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https://github.com/ashishgopalhattimare/parallel-concurrent-and-distributed-programming-in-java

Parallel, Concurrent, and Distributed Programming in Java | Coursera
https://github.com/ashishgopalhattimare/parallel-concurrent-and-distributed-programming-in-java

block-isolation boruvka-algorithm concurrent-programming critical-section distributed-programming java-8 kafka locks mapreduce-java mpi parallel-programming rice-university spark synchronization threads

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Parallel, Concurrent, and Distributed Programming in Java | Coursera

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# Parallel-Concurrent-and-Distributed-Programming-in-Java





  



 Parallel Concurrent and Distributed Programming in Java | Coursera Certification 



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LEGENDS LABELLING 

✔️	- The topics covered during the course

✅      - Self-done assignment

☑	- Instructor assistence required



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## Parallel Programming in Java



Week 1 : Task Parallelism



✔️ Demonstrate task parallelism using Asynkc/Finish constructs 

✔️ Create task-parallel programs using Java's Fork/Join Framework 

✔️ Interpret Computation Graph abstraction for task-parallel programs 

✔️ Evaluate the Multiprocessor Scheduling problem using Computation Graphs 

✔️ Assess sequetional bottlenecks using Amdahl's Law 



✅ Mini project 1 : Reciproncal-Array-Sum using the Java Fork/Join Framework



Week 2 : Functional Parallelism



✔️ Demonstrate functional parallelism using the Future construct 

✔️ Create functional-parallel programs using Java's Fork/Join Framework 

✔️ Apply the princple of memoization to optimize functional parallelism 

✔️ Create functional-parallel programs using Java Streams 

✔️ Explain the concepts of data races and functional/structural determinism 



✅ Mini project 2 : Analysing Student Statistics Using Java Parallel Streams



Week 3 : Loop Parallelism



✔️ Create programs with loop-level parallelism using the Forall and Java Stream constructs 

✔️ Evaluate loop-level parallelism in a matrix-multiplication example 

✔️ Examine the barrier construct for parallel loops 

✔️ Evaluate parallel loops with barriers in an iterative-averaging example 

✔️ Apply the concept of iteration grouping/chunking to improve the performance of parallel loops 



✅ Mini project 3 : Parallelizing Matrix-Matrix Multiply Using Loop Parallelism



Week 4 : Data flow Synchronization and Pipelining



✔️ Create split-phase barriers using Java's Phaser construct 

✔️ Create point-to-point synchronization patterns using Java's Phaser construct 

✔️ Evaluate parallel loops with point-to-point synchronization in an iterative-averaging example 

✔️ Analyze pipeline parallelism using the principles of point-to-point synchronization 

✔️ Interpret data flow parallelism using the data-driven-task construct 



☑ Mini project 4 : Using Phasers to Optimize Data-Parallel Applications



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## Concurrent Programming in Java

	

Week 1 : Threads and Locks



✔️ Understand the role of Java threads in building concurrent programs 

✔️ Create concurrent programs using Java threads and the synchronized statement (structured locks) 

✔️ Create concurrent programs using Java threads and lock primitives in the java.util.concurrent library (unstructured locks) 

✔️ Analyze programs with threads and locks to identify liveness and related concurrency bugs 

✔️ Evaluate different approaches to solving the classical Dining Philosophers Problem 



✅ Mini project 1 : Locking and Synchronization



Week 2 : Critical Sections and Isolation



✔️ Create concurrent programs with critical sections to coordinate accesses to shared resources 

✔️ Create concurrent programs with object-based isolation to coordinate accesses to shared resources with more overlap than critical sections 

✔️ Evaluate different approaches to implementing the Concurrent Spanning Tree algorithm 

✔️ Create concurrent programs using Java's atomic variables 

✔️ Evaluate the impact of read vs. write operations on concurrent accesses to shared resources 



✅ Mini project 2 : Global and Object-Based Isolation



Week 3 : Actors



✔️ Understand the Actor model for building concurrent programs 

✔️ Create simple concurrent programs using the Actor model 

✔️ Analyze an Actor-based implementation of the Sieve of Eratosthenes program 

✔️ Create Actor-based implementations of the Producer-Consumer pattern 

✔️ Create Actor-based implementations of concurrent accesses on a bounded resource 



✅ Mini project 3 : Sieve of Eratosthenes Using Actor Parallelism



Week 4 : Concurrent Data Structures



✔️ Understand the principle of optimistic concurrency in concurrent algorithms 

✔️ Understand implementation of concurrent queues based on optimistic concurrency 

✔️ Understand linearizability as a correctness condition for concurrent data structures 

✔️ Create concurrent Java programs that use the java.util.concurrent.ConcurrentHashMap library 

✔️ Analyze a concurrent algorithm for computing a Minimum Spanning Tree of an undirected graph 



☑ Mini project 4 : Parallelization of Boruvka's Minimum Spanning Tree Algorithm



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## Distributed Programming in Java



Week 1 : Distributed Map Reduce



✔️ Explain the MapReduce paradigm for analyzing data represented as key-value pairs 

✔️ Apply the MapReduce paradigm to programs written using the Apache Hadoop framework 

✔️ Create Map Reduce programs using the Apache Spark framework 

✔️ Acknowledge the TF-IDF statistic used in data mining, and how it can be computed using the MapReduce paradigm 

✔️ Create an implementation of the PageRank algorithm using the Apache Spark framework 



☑ Mini project 1 : Page Rank with Spark



Week 2 : Client-Server Programming



✔️ Generate distributed client-server applications using sockets 

✔️ Demonstrate different approaches to serialization and deserialization of data structures for distributed programming 

✔️ Recall the use of remote method invocations as a higher-level primitive for distributed programming (compared to sockets) 

✔️ Evaluate the use of multicast sockets as a generalization of sockets 

✔️ Employ distributed publish-subscribe applications using the Apache Kafka framework 



✅ Mini project 2 : Filer Server



Week 3 : Message Passing



✔️ Create distributed applications using the Single Program Multiple Data (SPMD) model 

✔️ Create message-passing programs using point-to-point communication primitives in MPI 

✔️ Identify message ordering and deadlock properties of MPI programs 

✔️ Evaluate the advantages of non-blocking communication relative to standard blocking communication primitives 

✔️ Explain collective communication as a generalization of point-to-point communication 



☑ Mini project 3 : Matrix Multiply in MPI



Week 4 : Combining Distribution and Multuthreading



✔️ Distinguish processes and threads as basic building blocks of parallel, concurrent, and distributed Java programs 

✔️ Create multithreaded servers in Java using threads and processes 

✔️ Demonstrate how multithreading can be combined with message-passing programming models like MPI 

✔️ Analyze how the actor model can be used for distributed programming 

✔️ Assess how the reactive programming model can be used for distrubted programming 



✅ Mini project 4 : Multi-Threaded File Server