Big Data Analytics: A. Parallel Computing / A.2 Message Passing Interface (MPI)

Big Data Analytics: Parallel Computing with Message Passing Interface (MPI)

Course Overview
This study material provides a deep dive into Message Passing Interface (MPI), a powerful standard for enabling parallel computing across distributed memory systems. Learn the key concepts of MPI, including point-to-point communication, collective operations, and one-sided communication, with practical examples to guide you through real-world applications.

Key Topics Covered:

• The MPI Standard: Introduction to the MPI standard, focusing on OpenMPI. Understand how to compile and run MPI programs, including a basic Java MPI Skeleton and the classic Hello World MPI example to get you started.

• Point-to-Point Communication:

  • Blocking vs. Non-blocking Communication: Learn the difference between blocking and non-blocking send/receive operations.
  • Blocking Send and Receive: Explore the synchronous method for sending and receiving messages in MPI.
  • Computing Pi:
    • Sequential Computation: Start by computing Pi sequentially.
    • Parallel Computation: Learn how to parallelize the Pi computation using MPI for more efficient performance.
  • Non-blocking Communication: Delve into asynchronous message passing with practical examples showing its advantages in distributed computing.
  • Blocking and Non-blocking Communication Examples: Compare and contrast these methods through real-world examples.

• Collective Communication:

  • Communicator Methods: Learn how to use communicator methods to manage groups of processes in MPI.
  • Broadcast: Example showing how to broadcast data from one process to all other processes.
  • Reduce Operation: Explore the Reduce method in MPI to aggregate data across multiple processes, with practical examples.
  • Nearest Neighbor Problem:
    • Sequential Nearest Neighbor: Learn how to solve the nearest neighbor problem sequentially.
    • Sequential with Partial Distances: Optimize the sequential solution by breaking it down into partial distances.
    • Parallel Nearest Neighbor: Discover how to parallelize the nearest neighbor algorithm for better performance in big data contexts.
  • More Collective Communication Operations: Further MPI collective operations are introduced to enhance parallel processing capabilities.

• One-Sided Communication:

  • One-Sided Put Example: Learn how to use one-sided communication in MPI to write data directly into another process's memory.
  • One-Sided Get Example: Understand how to retrieve data from another process's memory without explicit synchronization.
  • Explore additional MPI capabilities for advanced parallel computing tasks.

Why Choose This Material?

• Detailed coverage of MPI programming, with both blocking and non-blocking communication methods explained.
• Practical, real-world examples to help you master point-to-point and collective communication.
• Key insights into optimizing parallel algorithms for distributed memory systems using MPI.

This material is ideal for students and professionals in Big Data Analytics, High-Performance Computing (HPC), or Parallel Computing who want to build efficient, scalable applications using MPI.