course outline

Author: ashishgopalhattimare

README.md

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 # Parallel-Concurrent-and-Distributed-Programming-in-Java
 Parallel, Concurrent, and Distributed Programming in Java | Coursera
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+## 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
+
+<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
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+<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
+
+<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
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+
+## 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
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+<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
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+<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
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+<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
+
+## 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
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+<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
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+<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
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+<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
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