Algorithms Lecture 1: Introduction to AlgorithmsMohamed Loey
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
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Algorithms Lecture 1: Introduction to AlgorithmsMohamed Loey
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data. https://apkleet.com
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Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
a. Concept and Definition
b. Binary Tree
c. Introduction and application
d. Operation
e. Types of Binary Tree
• Complete
• Strictly
• Almost Complete
f. Huffman algorithm
g. Binary Search Tree
• Insertion
• Deletion
• Searching
h. Tree Traversal
• Pre-order traversal
• In-order traversal
• Post-order traversal
Slides at myblog
http://www.ashimlamichhane.com.np/2016/07/tree-slide-for-data-structure-and-algorithm/
Assignments at github
https://github.com/ashim888/dataStructureAndAlgorithm/tree/dev/Assignments/assignment_7
2D array in C++ language ,define the concept of c++ Two-Dimensional array .with example .and also Accessing Array Components concept.and Processing Two-Dimensional Arrays.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
a. Concept and Definition
b. Binary Tree
c. Introduction and application
d. Operation
e. Types of Binary Tree
• Complete
• Strictly
• Almost Complete
f. Huffman algorithm
g. Binary Search Tree
• Insertion
• Deletion
• Searching
h. Tree Traversal
• Pre-order traversal
• In-order traversal
• Post-order traversal
Slides at myblog
http://www.ashimlamichhane.com.np/2016/07/tree-slide-for-data-structure-and-algorithm/
Assignments at github
https://github.com/ashim888/dataStructureAndAlgorithm/tree/dev/Assignments/assignment_7
2D array in C++ language ,define the concept of c++ Two-Dimensional array .with example .and also Accessing Array Components concept.and Processing Two-Dimensional Arrays.
Basic Computer Engineering Unit II as per RGPV SyllabusNANDINI SHARMA
Algorithm, Flowchart, Categories of Programming Languages, OOPs vs POP, concepts of OOPs, Inheritance, C++ Programming, How to write C++ program as a beginner, Array, Structure, etc
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
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Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
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(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
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1. Introduction:
Basic Concepts and Notations
Complexity analysis: time space tradeoff
Algorithmic notations, Big O notation
Introduction to omega, theta and little o
notation
2. Basic Concepts and Notations
Algorithm: Outline, the essence of a computational
procedure, step-by-step instructions
Program: an implementation of an algorithm in some
programming language
Data Structure: Organization of data needed to
solve the problem
3. Classification of Data
StructuresData Structures
Primitive
Data Structures
Non-Primitive
Data Structures
Linear
Data Structures
Non-Linear
Data Structures
• Integer
• Real
• Character
• Boolean
• Array
• Stack
• Queue
• Linked List
• Tree
• Graph
4. Data Structure Operations
Data Structures are processed by using certain operations.
1.Traversing: Accessing each record exactly once so that certain
items in the record may be processed.
2.Searching: Finding the location of the record with a given key
value, or finding the location of all the records that satisfy one or
more conditions.
3.Inserting: Adding a new record to the structure.
4.Deleting: Removing a record from the structure.
5. Special Data Structure-
Operations
• Sorting: Arranging the records in some logical order
(Alphabetical or numerical order).
• Merging: Combining the records in two different sorted
files into a single sorted file.
6. Algorithmic Problem
Infinite number of input instances satisfying the
specification. For example: A sorted, non-decreasing
sequence of natural numbers of non-zero, finite
length:
1, 20, 908, 909, 100000, 1000000000.
3.
Specification
of input ?
Specification
of output as a
function of
input
7. Algorithmic Solution
Algorithm describes actions on the input instance
Infinitely many correct algorithms for the same
algorithmic problem
Specification
of input
Algorithm
Specification
of output as a
function of
input
8. What is a Good Algorithm?
Efficient:
Running time
Space used
Efficiency as a function of input size:
The number of bits in an input number
Number of data elements(numbers, points)
9. Complexity analysis
Why we should analyze algorithms?
Predict the resources that the algorithm requires
Computational time (CPU consumption)
Memory space (RAM consumption)
Communication bandwidth consumption
The running time of an algorithm is:
The total number of primitive operations executed (machine
independent steps)
Also known as algorithm complexity
10. Time Complexity
Worst-case
An upper bound on the running time for any input of
given size
Average-case
Assume all inputs of a given size are equally likely
Best-case
The lower bound on the running time
11. Time Complexity – Example
Sequential search in a list of size n
Worst-case:
n comparisons
Best-case:
1 comparison
Average-case:
n/2 comparisons
12. time space tradeoff
A time space tradeoff is a situation where the memory use
can be reduced at the cost of slower program execution
(and, conversely, the computation time can be reduced at
the cost of increased memory use).
As the relative costs of CPU cycles, RAM space, and hard
drive space change—hard drive space has for some time
been getting cheaper at a much faster rate than other
components of computers[citation needed]—the
appropriate choices for time space tradeoff have changed
radically.
Often, by exploiting a time space tradeoff, a program can
be made to run much faster.
13. Asymptotic notations
Algorithm complexity is rough estimation of
the number of steps performed by given
computation depending on the size of the
input data
Measured through asymptotic notation
O(g) where g is a function of the input data size
Examples:
Linear complexity O(n) – all elements are processed once (or
constant number of times)
Quadratic complexity O(n2
) – each of the elements is
processed n times
15. Example
The running time is O(n2
) means there is a function
f(n) that is O(n2
) such that for any value of n, no
matter what particular input of size n is chosen, the
running time of that input is bounded from above by
the value f(n).
3 * n2
+ n/2 + 12 ∈ O(n2
)
4*n*log2(3*n+1) + 2*n-1 ∈ O(n * log n)
17. Example
When we say that the running time (no modifier) of
an algorithm is Ω (g(n)).
we mean that no matter what particular input of size
n is chosen for each value of n, the running time on
that input is at least a constant times g(n), for
sufficiently large n.
n3
+ 20n ∈ Ω(n2
)
22. 22
Notes on o-notation
O-notation may or may not be asymptotically
tight for upper bound.
2n2
= O(n2
) is tight, but 2n = O(n2
) is not tight.
o-notition is used to denote an upper bound
that is not tight.
2n = o(n2
), but 2n2
≠ o(n2
).
Difference: for some positive constant c in O-
notation, but all positive constants c in o-
notation.
In o-notation, f(n) becomes insignificant
relative to g(n) as n approaches infinitely.
23. Big O notation
f(n)=O(g(n)) iff there exist a positive constant c and
non-negative integer n0 such that
f(n) ≤ cg(n) for all n≥n0.
g(n) is said to be an upper bound of f(n).
24. Basic rules
1. Nested loops are multiplied together.
2. Sequential loops are added.
3. Only the largest term is kept, all others are
dropped.
4. Constants are dropped.
5. Conditional checks are constant (i.e. 1).
30. At first you might say that the upper bound is O(2n);
however, we drop constants so it becomes O(n)
31. Example 4
//linear
for(int i = 0; i < n; i++) {
cout << i << endl;
}
//quadratic
for(int i = 0; i < n; i++) {
for(int j = 0; j < n; j++){
//do constant time stuff
}
}
32. Ans : In this case we add each loop's Big O, in this
case n+n^2. O(n^2+n) is not an acceptable answer
since we must drop the lowest term. The upper bound
is O(n^2). Why? Because it has the largest growth
rate
33. Example 5
for(int i = 0; i < n; i++) {
for(int j = 0; j < 2; j++){
//do stuff
}
}
34. Ans: Outer loop is 'n', inner loop is 2, this we have 2n,
dropped constant gives up O(n)