopini theorem, also called structured program theorem,
stated that working out a function is possible by combining
subprograms in only three manners:

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 5. Multiple programming paradigms
• Multiple programming paradigms, also known as multi-paradigm programming, refers to the ability of a
programming language to support and integrate multiple programming styles or paradigms within a single
language. A programming paradigm is a way of thinking and structuring programs based on certain principles
and concepts.

• A multi-paradigm programming language is a computer language that provides several abstraction

mechanisms. Different programming problems require different approaches, so a good programming language
should support multiple paradigms .

• Traditionally, programming languages have been associated with a specific paradigm, such as procedural,
object-oriented, or functional. However, with the advancement of programming language design, many
modern languages have incorporated elements and features from multiple paradigms, providing developers
with more flexibility and expressive power.

• Procedural Programming: This paradigm focuses on the step-by-step execution of a sequence of instructions
or procedures. It emphasizes the use of procedures or functions to organize and structure code.

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• Some common programming paradigms include
1. Structural Programming Paradigm
2. Procedural Programming Paradigm
3. Object-Oriented Programming Paradigm
4. Concurrent Programming Paradigm
5. Declarative Programming Paradigm
6. Graphical User Interface Based Programming Paradigm
7. Functional Programming Paradigm
8. Logic Programming Paradigm
9. Parallel Programming Paradigm
10. Network Programming Paradigm
11. Automata Based programming Paradigm
12. Symbolic Programming Paradigm
13. Event Programming Paradigm
14. Imperative Programming Paradigm

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• Object-Oriented Programming (OOP): OOP is based on the concept of objects that
encapsulate data and behavior. It promotes modularity, reusability, and data
abstraction. Languages like C++, Java, and Python support OOP.
• Functional Programming: This paradigm treats computation as the evaluation of
mathematical functions. It emphasizes immutability, pure functions, and higher-
order functions. Languages like Haskell, Lisp, and Scala support functional
programming.
• Declarative Programming: Declarative programming focuses on describing the
desired result rather than specifying the detailed steps to achieve it. Examples
include SQL for database queries and HTML/CSS for web development.
• Logic Programming: Logic programming involves defining relationships and rules
and letting the program reason about queries and logical inferences. Prolog is a
popular logic programming language.
• Concurrent Programming: Concurrent programming deals with handling multiple
tasks or processes that execute concurrently or in parallel. It addresses
synchronization, communication, and coordination among concurrent processes.
Languages like Go, Erlang, and Java (with concurrency libraries) provide support
for concurrent programming.
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• Structured Programming: Structured programming builds upon imperative programming
and emphasizes the use of structured control flow constructs like loops and conditionals.
It aims to improve code readability and maintainability by using procedures, functions,
and modules for organizing and structuring code. Languages like C, Pascal, and Python
support structured programming.

• By supporting multiple paradigms, programming languages can address different problem

domains and allow developers to choose the most appropriate style for a given task. This
flexibility enables the combination of different programming techniques within a single
program, leading to more expressive and maintainable code. It also promotes code reuse
and interoperability between different paradigms, as developers can leverage the strengths
of each paradigm to solve specific challenges

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 6. Programming Paradigm hierarchy
• A hierarchy in programming is an organizational structure in which items are ranked
according to levels of importance. The concept of a programming paradigm hierarchy
refers to the organization and relationship between different programming paradigms
based on their characteristics and capabilities. It provides a way to understand how
various paradigms relate to each other and how they build upon or differ from one
another in terms of abstraction, data handling, control flow, and programming
concepts
• While there is no universally accepted
hierarchy, here is a general representation
of the programming paradigm hierarchy:

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Imperative Paradigm: Procedural, Object-Oriented and Parallel
processing
• The imperative paradigm is a programming paradigm that focuses on specifying a
sequence of instructions or statements that the computer must execute to achieve a
desired outcome. It involves describing the steps or procedures to be followed in order to
solve a problem.
The imperative paradigm is characterized by mutable state and explicit control flow.
i) Procedural Programming:
• Procedural programming is a specific form of the imperative paradigm that organizes code
into procedures or subroutines. It emphasizes the use of procedures or functions, which
are named blocks of code that can be called and executed from different parts of the
program. Procedural programming promotes code modularity, reusability, and structured
control flow using constructs like loops and conditionals. Languages like C, Pascal, and
Fortran are examples of procedural programming languages.
ii) Object-Oriented Programming (OOP):
• Object-oriented programming (OOP) extends the imperative paradigm by introducing the
concept of objects. In OOP, objects are entities that encapsulate data (attributes) and
behavior (methods or functions). OOP emphasizes concepts such as data abstraction,
encapsulation, inheritance, and polymorphism. It allows for modular and reusable code
through the use of classes, which define the blueprint for creating objects. Languages like
Java, C++, and Python are examples of languages that support object-oriented
programming.
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iii) Parallel Processing :
Parallel processing is a concept that refers to the simultaneous execution of multiple
tasks or processes. It involves dividing a problem into smaller subproblems that can be
executed concurrently on multiple processors or cores. The goal of parallel processing is
to improve performance and efficiency by exploiting the available computational
resources.
• Parallel processing can be achieved through various techniques, such as multi-threading,
multiprocessing, and distributed computing. Languages like Go, Erlang, and Java (with
concurrency libraries) provide support for parallel processing.
• In summary, the imperative paradigm focuses on specifying a sequence of instructions to
be executed by the computer. Procedural programming organizes code into procedures or
subroutines, while object-oriented programming introduces the concept of objects for data
encapsulation and modular code.
• Parallel processing allows for the simultaneous execution of multiple tasks or processes to
improve performance. These concepts and paradigms provide different approaches and
techniques for structuring and solving problems within the imperative programming
paradigm
•

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Declarative programming paradigm: (Logic, Functional and Database
processing)
• Declarative programming is a programming paradigm that focuses on describing what
needs to be achieved rather than how to achieve it.
• It emphasizes the use of declarative statements or expressions that specify the desired
result or outcome, leaving the details of how the computation is carried out to the
underlying system or interpreter.
• Declarative programming consists of several sub-paradigms, including logic
programming, functional programming, and database processing:

i) Logic Programming:
• Logic programming is a declarative programming paradigm that is based on formal logic.
It involves defining relationships, rules, and constraints using logical formulas.
• The programmer specifies a set of logical rules, and the program uses logical inference to
query and reason about these rules.
• The most well-known logic programming language is Prolog, which provides mechanisms
for defining relations and conducting logical queries.

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ii) Functional Programming:
• Functional programming is another declarative programming paradigm
that treats computation as the evaluation of mathematical functions.
• It emphasizes the use of pure functions, which have no side effects and
always produce the same output for the same input.
• Functional programming promotes immutability, higher-order functions,
and the composition of functions to achieve desired results. Languages
like Haskell, Lisp, and Scala support functional programming.
iii) Database Processing:
• Database processing is a specific application of declarative programming
that deals with manipulating and querying databases.
• SQL (Structured Query Language) is a common language used in
database processing, which allows programmers to declaratively specify
operations like querying, inserting, updating, and deleting data from
databases.
• In SQL, the programmer describes the desired results and lets the
database management system (DBMS) handle the optimization and
execution details.

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• In all of these declarative programming sub-paradigms, the focus is on expressing the
desired outcome or relationship rather than specifying a step-by-step procedure.

• The systems or interpreters responsible for executing declarative programs handle the
details of how to achieve the desired result efficiently.

• Declarative programming allows for concise and expressive code, code reuse, and a higher
level of abstraction, making programs more maintainable and easier to reason about.

• However, it may have performance implications, as the underlying system must determine
the most efficient way to execute the declarative statements or queries

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8. Machine Codes – Procedural and Object Oriented
Programming

• Machine code, also known as machine language, is the lowest level of programming
language that can be directly executed by a computer's processor.
• It consists of binary instructions that represent specific operations and data
manipulations understood by the computer's hardware.
• Machine code instructions are specific to a particular computer architecture or processor.

• Procedural Programming with Machine Code:

• In procedural programming, machine code instructions are used to write programs that
follow a procedural structure.
• Procedural programming focuses on breaking down a problem into a series of procedures
or functions, which are then executed sequentially. Each procedure or function contains
a set of machine code instructions that perform a specific task.
• In procedural programming with machine code, the programmer directly writes or
manipulates the machine code instructions to implement the desired functionality.
• The programmer needs to understand the low-level details of the computer's architecture,
instruction set, and memory layout to write efficient and correct code.

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Procedural Programming

• High level languages such as COBOL, FORTRAN and C, is commonly known as procedure oriented programming(POP).
In the procedure oriented programming, program is divided into sub programs or modules and then assembled to form a
complete program. These modules are called functions.
• The problem is viewed as a sequence of things to be done.
• The primary focus is on functions.
• Procedure-oriented programming basically consists of writing a list of instructions for the computer to follow and
organizing these instructions into groups known as functions.
• In a multi-function program, many important data items are placed as global so that they may be accessed by all functions.
Each function may have its own local data. If a function made any changes to global data, these changes will reflect in other
functions. Global data are more unsafe to an accidental change by a function. In a large program it is very difficult to
identify what data is used by which function.
• This approach does not model real world problems. This is because functions are action-oriented and do not really
correspond to the elements of the problem.
Typical structure of procedure-oriented program

• A function is a block of organized, reusable code

that is used to perform a single, related action.

Functions provide better modularity for your
application and a high degree of code reusing.
• There are 2 types of function
• Built-in function ex. println()
• User defined function - User can create
their own functions.

Arun, Asst Prof, Dept of SWE, SRMIST

Characteristics of Procedure-Oriented Programming

• Emphasis is on doing things.

• Large programs are divided into smaller programs known as functions.

• Most of the functions share global data.

• Data move openly around the system from function to function.

• Functions transform data from one form to another.

• Employs top-down approach in program design.

• There are different ways in which parameter data can be passed into and out of methods
and functions.
• Let us assume that a function B() is called from another function A(). In this case A is
called the “caller function” and B is called the “called function or callee function”.
Also, the arguments which A sends to B are called actual arguments and the parameters
of B are called formal arguments.
Types of parameters:
i) Formal Parameter: A variable and its type as they appear in the prototype of the
function or method.
Syntax:
function_name(datatype variable_name)
ii) Actual Parameter: The variable or expression corresponding to a formal parameter that
appears in the function or method call in the calling environment.
Syntax:
func_name(variable name(s));

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Object-Oriented Programming with Machine Code:
• Object-oriented programming (OOP) is a higher-level programming paradigm that emphasizes
objects as the fundamental building blocks of programs.
• OOP provides concepts such as classes, objects, encapsulation, inheritance, and polymorphism.
While machine code is not inherently object-oriented, it can still be used to implement object-
oriented programming principles at a lower level.
• In an object-oriented programming approach with machine code, the programmer can design and
implement their own object-oriented system using machine code instructions.
• This involves designing memory layouts, defining structures for objects, implementing inheritance
and polymorphism mechanisms manually, and managing method dispatching.
• However, implementing object-oriented programming directly with machine code can be complex
and error-prone, as it requires handling memory management, vtables, and other low-level details
manually.
• This approach is rarely used in practice due to the availability of high-level programming
languages and compilers that abstract away these low-level details.

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Structure of object-oriented programming

• The structure, or building blocks, of object-oriented programming include the following:

• Classes are user-defined data types that act as the blueprint for individual objects, attributes and methods.

• Objects are instances of a class created with specifically defined data. Objects can correspond to real-world
objects or an abstract entity. When class is defined initially, the description is the only object that is defined.

• Methods are functions that are defined inside a class that describe the behaviors of an object. Each method
contained in class definitions starts with a reference to an instance object. Additionally, the subroutines
contained in an object are called instance methods. Programmers use methods for reusability or keeping
functionality encapsulated inside one object at a time.

• Attributes are defined in the class template and represent the state of an object. Objects will have data stored
in the attributes field. Class attributes belong to the class itself.

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 9. Suitability of Multiple paradigms in the
programming language
• The suitability of multiple paradigms in a programming language refers to the extent to
which the language supports and integrates different programming paradigms effectively.
• It assesses how well a programming language accommodates the principles and concepts
of various paradigms and allows developers to seamlessly use multiple paradigms within
a single codebase. The suitability of multiple paradigms can have several advantages:
Benefits of using multiple paradigms in a programming language:
• Flexibility: Different paradigms offer various ways to approach problems, giving developers
flexibility in choosing the most suitable approach for a particular task.
• Expressiveness: Multiple paradigms allow developers to express ideas and algorithms in ways
that best match the problem's nature, leading to clearer and more concise code.
• Code Reuse: By combining paradigms, developers can leverage the strengths of each paradigm,
leading to better code reuse and more efficient development.
• Performance Optimization: Certain paradigms, like imperative programming, can offer
performance benefits when handling low-level operations.
• Learning and Growth: Working with multiple paradigms exposes developers to different ways of
thinking and problem-solving, which can lead to personal and professional growth.
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Drawbacks of using multiple paradigms in a programming language:
• Complexity: Integrating multiple paradigms can lead to complex code,
making it harder to understand and maintain, especially for
inexperienced developers.
• Consistency: Maintaining a consistent coding style and design
philosophy might become challenging when combining paradigms.
• Learning Curve: Developers need to be proficient in multiple
paradigms, which could increase the learning curve, especially for
newcomers to the language.
• Tooling and Community Support: Some language features and
libraries might be better suited for specific paradigms, and using
multiple paradigms could limit the availability of suitable tools and
community support.
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Challenges and considerations when incorporating multiple paradigms:
• Problem-Specific Solutions: Different programming paradigms are better suited for
addressing specific types of problems. By incorporating multiple paradigms, a language
can offer a more diverse set of tools to tackle various challenges. This can be especially
valuable when developing complex systems that involve different aspects, such as user
interfaces, data processing, and system-level operations.
• Language Extensibility: Supporting multiple paradigms can enhance the extensibility of a
language. Developers can choose the most appropriate paradigm or even combine
paradigms to build custom solutions tailored to their needs. This flexibility can make the
language more adaptable and attractive to a broader range of developers.
• Legacy Code Integration: Many existing codebases are written in languages that support
a specific paradigm. By allowing multiple paradigms, a language can make it easier to
integrate legacy code into new projects, reducing the need for a complete rewrite and
facilitating the transition to the new language.
• Developer Productivity: Different paradigms can be more intuitive and efficient for
specific tasks. When a language supports multiple paradigms, developers can leverage
the strengths of each paradigm, leading to increased productivity and reduced
development time.
• Learning and Skill Transfer: Developers who are proficient in one paradigm may find it
easier to learn other paradigms if they are all supported within the same language. This
can improve cross-functional collaboration and enable developers to apply knowledge
from one paradigm to solve problems in other areas.

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• Ecosystem and Libraries: A diverse language ecosystem can benefit from a
wider range of libraries and tools that cater to different paradigms. This can
result in a richer selection of resources for developers to use and
contribute to.
• Language Complexity: Supporting multiple paradigms can make the
language more complex, leading to potential confusion for developers,
especially those new to the language. Striking a balance between flexibility
and simplicity is essential.
• Interoperability: When combining paradigms, it's crucial to ensure that
they work well together and do not create conflicts or unexpected
behaviors. Language designers must carefully design and test how different
paradigms interact.
• Consistency and Codebase Maintainability: A codebase that allows mixing
multiple paradigms can become challenging to maintain and understand,
especially as the project grows and more developers contribute.
• Performance Overhead: Certain paradigms might introduce additional
overhead or limitations that could affect performance. Language designers
need to carefully consider performance implications when incorporating
new paradigms.
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• However, it's important to note that incorporating multiple paradigms in a
programming language can also introduce complexity.
• Developers need to carefully consider the trade-offs and design decisions
associated with supporting multiple paradigms.
• Striking a balance between providing flexibility and maintaining language
consistency can be a challenge.
• Overall, the suitability of multiple paradigms in a programming language
provides developers with flexibility, expressiveness, code reusability, and the
ability to choose the most appropriate approach for solving different problems.
• It empowers developers to write efficient and maintainable code and encourages
innovation and growth within the programming community

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 10. Subroutine
• A subroutine is a named sequence of instructions within a program that
performs a specific task. It is also known as a function or procedure.
Subroutines help in organizing code, promoting code reusability, and improving
code readability.
• When a subroutine is called, the program jumps to the subroutine's location,
executes its instructions, and returns to the point of the program from where it
was called.

• Method Call Overhead:

• Method call overhead refers to the additional time and resources required to
invoke a method or function in an object-oriented programming language. When
a method is called, there is a certain amount of overhead involved in setting up
the call, passing arguments, and returning results.
• This overhead includes tasks such as pushing arguments onto the stack, saving
registers, and managing the call stack. While the overhead is typically small and
negligible for most applications, it becomes more significant in high-performance
scenarios or when calling methods frequently in tight loops.
•
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Dynamic Memory Allocation for Message and Object
Storage
• In object-oriented programming, objects are instances of classes that
encapsulate data and behavior.
• Dynamic memory allocation is often used to allocate memory for objects during
runtime.
• When an object is created, memory is dynamically allocated from the heap to
store the object's data.
• This dynamic memory allocation allows objects to have a flexible lifetime and
enables the creation and destruction of objects as needed.
• Message passing is a mechanism used in object-oriented programming to invoke
methods or communicate between objects.
• When a message is sent to an object, the object's method is invoked to handle
the message.
• Depending on the programming language and implementation, the message
might contain information such as the name of the method and the arguments
to be passed.
•
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• Dynamic memory allocation for message and object storage involves the
allocation and deallocation of memory during runtime, as objects are created,
used, and destroyed.
• This flexibility in memory allocation allows for dynamic object creation,
polymorphism, and memory management.
• However, dynamic memory allocation comes with additional overhead compared
to static memory allocation. There is a cost associated with allocating and
deallocating memory, and improper memory management can lead to memory
leaks or fragmentation.
• Efficient memory allocation strategies and techniques, such as pooling, garbage
collection, or smart pointers, are often employed to optimize memory usage and
minimize overhead in dynamic memory allocation scenarios.
• Overall, subroutines, method call overhead, and dynamic memory allocation for
message and object storage are important concepts in programming that help
organize code, enable code reuse, and provide flexibility in memory management.
• Understanding these concepts is crucial for writing efficient and maintainable
code in procedural and object-oriented programming paradigms

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 11. Dynamically dispatched message calls and direct
procedure call overheads
• Dynamically Dispatched Message Calls:
• In object-oriented programming, dynamically dispatched message calls refer to the
mechanism of invoking methods or functions on objects at runtime based on the
actual type of the object.
• When a message is sent to an object, the runtime system determines the appropriate
method to be called based on the object's dynamic type or class hierarchy.
• Dynamically dispatched message calls involve a level of indirection and typically
incur some overhead compared to direct procedure calls. The overhead is due to the
need for runtime lookup and method resolution to determine the correct method
implementation to be invoked.
• This lookup process involves traversing the object's class hierarchy and finding the
appropriate method implementation based on the dynamic type of the object.
• The overhead associated with dynamically dispatched message calls can vary
depending on factors such as the programming language, the complexity of the class
hierarchy, and the efficiency of the runtime system.
• However, modern object-oriented programming languages and runtime systems
employ various optimizations, such as caching method tables or using virtual
function tables (vtables), to reduce the overhead of dynamic dispatch.

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• Direct Procedure Call Overheads:
• Direct procedure calls refer to the direct invocation of procedures or functions
without involving any dynamic dispatch mechanism.
• In direct procedure calls, the address of the function is known at compile time,
allowing the program to directly jump to the memory location of the function and
execute its instructions.
• Direct procedure calls typically have lower overhead compared to dynamically
dispatched message calls.
• The direct nature of the call avoids the need for runtime method resolution and
lookup, reducing the indirection and associated overhead.
• Direct procedure calls have a more straightforward and efficient execution path
since the target procedure's address is known in advance.

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• However, it's important to note that the overhead of direct procedure calls
can still exist due to factors such as argument passing, stack
manipulation, and context switching.
• The specific overhead may vary depending on the programming language,
the calling convention used, and the underlying hardware architecture.
• In general, dynamically dispatched message calls introduce a level of
indirection and overhead due to the runtime lookup and method
resolution required.
• On the other hand, direct procedure calls have lower overhead as they
directly invoke functions without the need for runtime lookup.
• The choice between dynamically dispatched message calls and direct
procedure calls depends on the specific requirements of the application,
the level of polymorphism needed, and the performance considerations
•

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 12. Object Serialization
• Object serialization refers to the process of converting an object's
state into a format that can be stored, transmitted, or reconstructed
later.
• It involves transforming the object and its associated data into a
sequence of bytes, which can be written to a file, sent over a network, or
stored in a database.
• The reverse process, where the serialized data is used to reconstruct the
object, is called deserialization.
Object serialization is primarily used for two purposes:
• Persistence: Object serialization allows objects to be stored persistently,
meaning they can be saved to a file or database and retrieved later. This
enables applications to preserve the state of objects across multiple
program executions or to transfer objects between different systems.

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• Communication: Serialized objects can be sent over a network or transferred between different
processes or systems. This is particularly useful in distributed systems or client-server
architectures where objects need to be exchanged between different components or across
different platforms.
• During object serialization, the object's state, which includes its instance variables, is
transformed into a serialized form. This process may involve encoding the object's data,
along with information about its class structure and metadata. The serialized data is
typically represented as a sequence of bytes or a structured format like XML or JSON.

• Some programming languages and frameworks provide built-in support for object
serialization, offering libraries and APIs that handle the serialization and deserialization
process automatically. These libraries often provide mechanisms to control serialization,
such as excluding certain fields, customizing serialization behavior, or implementing
custom serialization logic.
•
• However, not all objects are serializable by default. Certain object attributes, such as
open file handles, network connections, or transient data, may not be suitable for
serialization. In such cases, specific measures need to be taken to handle or exclude
these attributes during serialization.
•
• Object serialization is a powerful mechanism that facilitates data storage, communication,
and distributed computing. It allows objects to be easily persisted or transmitted across
different systems, preserving their state and enabling seamless integration between
heterogeneous environments

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 13. parallel Computing
• Parallel computing refers to the use of multiple processors or computing resources to
solve a computational problem or perform a task simultaneously.
• It involves breaking down a problem into smaller parts that can be solved concurrently or
in parallel, thus achieving faster execution and increased computational power.
• Parallel computing can be applied to various types of problems, ranging from
computationally intensive scientific simulations and data analysis to web servers
handling multiple requests simultaneously.
• It is particularly beneficial for tasks that can be divided into independent subtasks that
can be executed concurrently.
• There are different models and approaches to parallel computing:
Task Parallelism:
• In task parallelism, the problem is divided into multiple independent tasks or subtasks
that can be executed concurrently.
• Each task is assigned to a separate processing unit or thread, allowing multiple tasks to
be processed simultaneously.
• Task parallelism is well-suited for irregular or dynamic problems where the execution
time of each task may vary.

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Data Parallelism:
• Data parallelism involves dividing the data into smaller chunks and processing them
simultaneously on different processing units.
• Each unit operates on its portion of the data, typically applying the same computation or
algorithm to each chunk.
• Data parallelism is commonly used in scientific simulations, image processing, and
numerical computations.
Message Passing:
• Message passing involves dividing the problem into smaller tasks that communicate and
exchange data by sending messages to each other.
• Each task operates independently and exchanges information with other tasks as
needed.
• This approach is commonly used in distributed systems and parallel computing
frameworks such as MPI (Message Passing Interface).
Shared Memory:
Shared memory parallelism involves multiple processors or threads accessing and
modifying a shared memory space.
This model allows parallel tasks to communicate and synchronize by reading and writing to
shared memory locations.
Programming models such as OpenMP and Pthreads utilize shared memory parallelism.
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• Parallel computing offers several benefits, including:
Increased speed:
• By dividing the problem into smaller parts and executing them simultaneously, parallel
computing can significantly reduce the overall execution time and achieve faster results.
Enhanced scalability:
• Parallel computing allows for the efficient utilization of multiple processing units or
resources, enabling systems to scale and handle larger workloads.
Improved performance:
• Parallel computing enables the execution of complex computations and simulations that
would otherwise be infeasible or take an impractical amount of time with sequential
processing.

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• However, parallel computing also introduces challenges such as load balancing, data
synchronization, and communication overhead.
• Proper design and optimization techniques are essential to ensure efficient and effective
parallel execution.
• Overall, parallel computing is a powerful approach for achieving high-performance
computing and tackling complex problems by harnessing the capabilities of multiple
processing units or resources.
• It plays a crucial role in various domains, including scientific research, data analysis,
artificial intelligence, and large-scale computing systems

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