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Parallel, Concurrent, and Distributed Programming in Java Specialization

Parallel, Concurrent, Distributed, Programming in Java Specialization - Rice U.

• Parallel Computing

  • TASK PARALLELISM
    • 1.1 Task Creation and Termination (Async, Finish)
    • 1.2 Tasks in Java's Fork/Join Framework
    • 1.3 Computation Graphs, Work, Span
    • 1.4 Multiprocessor Scheduling, Parallel Speedup
    • 1.5 Amdahl's Law
    • Mini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework
  • FUNCTIONAL PARALLELISM
    • 2.1 Futures: Tasks with Return Values
    • 2.2 Futures in Java's Fork/Join Framework
    • 2.3 Memoization
    • 2.4 Java Streams
    • 2.5 Data Races and Determinism
    • Mini Project 2: Analyzing Student Statistics Using Java Parallel Streams
  • LOOP PARALLELISM
    • 3.1 Parallel Loops
    • 3.2 Parallel Matrix Multiplication
    • 3.3 Barriers in Parallel Loops
    • 3.4 Parallel One-Dimensional Iterative Averaging
    • 3.5 Iteration Grouping/Chunking in Parallel Loops
    • Mini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism
  • DATA FLOW SYNCHRONIZATION AND PIPELINING
    • 4.1 Split-phase Barriers with Java Phasers
    • 4.2 Point-to-Point Sychronization with Phasers
    • 4.3 One-Dimensional Iterative Averaging with Phasers
    • 4.4 Pipeline Parallelism
    • 4.5 Data Flow Parallelism
    • Mini Project 4: Using Phasers to Optimize Data-Parallel Applications

• Concurrent Programming:

  • THREADS AND LOCKS
    • 1.1 Threads
    • 1.2 Structured Locks
    • 1.3 Unstructured Locks
    • 1.4 Liveness
    • 1.5 Dining Philosophers
    • Mini Project 1: Locking and Synchronization
  • CRITICAL SECTIONS AND ISOLATION
    • 2.1 Critical Sections
    • 2.2 Object Based Isolation (Monitors)
    • 2.3 Concurrent Spanning Tree Algorithm
    • 2.4 Atomic Variables
    • 2.5 Read, Write Isolation
    • Mini Project 2: Global and Object-Based Isolation
  • ACTORS
    • 3.1 Actors
    • 3.2 Actor Examples
    • 3.3 Sieve of Eratosthenes Algorithm
    • 3.4 Producer-Consumer Problem
    • 3.5 Bounded Buffer Problem
    • Mini Project 3: Sieve of Eratosthenes Using Actor Parallelism
  • CONCURRENT DATA STRUCTURES
    • 4.1 Optimistic Concurrency
    • 4.2 Concurrent Queue
    • 4.3 Linearizability
    • 4.4 Concurrent Hash Map
    • 4.5 Concurrent Minimum Spanning Tree Algorithm
    • Mini Project 4: Parallelization of Boruvka's Minimum Spanning Tree Algorithm

• Distributed Computing

  • DISTRIBUTED MAP REDUCE
    • 1.1 Introduction to Map-Reduce
    • 1.2 Hadoop Framework
    • 1.3 Spark Framework
    • 1.4 TF-IDF Example
    • 1.5 Page Rank Example
    • Mini Project 1: Page Rank with Spark
  • CLIENT-SERVER PROGRAMMING
    • 2.1 Introduction to Sockets
    • 2.2 Serialization/Deserialization
    • 2.3 Remote Method Invocation
    • 2.4 Multicast Sockets
    • 2.5 Publish-Subscribe Model
    • Mini Project 2: File Server
  • MESSAGE PASSING
    • 3.1 Single Program Multiple Data (SPMD) model
    • 3.2 Point-to-Point Communication
    • 3.3 Message Ordering and Deadlock
    • 3.4 Non-Blocking Communications
    • 3.5 Collective Communication
    • Mini Project 3: Matrix Multiply in MPI
  • COMBINING DISTRIBUTION AND MULTITHREADING
    • 4.1 Processes and Threads
    • 4.2 Multithreaded Servers
    • 4.3 MPI and Threading
    • 4.4 Distributed Actors
    • 4.5 Distributed Reactive Programming
    • Mini Project 4: Multi-Threaded File Server