Everything lies inside an algorithm in the world of coding and algorithm formation which is the basis of data structure and manipulation of the algorithm in computer science and information technology which is ultimately used to find a particular problems solution
Presentation on Python functions using Jupyter Notebook in Microsoft Azure Platform in the seven days Faculty Development and Training Program on GE8151 - Problem Solving and Python Programming held at Anna University Regional Campus, Coimbatore.
Specifying function specializations over an arbitrary set of type constraints is a daunting task in C++ as soon as those constraints become more and more complex and/or grow in number. Various idioms are traditionally used to solve this problem: SFINAE or Tag Dispatching for example.
This talk introduces Boost.Dispatch, an infrastructure library that make Tag Dispatching easier to use and maintain by providing a protocol to define Tags and relationship between them, to map an arbitrary set of tags to a given function implementation and to extend said list of specialization in an open, modular way. The main new asset of Boost.Dispatch is the ability to use categorization of function properties and/or architectural information to guide the dispatch in addition to the more traditional use of type properties.
The talk will quickly brushes a picture of what SFINAE, overloading and Tag Dispatching mean in C++ and what are their limitations. We’ll introduce Boost.Dispatch over some examples ranging from simple library design to actual high performance computing code using the library to select best implementation of a function based on non-trivial architecture dependent information. Then we’ll dive into the implementation of the library and try to sketches the upcoming challenges yet to be solved.
HDR Defence - Software Abstractions for Parallel ArchitecturesJoel Falcou
Performing large, intensive or non-trivial computing on array like data
structures is one of the most common task in scientific computing, video game
development and other fields. This matter of fact is backed up by the large number
of tools, languages and libraries to perform such tasks. If we restrict ourselves to
C++ based solutions, more than a dozen such libraries exists from BLAS/LAPACK
C++ binding to template meta-programming based Blitz++ or Eigen.
If all of these libraries provide good performance or good abstraction, none of
them seems to fit the need of so many different user types. Moreover, as parallel
system complexity grows, the need to maintain all those components quickly
become unwieldy. This thesis explores various software design techniques - like
Generative Programming, MetaProgramming and Generic Programming - and their
application to the implementation of various parallel computing libraries in such a
way that abstraction and expressiveness are maximized while efficiency overhead is
minimized.
(Costless) Software Abstractions for Parallel ArchitecturesJoel Falcou
Performing large, intensive or non-trivial computing on array like data structures is one of the most common task in scientific computing, video game development and other fields. This matter of fact is backed up by the large number of tools, languages and libraries to perform such tasks. If we restrict ourselves to C++ based solutions, more than a dozen such libraries exists from BLAS/LAPACK C++ binding to template meta-programming based Blitz++ or Eigen. If all of these libraries provide good performance or good abstraction, none of them seems to fit the need of so many different user types.
Moreover, as parallel system complexity grows, the need to maintain all those components quickly become unwieldy. This talk explores various software design techniques - like Generative Programming, MetaProgramming and Generic Programming - and their application to the implementation of a parallel computing librariy in such a way that:
- abstraction and expressiveness are maximized - cost over efficiency is minimized
We'll skim over various applications and see how they can benefit from such tools. We will conclude by discussing what lessons were learnt from this kind of implementation and how those lessons can translate into new directions for the language itself.
Presentation on Python functions using Jupyter Notebook in Microsoft Azure Platform in the seven days Faculty Development and Training Program on GE8151 - Problem Solving and Python Programming held at Anna University Regional Campus, Coimbatore.
Specifying function specializations over an arbitrary set of type constraints is a daunting task in C++ as soon as those constraints become more and more complex and/or grow in number. Various idioms are traditionally used to solve this problem: SFINAE or Tag Dispatching for example.
This talk introduces Boost.Dispatch, an infrastructure library that make Tag Dispatching easier to use and maintain by providing a protocol to define Tags and relationship between them, to map an arbitrary set of tags to a given function implementation and to extend said list of specialization in an open, modular way. The main new asset of Boost.Dispatch is the ability to use categorization of function properties and/or architectural information to guide the dispatch in addition to the more traditional use of type properties.
The talk will quickly brushes a picture of what SFINAE, overloading and Tag Dispatching mean in C++ and what are their limitations. We’ll introduce Boost.Dispatch over some examples ranging from simple library design to actual high performance computing code using the library to select best implementation of a function based on non-trivial architecture dependent information. Then we’ll dive into the implementation of the library and try to sketches the upcoming challenges yet to be solved.
HDR Defence - Software Abstractions for Parallel ArchitecturesJoel Falcou
Performing large, intensive or non-trivial computing on array like data
structures is one of the most common task in scientific computing, video game
development and other fields. This matter of fact is backed up by the large number
of tools, languages and libraries to perform such tasks. If we restrict ourselves to
C++ based solutions, more than a dozen such libraries exists from BLAS/LAPACK
C++ binding to template meta-programming based Blitz++ or Eigen.
If all of these libraries provide good performance or good abstraction, none of
them seems to fit the need of so many different user types. Moreover, as parallel
system complexity grows, the need to maintain all those components quickly
become unwieldy. This thesis explores various software design techniques - like
Generative Programming, MetaProgramming and Generic Programming - and their
application to the implementation of various parallel computing libraries in such a
way that abstraction and expressiveness are maximized while efficiency overhead is
minimized.
(Costless) Software Abstractions for Parallel ArchitecturesJoel Falcou
Performing large, intensive or non-trivial computing on array like data structures is one of the most common task in scientific computing, video game development and other fields. This matter of fact is backed up by the large number of tools, languages and libraries to perform such tasks. If we restrict ourselves to C++ based solutions, more than a dozen such libraries exists from BLAS/LAPACK C++ binding to template meta-programming based Blitz++ or Eigen. If all of these libraries provide good performance or good abstraction, none of them seems to fit the need of so many different user types.
Moreover, as parallel system complexity grows, the need to maintain all those components quickly become unwieldy. This talk explores various software design techniques - like Generative Programming, MetaProgramming and Generic Programming - and their application to the implementation of a parallel computing librariy in such a way that:
- abstraction and expressiveness are maximized - cost over efficiency is minimized
We'll skim over various applications and see how they can benefit from such tools. We will conclude by discussing what lessons were learnt from this kind of implementation and how those lessons can translate into new directions for the language itself.
Design and Analysis of Algorithm help to design the algorithms for solving different types of problems in Computer Science. It also helps to design and analyze the logic of how the program will work before developing the actual code for a program.
Discrete mathematics counting and logic relationSelf-Employed
In the mathematics world logic is the main fundamental key wi=ord to deal with the problems .If any problems are tried to solve by using certain logics and information provided by the statement then it became quite easier to get the required result effectively and efficiently without any lag of time.
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2. Things To Be Discussed In This Unit
• Program Design
• Concept and Definition
• Design of algorithm
• Characteristic of algorithm
• Big O notation
2
3. Learning Objectives
• Understand the type of problem that will be covered in this class
• Recognize some problems for which sophisticated algorithms might not be
necessary.
• Question if your solution technique is an efficient one? Any room for
optimization?
3
4. Program Design
• Involves taking the specification and designing solutions, the designer needs to
adopt a design strategy.
• Solution Strategy should work correctly in all conditions
• A large program should be divided into small modules and sub modules.
• Other important criteria by which a program can be judged are execution time and
storage requirement
4
5. Algorithms
• The term ’algorithm’ refers to the sequence of instructions that must be followed
to solve a problem.
• Logical representation of the instructions which should be executed to perform a
meaningful task.
• Algorithms are generally created independent of underlying languages, i.e. an
algorithm can be implemented in more than one programming language.
5
6. An algorithm has certain characteristics
• Each instruction should be unique and concise.
• Each instruction should be relative in nature and should not be repeated infinitely
• Repetition of same task(s) should be avoided.
• The result should be available to the user after algorithm terminates.
6
7. NOTE:
• After an algorithm has been designed, its efficiency must be analyzed. i.e CPU time
and memory
• Memory space and running time should be taken care of.
• The importance of efficiency of an algorithm is in the correctness, i.e does it
always produce the correct result, and program complexity which considers
both the difficulty of implementing an algorithm along with its efficiency
7
8. Properties of an Algorithm
• Input: A number of quantities are provided to an algorithm initially before the algorithm
begins. These quantities are inputs which are processed by the algorithm.
• Definiteness: Each step must be clear and unambiguous.
• Effectiveness: Each step must be carried out in finite time.
• Finiteness: Algorithms must terminate after finite time or step
• Output: An algorithm must have output.
• Correctness: Correct set of output values must be produced from the each set of inputs.
8
9. Write an algorithm to find the greatest number among three numbers
Step 1: Read three numbers and store them in X, Y and Z
Step 2: Compare X and Y. if X is greater than Y then go to step 5 else go to step 3
Step 3: Compare Y and Z. if Y is greater than Z then print “Y is greatest” and go to step 7
otherwise go to step 4
Step 4: Print “Z is greatest” and go to step 7
Step 5: Compare X and Z. if X is greater than Z then print “X is greatest” and go to step 7
otherwise go to step 6
Step 6: Print “Z is greatest” and go to step 7
Step 7: Stop
9
10. Different Approaches To Designing An Algorithm
• A complex system may be divided into smaller units called modules.
• The advantage of modularity is that it focuses on specific module.
• Modularity enhances design clarity, which in turn eases implementation,
debugging, testing, documenting and maintenance of the project.
• To design a hierarchy of a system there are two possible approaches
• Top-down approach
• Bottom-up approach
10
11. Top-Down Approach
• How it is done?
• Identify the major components of the system,
• decompose them into their lower-level components and
• Iterate until the desired level of module complexity is achieved.
• It basically starts with the big picture. It breaks down from there into smaller
segments.
• Top-down design method takes the form of stepwise refinement.
11
12. Bottom-Up Approach
• A bottom-up approach is the piecing together of systems to give rise to more
complex systems
• Bottom-up method works with layers of abstraction.
• Elements are then linked together to form larger subsystems, which then in turn
are linked, sometimes in many levels, until a complete top-level system is formed.
• This strategy often resembles a "seed" model, by which the beginnings are small
but eventually grow in complexity and completeness.
12
13. Top-Down versus Bottom-Up Approach
• The top-down approach, however, is often useful way to better document a
design.
• The design activity should not be constrained to proceed according to a fixed
pattern but should be a blend of top-down and bottom-up approaches
13
14. Complexity
• When we talk of complexity in context of computers, we call it computational
complexity.
• Computational complexity is a characterization of the time or space requirements
for solving a problem by a particular algorithm.
• Lesser the complexity better an algorithm.
14
15. Complexity
• Given a particular problem, let ’n’ denote its size. The time required of a specific
algorithm for solving this problem is expressed by a function:
f : R->R
• Such that, f(n) is the largest amount of time needed by the algorithm to solve the
problem of size n.
• Function ‘f’ is usually called the time complexity function.
• Thus we conclude that the analysis of the program requires two main
considerations:
• Time Complexity (time required for completion)
• Space Complexity (memory required for completion)
15
16. Time Complexity
• While measuring the time complexity of an algorithm, we concentrate on
developing only the frequency count for all key statements
(statements that are important and are the basic instructions of an algorithm)
• This is because, it is often difficult to get reliable timing figure because of clock
limitations and the multiprogramming or the sharing environment.
16
17. Algorithm A
• In an algorithm A we may find that the statement a=a+1 is
independent and is not contained within any loop.
• Therefore, the number of times this shall be executed is 1.
• We say that the frequency count of an algorithm A is 1.
17
18. Algorithm B
• In this algorithm, i.e. B, the key statement is the assignment operation a=a+1.
• Because this statement is contained within a loop, the number of times it is
executed is n, as the loop runs for n times.
• The frequency count for this algorithm is n.
18
19. Algorithm C
• The frequency count for the statement a=a+1 is n2 as the inner loop runs n times,
each time the outer loop runs, the inner loop also runs for n times.
• If an algorithm perform f(n) basic operations when the size of its input is n, then its
total running time will be cf(n), where c is a constant that depends upon the
algorithm, on the way it is programmed, and on the way the
computer is used, but c does not depend on the size of the input
19
20. Space Complexity
• Space complexity is essentially the number of memory cells which an algorithm needs.
• A good algorithm keeps this number as small as possible, too.
• There is often a time-space-tradeoff involved in a problem, that is, it cannot be solved with few
computing time and low memory consumption.
• Space complexity is measured with respect to the input size for a given instance of a problem.
20
22. Ex 1: Greatest Common Divisor
• DEFINITION:
• For integers, a and b, their greatest common divisor or gcd(a,b) is the largest integer d so that
d divides both a and b
• Compute GCD
• Input: Integers a,b ≥0
• Output: gcd(a,b)
• Ex: Run on large numbers like
gcd(3198848,1653264)
22
23. Function NaiveGCD(a,b)
• PROBLEMS:
• Takes too much time to complete
• Not an optimal solution
23
Best=0
For d from 1 to a+b:
if d/a and d/b:
best=d
return best
24. Function efficientGCD(a,b)
• Lemma
• Let a’ be the remainder when a is divided by b, then
gcd(a,b)=gcd(a’,b)=gcd(b,a’)
• So what is gcd(357,234)?
24
If b=0:
return a
a’ = the remainder when a is divided by b
return efficientGCD(b,a’)
Example
25. Summary of naïve vs efficient algorithms
• Naïve algorithm is too slow
• The correct algorithm is much better
• Finding the correct algorithm requires knowing something interesting about the
problem
25