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By Suryam Saini + CU Connect

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UNIT-I Fundamentals of Parallel and Distributed Systems [15 HRS]

1. Introduction :
   1. Introduction to Parallel and Distributed Computing,
   2. Characteristics, Scope , Goals, applications,
   3. Differences between parallel and distributed systems,
   4. issues and challenges of Parallel and Distributed Computing,
2. Distributed Systems:
   1. Components of a distributed system ,
   2. Characteristics and architecture of distributed systems,
   3. Types of distributed systems:
      1. Client-server
      2. peer-to-peer,
   4. Communication in distributed systems.
   5. Security in distributed system.
   6. Pipelining concepts.
3. Parallel Computing Platforms:
   1. Need for parallelism,
   2. Types of parallelism:
      1. Data parallelism,
      2. task parallelism ,
   3. Parallel computing models:
      1. SIMD, MIMD, SISD, MISD,
      2. Flynn’s taxonomy ,
      3. Shared memory vs. distributed memory .
   4. Parallel & Distributed Computing Concepts:
      1. Multithreading concepts:
         1. Threads vs. processes ,
         2. Synchronization,
      2. introduction to CAP theorem.

NOTES

Introduction to Parallel and Distributed Computing

Parallel and Distributed Computing is a field of computer science that focuses on utilizing multiple computing resources to solve complex computational problems efficiently.

1.1 Parallel Computing

1. Parallel computing is a technique where multiple processors execute multiple tasks simultaneously within a single system, often sharing memory.
2. Parallel computing, also known as parallel processing, speeds up a computational task by dividing it into smaller jobs across multiple processors inside one computer.
3. Parallel computing provides numerous advantages. Parallel computing helps to increase the CPU utilization and improve the performance because several processors work simultaneously.

1.2 Advantages of Parallel Computing

1. It saves time and money because many resources working together cut down on time and costs.
2. It may be difficult to resolve larger problems on Serial Computing.
3. You can do many things at once using many computing resources.
4. Parallel computing is much better than serial computing for modeling, simulating, and comprehending complicated real-world events.
5. Increased Speed: In this technique, several calculations are executed concurrently hence reducing the time of computation required to complete large scale problems.
6. Efficient Use of Resources: Takes full advantage of all the processing units it is equipped with hence making the best use of the machine’s computational power.