Big Data Analytics: Parallel Computing and Threads

Big Data Analytics: Parallel Computing and Threads

Course Overview
This study material introduces the essential concepts of Parallel Computing and the use of Threads in the context of Big Data Analytics. Learn how to manage and synchronize multiple threads efficiently, ensuring optimized performance for large-scale data processing tasks.

Key Topics Covered:

• Processes and Threads: Understand the difference between processes and threads in parallel computing. Explore how threads allow for concurrent execution, speeding up computations in big data environments.

• Threads APIs: Learn how to work with Threads APIs to create and manage threads in your application.

• Starting Threads: Practical examples on how to start threads, including:

  • Interrupting Threads
  • Computation Examples: Learn about issues like non-cooperative/broken threads and how to fix them with cooperative threads.
  • Sleeping Threads: Explore how sleeping threads can be used to manage thread execution timing.

• Synchronization:

  • Waiting for Termination: How to synchronize threads to wait for each other’s completion.
  • Synchronized Methods and Atomic Objects: Learn how to prevent race conditions and ensure thread safety.
  • Deadlock: Understand deadlock situations with real-world examples and explore solutions to prevent these synchronization issues.
  • Conditions and Guarded Blocks: Examples of using conditions and guarded blocks to control thread execution.
  • Thread States: Explore the different states that threads can occupy during execution, and learn how to manage them.

• Synchronization II - Locks:

  • Learn about Locks and their usage in ensuring synchronized access to shared resources.
  • Best practices for using locks with examples.
  • Thread Pools: Understand the concept of thread pools and how to manage them for optimized performance.
  • Examples of Dependency Graphs to manage thread execution order in complex systems.

• OpenMP for Parallel Computing:

  • Parallel Sections: Learn how to define and manage parallel sections using OpenMP, a widely-used API for parallel computing.
  • Directives: Explore how directives in OpenMP allow for easy parallelization of tasks with minimal code changes.
  • Several examples are provided to showcase the practical application of OpenMP in data-intensive environments.

Why Choose This Material?

• A comprehensive guide to parallel computing with a focus on thread management for big data analytics.
• Includes practical code examples for thread synchronization, deadlock prevention, and parallel processing using OpenMP.
• Perfect for students and professionals looking to leverage multi-threading to handle large-scale data processing tasks in big data environments.

This material is crucial for anyone involved in Big Data, Data Engineering, or Software Development, where efficient parallel computation is required to process vast amounts of data.