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1 | 1 | # Parallel-Concurrent-and-Distributed-Programming-in-Java |
2 | 2 | Parallel, Concurrent, and Distributed Programming in Java | Coursera |
| 3 | + |
| 4 | + |
| 5 | +## Parallel Programming in Java |
| 6 | + |
| 7 | +<b><u>Week 1 : Task Parallelism</u></b> |
| 8 | +- Demonstrate task parallelism using Asynkc/Finish constructs |
| 9 | +- Create task-parallel programs using Java's Fork/Join Framework |
| 10 | +- Interpret Computation Graph abstraction for task-parallel programs |
| 11 | +- Evaluate the Multiprocessor Scheduling problem using Computation Graphs |
| 12 | +- Assess sequetional bottlenecks using Amdahl's Law |
| 13 | + |
| 14 | +<b><u>Week 2 : Functional Parallelism</u></b> |
| 15 | + |
| 16 | +- Demonstrate functional parallelism using the Future construct |
| 17 | +- Create functional-parallel programs using Java's Fork/Join Framework |
| 18 | +- Apply the princple of memoization to optimize functional parallelism |
| 19 | +- Create functional-parallel programs using Java Streams |
| 20 | +- Explain the concepts of data races and functional/structural determinism |
| 21 | + |
| 22 | +<b><u>Week 3 : Loop Parallelism</u></b> |
| 23 | +- Create programs with loop-level parallelism using the Forall and Java Stream constructs |
| 24 | +- Evaluate loop-level parallelism in a matrix-multiplication example |
| 25 | +- Examine the barrier construct for parallel loops |
| 26 | +- Evaluate parallel loops with barriers in an iterative-averaging example |
| 27 | +- Apply the concept of iteration grouping/chunking to improve the performance of parallel loops |
| 28 | + |
| 29 | +<b><u>Week 4 : Data flow Synchronization and Pipelining</u></b> |
| 30 | +- Create split-phase barriers using Java's Phaser construct |
| 31 | +- Create point-to-point synchronization patterns using Java's Phaser construct |
| 32 | +- Evaluate parallel loops with point-to-point synchronization in an iterative-averaging example |
| 33 | +- Analyze pipeline parallelism using the principles of point-to-point synchronization |
| 34 | +- Interpret data flow parallelism using the data-driven-task construct |
| 35 | + |
| 36 | + |
| 37 | +## Concurrent Programming in Java |
| 38 | + |
| 39 | +<b><u>Week 1 : Threads and Locks</u></b> |
| 40 | +- Understand the role of Java threads in building concurrent programs |
| 41 | +- Create concurrent programs using Java threads and the synchronized statement (structured locks) |
| 42 | +- Create concurrent programs using Java threads and lock primitives in the java.util.concurrent library (unstructured locks) |
| 43 | +- Analyze programs with threads and locks to identify liveness and related concurrency bugs |
| 44 | +- Evaluate different approaches to solving the classical Dining Philosophers Problem |
| 45 | + |
| 46 | +<b><u>Week 2 : Critical Sections and Isolation</u></b> |
| 47 | +- Create concurrent programs with critical sections to coordinate accesses to shared resources |
| 48 | +- Create concurrent programs with object-based isolation to coordinate accesses to shared resources with more overlap than critical sections |
| 49 | +- Evaluate different approaches to implementing the Concurrent Spanning Tree algorithm |
| 50 | +- Create concurrent programs using Java's atomic variables |
| 51 | +- Evaluate the impact of read vs. write operations on concurrent accesses to shared resources |
| 52 | + |
| 53 | +<b><u>Week 3 : Actors</u></b> |
| 54 | +- Understand the Actor model for building concurrent programs |
| 55 | +- Create simple concurrent programs using the Actor model |
| 56 | +- Analyze an Actor-based implementation of the Sieve of Eratosthenes program |
| 57 | +- Create Actor-based implementations of the Producer-Consumer pattern |
| 58 | +- Create Actor-based implementations of concurrent accesses on a bounded resource |
| 59 | + |
| 60 | +<b><u>Week 4 : Concurrent Data Structures</u></b> |
| 61 | +- Understand the principle of optimistic concurrency in concurrent algorithms |
| 62 | +- Understand implementation of concurrent queues based on optimistic concurrency |
| 63 | +- Understand linearizability as a correctness condition for concurrent data structures |
| 64 | +- Create concurrent Java programs that use the java.util.concurrent.ConcurrentHashMap library |
| 65 | +- Analyze a concurrent algorithm for computing a Minimum Spanning Tree of an undirected graph |
| 66 | + |
| 67 | +## Distributed Programming in Java |
| 68 | + |
| 69 | +<b><u>Week 1 : Distributed Map Reduce</u></b> |
| 70 | +- Explain the MapReduce paradigm for analyzing data represented as key-value pairs |
| 71 | +- Apply the MapReduce paradigm to programs written using the Apache Hadoop framework |
| 72 | +- Create Map Reduce programs using the Apache Spark framework |
| 73 | +- Acknowledge the TF-IDF statistic used in data mining, and how it can be computed using the MapReduce paradigm |
| 74 | +- Create an implementation of the PageRank algorithm using the Apache Spark framework |
| 75 | + |
| 76 | +<b><u>Week 2 : Client-Server Programming</u></b> |
| 77 | +- Generate distributed client-server applications using sockets |
| 78 | +- Demonstrate different approaches to serialization and deserialization of data structures for distributed programming |
| 79 | +- Recall the use of remote method invocations as a higher-level primitive for distributed programming (compared to sockets) |
| 80 | +- Evaluate the use of multicast sockets as a generalization of sockets |
| 81 | +- Employ distributed publish-subscribe applications using the Apache Kafka framework |
| 82 | + |
| 83 | +<b><u>Week 3 : Message Passing</u></b> |
| 84 | +- Create distributed applications using the Single Program Multiple Data (SPMD) model |
| 85 | +- Create message-passing programs using point-to-point communication primitives in MPI |
| 86 | +- Identify message ordering and deadlock properties of MPI programs |
| 87 | +- Evaluate the advantages of non-blocking communication relative to standard blocking communication primitives |
| 88 | +- Explain collective communication as a generalization of point-to-point communication |
| 89 | + |
| 90 | +<b><u>Week 4 : Combining Distribution and Multuthreading</u></b> |
| 91 | +- Distinguish processes and threads as basic building blocks of parallel, concurrent, and distributed Java programs |
| 92 | +- Create multithreaded servers in Java using threads and processes |
| 93 | +- Demonstrate how multithreading can be combined with message-passing programming models like MPI |
| 94 | +- Analyze how the actor model can be used for distributed programming |
| 95 | +- Assess how the reactive programming model can be used for distrubted programming |
| 96 | + |
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