Computer Science

Parallelism and Distributed Systems

Parallelism and Distributed Systems

Parallel and distributed computing is a type of computation in which many calculations are performed simultaneously, as opposed to sequentially (one after the other). Understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems is necessary to effectively utilize such systems.

Prerequisites

To study Parallelism and Distributed Systems, students need to have proficiency in Computer Programming and knowledge in Computer Systems

CS 256 Formal Methods for Reactive Systems

Stanford University

Winter 2023

This advanced course delves into the complexities of programming concurrent and reactive systems. It provides a firm theoretical foundation for understanding temporal logics like LTL and CTL, and the main verification techniques including deductive and algorithmic. The course necessitates a background in mathematical logic and familiarity with Algol-like languages.

No concepts data

+ 5 more concepts

CS 294-91 Distributed Computing

UC Berkeley

Winter 2013

This course provides basic theoretical and practical foundations of distributed systems. Students learn about system models, safety and liveness of protocols, different failure models, reliable group communication abstractions, and more. It utilizes a textbook and additional research paper-based lectures.

No concepts data

+ 17 more concepts

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.

No concepts data

+ 36 more concepts

CS 315B Parallel Programming

Stanford University

Fall 2022

This course explores advanced topics in supercomputer programming, with a focus on tasking runtimes. It offers a survey of programming models, supercomputer architectures, and in-depth lessons on tasking. Students will gain hands-on experience in cuNumeric and Regent programming languages. While it doesn't assume extensive background, good programming skills are required.

No concepts data

+ 16 more concepts

CS 149 PARALLEL COMPUTING

Stanford University

Fall 2022

Focused on principles and trade-offs in designing modern parallel computing systems, this course also teaches parallel programming techniques. It is intended for students looking to understand both parallel hardware and software design. Prerequisite knowledge in computer systems is required.

No concepts data

+ 45 more concepts