update readme

Author: ashishgopalhattimare

README.md

@@ -5,92 +5,114 @@ Parallel, Concurrent, and Distributed Programming in Java | Coursera
 ## 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
+- [ ] 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
+
+✅ <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
+- [ ] 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
+
+✅ <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
+- [ ] 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
+
+✅ <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
+- [ ] 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>
 
 ## 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
+- [ ] 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
+
+✅ <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
+- [ ] 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
+
+✅ <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
+- [ ] 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
+
+✅ <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
+- [ ] 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>
 
 ## 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
+- [ ] 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>
 
 <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
+- [ ] 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
+
+✅ <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
+- [ ] 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>
 
 <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
+- [ ] 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
 
+✅ <i>Mini project 4 : Multi-Threaded File Server</i>