Search algorithm

Memory hierarchy

Matrix Multiplication

Shared Memory Parallelism

Roofline and Performance Modeling

Sources of Parallelism and Locality

Communication-avoiding matrix multiplication

Data Parallel Algorithms

CUDA (Compute Unified Device Architecture)

Graphics Processor (GPU)

Distributed Memory Machines and Programming

Advanced MPI

Collective Communication Algorithms

UPC++: Partitioned Global Address Space Languages

Domain Specific Languages (Halide)

Distributed Data Structures (BCL)

Parallel Matrix Multiply

Dense Linear Algebra

Sparse-Matrix-Vector-Multiplication

Iterative Solvers

Structured Grids

Machine learning

Supervised learning

Deep learning

Unsupervised learning

Graph Partitioning

Fast Fourier transform

Graph Algorithms

Cloud Computing

Big Data Processing

High Performance Computing (HPC)

Dynamic Load Balancing

Hierarchical Methods for the N-Body Problem

Sorting algorithm

Computational Biology

Quantum Computing

CS 267: Applications of Parallel Computers UC Berkeley Spring 2020 The course addresses programming parallel computers to solve complex scientific and engineering problems. It covers an array of parallelization strategies for numerical simulation, data analysis, and machine learning, and provides experience with popular parallel programming tools. CS267 was originally designed to teach students how to program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets. CS267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented. CS267 satisfies part of the course requirements for the Designated Emphasis ("graduate minor") in [Computational Science and Engineering](http://www.google.com/url?q=http%3A%2F%2Fcitris-uc.org%2Finitiatives%2Fdecse%2F&sa=D&sntz=1&usg=AOvVaw1fRFGvIaIYhkwE3kp4J1nf).

While this general outline remains, a large change in the computing world started in the mid 2000's: not only are the [fastest computers](http://www.google.com/url?q=http%3A%2F%2Fwww.top500.org%2F&sa=D&sntz=1&usg=AOvVaw1SiByfUjOpTR6ctURAV2Ef) parallel, but nearly all computers are becoming parallel, because the physics of semiconductor manufacturing will no longer let conventional sequential processors get faster year after year, as they have for so long (roughly doubling in speed every 18 months for many years). So all programs that need to run faster will have to become parallel programs. (It is considered very unlikely that compilers will be able to automatically find enough parallelism in most sequential programs to solve this problem.) For background on this trend toward parallelism, click [here](http://www.google.com/url?q=http%3A%2F%2Fwww.eecs.berkeley.edu%2FPubs%2FTechRpts%2F2006%2FEECS-2006-183.html&sa=D&sntz=1&usg=AOvVaw2XoPTheTzKVlyjp1G_YcM5).

Students in CS267 will get an overview of the parallel architecture space, gain experience using some of the most popular parallel programming tools, and be exposed to a number of open research questions. The lectures will also cover a broad set of parallelization strategies for applications covering numerical simulation and data analysis to machine learning.   

CS 267: Applications of Parallel Computers

Parallelism and Distributed Systems

Control flow

Expressions

List (abstract data type)

Images

Dictionaries

Computer memory

Decomposition

Python Control Flow

Graphics

List of Lists

Computer file

Data Science

Internet

Python Variables

Function (computer programming)

Graphical Animation

String (computer science)

Nested Structures

Class (computer programming)

Function Variables

CS 106A: Programming Methodologies Stanford University Spring 2023 This is an introductory course offering step-by-step instruction on key programming concepts with Python, including control flow, decomposition, data science, and more. Students are guided through exercises to learn by doing. ### How Does This Class Work?

- Start the very beginning
- Careful to go step by step, little exercises every day
- Like swimming, you can't just read about it
- Learn by doing
- "reps" - Nick's favorite word
- Technology to practice ideas right away
- In just a couple weeks .. amazed at what you can do
- Partly because computers are powerful
- Partly because coding is easier than you think  No prior programming experience required. There is one required text for this class, entitled Karel the Robot Learns Python. It's a short interactive tutorial that introduces the major concepts in programming in the context of an extremely simple robot world. This text is available for free on the class website. In addition to this text, we will also have reference material on Python available via the class webpage.

CS 106A: Programming Methodologies

Computer Programming

Object (computer science)

Polymorphism (computer science)

Interface (object-oriented programming)

Inheritance (object-oriented programming)

Graphical User Interface (GUI)

Loops

Array (data structure)

Recursion (computer science)

Software design pattern

Big-O notation

Data structure

Linked list

Stack (abstract data type)

Queue (abstract data type)

Tree (data structure)

Hashing

Set (abstract data type)

CSCI 0150: Introduction to Object-Oriented Programming and Computer Science Brown University Fall 2022 CS0150 introduces Computer Science via object-oriented design and programming using Java and JavaFX for creating interactive programs with GUIs. Concepts like data structures, algorithms, and computational efficiency are explored. Practical exercises include engaging programming assignments like Doodle Jump and Tetris. The course is designed for all, requiring no prior knowledge. This course introduces students to Computer Science through object-oriented design and programming, using Java and the JavaFX graphics library. You will use these tools for building interactive programs with graphical user interfaces. CS0150 reinforces concepts with practical exercises in weekly lab sessions and with challenging and engaging programming assignments, such as Doodle Jump and Tetris!

There are no prerequisites for CS0150 and the course expects no prior programming experience. This course introduces principles of computer science, emphasizing object-oriented design and programming in Java, an effective modern technique for producing modular, reusable, and internet-aware programs. It also introduces interactive 2D computer graphics, user interface design, some fundamental data structures and algorithms, and computational efficiency. A sequence of successively more complex graphics programs, including Tetris and culminating in a significant final project, helps provide a serious introduction to the field. Each assignment includes an interactive user interface. Therefore, most of the games you program can be played! However, this course is not focused on games nor game design. It uses games as a natural and fun vehicle for learning object-oriented programming and graphical user interfaces.  The course takes an interactive approach to programming assignments and an equally interactive approach to lectures. Andy’s lectures are supplemented by skits performed by the UTAs (Undergraduate Teaching Assistants) to teach course concepts and to make the class fun and enjoyable!  This course is intended for both potential concentrators and those who may take only a single course. No prerequisites and no prior knowledge of programming and mathematics, beyond basic algebra, are required.  

CSCI 0150: Introduction to Object-Oriented Programming and Computer Science

Search algorithm

3 courses cover this concept

CS 267: Applications of Parallel Computers

CS 106A: Programming Methodologies

CSCI 0150: Introduction to Object-Oriented Programming and Computer Science