Parallel and Distributed Computing

Unit 1 – Intro to Parallel & Distributed Computing

Unit 2 – Parallel Computer Architectures

Unit 3 – Parallel Programming: Models & Languages

Unit 4 – OpenMP: Shared Memory Programming

Unit 5 – Distributed Memory Programming with MPI

Unit 6 – Parallel Algorithm Design & Analysis

Unit 7 – Load Balancing and Scheduling in PDC

Unit 8 – Optimizing Scalability and Performance

Unit 9 – Optimizing Sync and Communication

Unit 10 – Fault Tolerance in Distributed Systems

Unit 11 – Parallel File Systems and I/O

Unit 12 – GPU Computing and CUDA

Unit 13 – Big Data Processing Frameworks

Unit 14 – Cloud Computing & Virtualization

Unit 15 – Case Studies in Parallel Computing

## What do you learn in Parallel and Distributed Computing

You'll get into the nitty-gritty of designing and analyzing parallel algorithms and distributed systems. The course covers parallel architectures, programming models, performance analysis, load balancing, and fault tolerance. You'll also explore distributed algorithms, communication protocols, and scalability issues in large-scale systems.

## Is Parallel and Distributed Computing hard?

It can be pretty challenging, not gonna lie. The concepts can get pretty abstract, and you'll need to wrap your head around thinking in parallel. But once things click, it's super rewarding. The math can be tough, and debugging parallel programs is a whole new ballgame, but with practice, you'll get the hang of it.

## Tips for taking Parallel and Distributed Computing in college

1. Use [Fiveable Study Guides](https://fiveable.me/cram-mode) to help you cram 🌶️
2. Practice, practice, practice coding parallel algorithms
3. Visualize concepts with diagrams (seriously helps with stuff like message passing)
4. Get comfortable with performance metrics like speedup and efficiency
5. Join study groups to tackle complex distributed systems problems
6. Watch "The Imitation Game" for some historical context on parallel computing
7. Read "Designing Data-Intensive Applications" by Martin Kleppmann for real-world insights
8. Use tools like OpenMP and MPI to get hands-on experience
9. Don't be afraid to ask for help - this stuff can be tricky!

## Common pre-requisites for Parallel and Distributed Computing

1. Data Structures and Algorithms: You'll learn about fundamental data structures and algorithm design techniques. This class is crucial for understanding how to optimize parallel algorithms.

2. Computer Architecture: This course covers the basics of how computers are built and operate. It's essential for grasping parallel architectures and their impact on performance.

3. Operating Systems: You'll dive into how operating systems manage resources and processes. This knowledge is key for understanding distributed systems and concurrent programming.

## Classes similar to Parallel and Distributed Computing

1. High-Performance Computing: Focuses on optimizing code for supercomputers and clusters. You'll learn about parallel programming models and performance tuning techniques.

2. Cloud Computing: Covers the principles and technologies behind cloud services. You'll explore distributed storage, virtualization, and scalable web applications.

3. Big Data Systems: Deals with processing and analyzing massive datasets. You'll learn about distributed data processing frameworks like Hadoop and Spark.

4. Computer Networks: Explores the fundamentals of network protocols and architectures. This class is crucial for understanding communication in distributed systems.

## Majors related to Parallel and Distributed Computing

1. Computer Engineering: Combines computer science and electrical engineering to design and develop computer systems and networks. Students learn both hardware and software aspects of computing.

2. Computer Science: Focuses on the theoretical and practical aspects of computation and information processing. Students study algorithms, programming languages, and software development.

3. Electrical Engineering: Deals with the study and application of electricity, electronics, and electromagnetism. Students learn about computer architecture and digital systems design.

4. Data Science: Combines statistics, mathematics, and computer science to extract insights from data. Students learn about big data processing and distributed computing systems.

## What can you do with a degree in Parallel and Distributed Computing?

1. Distributed Systems Engineer: Design and implement large-scale distributed systems for companies like Google or Amazon. You'll work on making sure these systems are scalable, reliable, and efficient.

2. High-Performance Computing Specialist: Optimize code for supercomputers used in scientific research or financial modeling. You'll be pushing the limits of what's possible with parallel computing.

3. Cloud Infrastructure Architect: Design and manage cloud computing platforms for companies or cloud service providers. You'll work on making sure these systems can handle massive amounts of data and users.

4. Big Data Engineer: Develop and maintain systems for processing and analyzing huge datasets. You'll work with technologies like Hadoop and Spark to extract valuable insights from data.

## Parallel and Distributed Computing FAQs

1. How much math is involved in this course? There's a fair amount, especially when it comes to analyzing algorithm performance and scalability. You'll need a good grasp of calculus and linear algebra.

2. Can I take this course if I'm not a computer engineering major? Usually, yes, but you'll need a strong programming background and some knowledge of computer architecture.

3. What programming languages are typically used in this course? It varies, but C, C++, and Java are common choices. You might also use specialized parallel programming libraries like OpenMP or MPI.

4. How does this course relate to machine learning and AI? Parallel and distributed computing techniques are crucial for scaling up machine learning algorithms to handle big data. You'll learn principles that are directly applicable to large-scale AI systems.

Computer Engineering

1.1 Fundamentals of Parallel and Distributed Computing

1.3 Challenges and Opportunities in Parallel Computing

1.2 Historical Development and Motivations

1.4 Applications and Use Cases

2.2 Shared Memory Architectures

2.3 Distributed Memory Architectures

2.4 Hybrid and Heterogeneous Architectures

2.1 Flynn's Taxonomy and Classification of Parallel Systems

3.1 Shared Memory Programming Models

3.2 Message Passing Programming Models

3.3 Data Parallel and SIMD Models

3.5 Hybrid Programming Models

3.4 Task Parallel and Work Stealing Models

4.1 OpenMP Fundamentals and Directives

4.2 Parallel Regions and Work Sharing Constructs

4.3 Synchronization and Data Sharing

4.4 Advanced OpenMP Features and Best Practices

5.1 MPI Basics and Point-to-Point Communication

5.2 Collective Communication Operations

5.3 Derived Datatypes and Communicators

5.4 Advanced MPI Concepts and Performance Optimization

6.1 Parallel Algorithm Design Strategies

6.2 Decomposition and Mapping Techniques

6.4 Parallel Complexity Theory

6.3 Performance Metrics and Scalability Analysis

7.1 Static and Dynamic Load Balancing Techniques

7.2 Task Scheduling Algorithms

7.3 Work Stealing and Task Migration

7.4 Load Balancing in Heterogeneous Systems

8.1 Amdahl's Law and Gustafson's Law

8.3 Performance Profiling and Analysis Tools

8.4 Optimization Techniques for Parallel Programs

8.2 Strong and Weak Scaling

9.1 Locks, Semaphores, and Barriers

9.2 Communication Patterns and Overlapping

9.3 Memory Consistency Models

9.4 Reducing Communication Overhead

10.2 Checkpoint-Restart Mechanisms

10.4 Algorithm-Based Fault Tolerance

10.3 Replication and Redundancy Techniques

10.1 Types of Faults and Failures in Parallel Systems

11.2 Parallel File Systems Architecture

11.1 Parallel I/O Concepts and Challenges

11.3 MPI-IO and High-Level I/O Libraries

11.4 I/O Optimization Techniques

12.2 CUDA Thread Hierarchy and Memory Model

12.3 CUDA Kernel Optimization Techniques

12.4 GPU-Accelerated Libraries and Applications

12.1 GPU Architecture and CUDA Programming Model

13.1 MapReduce and Hadoop

13.2 Apache Spark and Distributed Data Processing

13.3 Stream Processing Systems

13.4 Graph Processing Frameworks

14.1 Cloud Computing Models and Services

14.2 Virtualization Technologies

14.3 Container-Based Virtualization and Orchestration

14.4 Serverless Computing and Function-as-a-Service

15.2 Data Analytics and Machine Learning

15.3 High-Performance Computing in Industry

15.4 Emerging Trends in Parallel and Distributed Computing

15.1 Scientific Computing Applications

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