Concurrency, Multithreading and Parallel Computing in Java

Multithreading and Concurrent Programming, Parallel Computation and MapReduce in Java + Fork-Join and Stream API

This course is about the basics of multithreading and concurrent programming with some parallel concepts. In the 21st century this topic is becoming more and more popular with the advent of Big Data and Machine Learning. We will consider the low level concepts such as threads, synchronization and locks. The second chapter will be about concurrent library: of course there are built in classes and interfaces that we can use when implementing multithreaded applications. Then we develope little programs as show-cases for multithreading: the dining-philosopher problem or the students in library simulation. Last chapter is about parallel computing and MapReduce.

What you’ll learn

  • Understand basic concurrency.
  • Understand the basics of multithreading.
  • Understand parallel processing.
  • Able to use the concepts in real life scenarios.

Course Content

  • Introduction –> 1 lecture • 2min.
  • Multithreading Theory –> 5 lectures • 17min.
  • Threads Manipulation –> 6 lectures • 30min.
  • Inter-Thread Communication –> 11 lectures • 59min.
  • Multithreading Concepts –> 10 lectures • 48min.
  • Creating Threads with Executors –> 7 lectures • 34min.
  • Concurrent Collections –> 7 lectures • 1hr 4min.
  • Multithreading Exercise I – Dining Philosophers Problem –> 6 lectures • 39min.
  • Multithreading Exercise II -Students Library Simulation –> 6 lectures • 16min.
  • Multithreading Exercise III – Miner Game Simulation –> 5 lectures • 17min.
  • Parallel Algorithms –> 11 lectures • 1hr 2min.
  • Fork-Join Framework –> 7 lectures • 52min.
  • Using Java’s Stream API –> 6 lectures • 38min.
  • MapReduce and Parallelization –> 3 lectures • 14min.
  • Course Materials (DOWNLOADS) –> 1 lecture • 1min.

Concurrency, Multithreading and Parallel Computing in Java

Requirements

  • Basic Java (inheritance, object oriented programming).

This course is about the basics of multithreading and concurrent programming with some parallel concepts. In the 21st century this topic is becoming more and more popular with the advent of Big Data and Machine Learning. We will consider the low level concepts such as threads, synchronization and locks. The second chapter will be about concurrent library: of course there are built in classes and interfaces that we can use when implementing multithreaded applications. Then we develope little programs as show-cases for multithreading: the dining-philosopher problem or the students in library simulation. Last chapter is about parallel computing and MapReduce.

Section 1 – Multithreading Theory:

  • theory behind multithreading
  • pros and cons of multithreading
  • life cycle of a thead

Section 2 – Threads Manipulation:

  • starting threads (Runnable interface and Thread class)
  • join keyword
  • daemon threads

Section 3 – Inter-Thread Communication:

  • memory management of threads
  • synchronization and synchronized blocks
  • locks
  • wait and notify
  • producer-consumer problem and solution
  • concurrent collections
  • latch, cyclic barrier and blocking queues
  • delay queue, priority queue and concurrent maps

Section 4 – Multithreading Concepts:

  • volatile keywords
  • deadlocks and livelocks
  • semaphores and mutexes
  • dining philosophers problem
  • library application
  • miner game

Section 6 – Executors and ExecutorServices:

  • executors
  • executor services

Section 6 – Concurrent Collections:

  • latches
  • cyclic barriers
  • delay and priority queues
  • concurrent HashMaps

Section 7 –  Simulations:

  • dining philosophers problem
  • library problem

Section 8 – Parallel Algorithms:

  • what is parallel computing
  • parallel merge sort
  • parallel algorithms

Section 9 – Fork-Join Framework

  • Fork-Join framework
  • maximum finding in parallel manner

Section 10 – Stream API

  • the Stream API explained with examples
  • sequential streams and parallel streams

Section 11 – BigData and MapReduce:

  • what is MapReduce
  • MapReduce and Fork-Join framework

Thanks for joining my course, let’s get started!