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 # Parallel-Concurrent-and-Distributed-Programming-in-Java
-Parallel, Concurrent, and Distributed Programming in Java | Coursera
 
+<p align="center">
+  <img src="https://github.com/ashishgopalhattimare/Parallel-Concurrent-and-Distributed-Programming-in-Java/blob/master/certificate/NDV8ZGXD45BP.png
+" width="30%">
+</p>
+<p align="center"> <i>Parallel Concurrent and Distributed Programming in Java | Coursera Certification</i> </p>
+---
+
+<b>LEGENDS LABELLING</b> </br>
+✔️	- The topics covered during the course</br>
+✅      - Self-done assignment</br>
+☑	- Instructor assistence required</br>
+
+---
 
 ## Parallel Programming in Java
 
 <b><u>Week 1 : Task Parallelism</u></b>
-- [ ] 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
+
+✔️ Demonstrate task parallelism using Asynkc/Finish constructs </br>
+✔️ Create task-parallel programs using Java's Fork/Join Framework </br>
+✔️ Interpret Computation Graph abstraction for task-parallel programs </br>
+✔️ Evaluate the Multiprocessor Scheduling problem using Computation Graphs </br>
+✔️ Assess sequetional bottlenecks using Amdahl's Law </br>
+
 
 ✅ <i>Mini project 1 : Reciproncal-Array-Sum using the Java Fork/Join Framework</i>
 
 <b><u>Week 2 : Functional Parallelism</u></b>
 
-- [ ] 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
+✔️ Demonstrate functional parallelism using the Future construct </br>
+✔️ Create functional-parallel programs using Java's Fork/Join Framework </br>
+✔️ Apply the princple of memoization to optimize functional parallelism </br>
+✔️ Create functional-parallel programs using Java Streams </br>
+✔️ Explain the concepts of data races and functional/structural determinism </br>
 
 ✅ <i>Mini project 2 : Analysing Student Statistics Using Java Parallel Streams</i>
 
 <b><u>Week 3 : Loop Parallelism</u></b>
-- [ ] 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
+
+✔️ Create programs with loop-level parallelism using the Forall and Java Stream constructs </br>
+✔️ Evaluate loop-level parallelism in a matrix-multiplication example </br>
+✔️ Examine the barrier construct for parallel loops </br>
+✔️ Evaluate parallel loops with barriers in an iterative-averaging example </br>
+✔️ Apply the concept of iteration grouping/chunking to improve the performance of parallel loops </br>
 
 ✅ <i>Mini project 3 : Parallelizing Matrix-Matrix Multiply Using Loop Parallelism</i>
 
 <b><u>Week 4 : Data flow Synchronization and Pipelining</u></b>
-- [ ] 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
 
-✅ <i>Mini project 4 : Using Phasers to Optimize Data-Parallel Applications</i>
+✔️ Create split-phase barriers using Java's Phaser construct </br>
+✔️ Create point-to-point synchronization patterns using Java's Phaser construct </br>
+✔️ Evaluate parallel loops with point-to-point synchronization in an iterative-averaging example </br>
+✔️ Analyze pipeline parallelism using the principles of point-to-point synchronization </br>
+✔️ Interpret data flow parallelism using the data-driven-task construct </br>
+
+☑ <i>Mini project 4 : Using Phasers to Optimize Data-Parallel Applications</i>
+
+---
 
 ## Concurrent Programming in Java
 
 <b><u>Week 1 : Threads and Locks</u></b>
-- [ ] 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
+
+✔️ Understand the role of Java threads in building concurrent programs </br>
+✔️ Create concurrent programs using Java threads and the synchronized statement (structured locks) </br>
+✔️ Create concurrent programs using Java threads and lock primitives in the java.util.concurrent library (unstructured locks) </br>
+✔️ Analyze programs with threads and locks to identify liveness and related concurrency bugs </br>
+✔️ Evaluate different approaches to solving the classical Dining Philosophers Problem </br>
 
 ✅ <i>Mini project 1 : Locking and Synchronization</i>
 
 <b><u>Week 2 : Critical Sections and Isolation</u></b>
-- [ ] 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
+
+✔️ Create concurrent programs with critical sections to coordinate accesses to shared resources </br>
+✔️ Create concurrent programs with object-based isolation to coordinate accesses to shared resources with more overlap than critical sections </br>
+✔️ Evaluate different approaches to implementing the Concurrent Spanning Tree algorithm </br>
+✔️ Create concurrent programs using Java's atomic variables </br>
+✔️ Evaluate the impact of read vs. write operations on concurrent accesses to shared resources </br>
 
 ✅ <i>Mini project 2 : Global and Object-Based Isolation</i>
 
 <b><u>Week 3 : Actors</u></b>
-- [ ] 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
+
+✔️ Understand the Actor model for building concurrent programs </br>
+✔️ Create simple concurrent programs using the Actor model </br>
+✔️ Analyze an Actor-based implementation of the Sieve of Eratosthenes program </br>
+✔️ Create Actor-based implementations of the Producer-Consumer pattern </br>
+✔️ Create Actor-based implementations of concurrent accesses on a bounded resource </br>
 
 ✅ <i>Mini project 3 : Sieve of Eratosthenes Using Actor Parallelism</i>
 
 <b><u>Week 4 : Concurrent Data Structures</u></b>
-- [ ] 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
 
-✅ <i>Mini project 4 : Parallelization of Boruvka's Minimum Spanning Tree Algorithm</i>
+✔️ Understand the principle of optimistic concurrency in concurrent algorithms </br>
+✔️ Understand implementation of concurrent queues based on optimistic concurrency </br>
+✔️ Understand linearizability as a correctness condition for concurrent data structures </br>
+✔️ Create concurrent Java programs that use the java.util.concurrent.ConcurrentHashMap library </br>
+✔️ Analyze a concurrent algorithm for computing a Minimum Spanning Tree of an undirected graph </br>
+
+☑ <i>Mini project 4 : Parallelization of Boruvka's Minimum Spanning Tree Algorithm</i>
+
+---
 
 ## Distributed Programming in Java
 
 <b><u>Week 1 : Distributed Map Reduce</u></b>
-- [ ] 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
 
-✅ <i>Mini project 1 : Page Rank with Spark</i>
+✔️ Explain the MapReduce paradigm for analyzing data represented as key-value pairs </br>
+✔️ Apply the MapReduce paradigm to programs written using the Apache Hadoop framework </br>
+✔️ Create Map Reduce programs using the Apache Spark framework </br>
+✔️ Acknowledge the TF-IDF statistic used in data mining, and how it can be computed using the MapReduce paradigm </br>
+✔️ Create an implementation of the PageRank algorithm using the Apache Spark framework </br>
+
+☑ <i>Mini project 1 : Page Rank with Spark</i>
 
 <b><u>Week 2 : Client-Server Programming</u></b>
-- [ ] 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
+
+✔️ Generate distributed client-server applications using sockets </br>
+✔️ Demonstrate different approaches to serialization and deserialization of data structures for distributed programming </br>
+✔️ Recall the use of remote method invocations as a higher-level primitive for distributed programming (compared to sockets) </br>
+✔️ Evaluate the use of multicast sockets as a generalization of sockets </br>
+✔️ Employ distributed publish-subscribe applications using the Apache Kafka framework </br>
 
 ✅ <i>Mini project 2 : Filer Server</i>
 
 <b><u>Week 3 : Message Passing</u></b>
-- [ ] 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
 
-✅ <i>Mini project 3 : Matrix Multiply in MPI</i>
+✔️ Create distributed applications using the Single Program Multiple Data (SPMD) model </br>
+✔️ Create message-passing programs using point-to-point communication primitives in MPI </br>
+✔️ Identify message ordering and deadlock properties of MPI programs </br>
+✔️ Evaluate the advantages of non-blocking communication relative to standard blocking communication primitives </br>
+✔️ Explain collective communication as a generalization of point-to-point communication </br>
+
+☑ <i>Mini project 3 : Matrix Multiply in MPI</i>
 
 <b><u>Week 4 : Combining Distribution and Multuthreading</u></b>
-- [ ] 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
+
+✔️ Distinguish processes and threads as basic building blocks of parallel, concurrent, and distributed Java programs </br>
+✔️ Create multithreaded servers in Java using threads and processes </br>
+✔️ Demonstrate how multithreading can be combined with message-passing programming models like MPI </br>
+✔️ Analyze how the actor model can be used for distributed programming </br>
+✔️ Assess how the reactive programming model can be used for distrubted programming </br>
 
 ✅ <i>Mini project 4 : Multi-Threaded File Server</i>