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1 | 1 | # Parallel-Concurrent-and-Distributed-Programming-in-Java |
2 | | -Parallel, Concurrent, and Distributed Programming in Java | Coursera |
3 | 2 |
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| 3 | +<p align="center"> |
| 4 | + <img src="https://github.com/ashishgopalhattimare/Parallel-Concurrent-and-Distributed-Programming-in-Java/blob/master/certificate/NDV8ZGXD45BP.png |
| 5 | +" width="30%"> |
| 6 | +</p> |
| 7 | +<p align="center"> <i>Parallel Concurrent and Distributed Programming in Java | Coursera Certification</i> </p> |
| 8 | +--- |
| 9 | + |
| 10 | +<b>LEGENDS LABELLING</b> </br> |
| 11 | +✔️ - The topics covered during the course</br> |
| 12 | +✅ - Self-done assignment</br> |
| 13 | +☑ - Instructor assistence required</br> |
| 14 | + |
| 15 | +--- |
4 | 16 |
|
5 | 17 | ## Parallel Programming in Java |
6 | 18 |
|
7 | 19 | <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 |
| 20 | + |
| 21 | +✔️ Demonstrate task parallelism using Asynkc/Finish constructs </br> |
| 22 | +✔️ Create task-parallel programs using Java's Fork/Join Framework </br> |
| 23 | +✔️ Interpret Computation Graph abstraction for task-parallel programs </br> |
| 24 | +✔️ Evaluate the Multiprocessor Scheduling problem using Computation Graphs </br> |
| 25 | +✔️ Assess sequetional bottlenecks using Amdahl's Law </br> |
| 26 | + |
13 | 27 |
|
14 | 28 | ✅ <i>Mini project 1 : Reciproncal-Array-Sum using the Java Fork/Join Framework</i> |
15 | 29 |
|
16 | 30 | <b><u>Week 2 : Functional Parallelism</u></b> |
17 | 31 |
|
18 | | --[ ]Demonstrate functional parallelism using the Future construct |
19 | | --[ ]Create functional-parallel programs using Java's Fork/Join Framework |
20 | | --[ ]Apply the princple of memoization to optimize functional parallelism |
21 | | --[ ]Create functional-parallel programs using Java Streams |
22 | | --[ ]Explain the concepts of data races and functional/structural determinism |
| 32 | +✔️ Demonstrate functional parallelism using the Future construct </br> |
| 33 | +✔️ Create functional-parallel programs using Java's Fork/Join Framework </br> |
| 34 | +✔️ Apply the princple of memoization to optimize functional parallelism </br> |
| 35 | +✔️ Create functional-parallel programs using Java Streams </br> |
| 36 | +✔️ Explain the concepts of data races and functional/structural determinism </br> |
23 | 37 |
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24 | 38 | ✅ <i>Mini project 2 : Analysing Student Statistics Using Java Parallel Streams</i> |
25 | 39 |
|
26 | 40 | <b><u>Week 3 : Loop Parallelism</u></b> |
27 | | -- [ ] Create programs with loop-level parallelism using the Forall and Java Stream constructs |
28 | | -- [ ] Evaluate loop-level parallelism in a matrix-multiplication example |
29 | | -- [ ] Examine the barrier construct for parallel loops |
30 | | -- [ ] Evaluate parallel loops with barriers in an iterative-averaging example |
31 | | -- [ ] Apply the concept of iteration grouping/chunking to improve the performance of parallel loops |
| 41 | + |
| 42 | +✔️ Create programs with loop-level parallelism using the Forall and Java Stream constructs </br> |
| 43 | +✔️ Evaluate loop-level parallelism in a matrix-multiplication example </br> |
| 44 | +✔️ Examine the barrier construct for parallel loops </br> |
| 45 | +✔️ Evaluate parallel loops with barriers in an iterative-averaging example </br> |
| 46 | +✔️ Apply the concept of iteration grouping/chunking to improve the performance of parallel loops </br> |
32 | 47 |
|
33 | 48 | ✅ <i>Mini project 3 : Parallelizing Matrix-Matrix Multiply Using Loop Parallelism</i> |
34 | 49 |
|
35 | 50 | <b><u>Week 4 : Data flow Synchronization and Pipelining</u></b> |
36 | | -- [ ] Create split-phase barriers using Java's Phaser construct |
37 | | -- [ ] Create point-to-point synchronization patterns using Java's Phaser construct |
38 | | -- [ ] Evaluate parallel loops with point-to-point synchronization in an iterative-averaging example |
39 | | -- [ ] Analyze pipeline parallelism using the principles of point-to-point synchronization |
40 | | -- [ ] Interpret data flow parallelism using the data-driven-task construct |
41 | 51 |
|
42 | | -✅ <i>Mini project 4 : Using Phasers to Optimize Data-Parallel Applications</i> |
| 52 | +✔️ Create split-phase barriers using Java's Phaser construct </br> |
| 53 | +✔️ Create point-to-point synchronization patterns using Java's Phaser construct </br> |
| 54 | +✔️ Evaluate parallel loops with point-to-point synchronization in an iterative-averaging example </br> |
| 55 | +✔️ Analyze pipeline parallelism using the principles of point-to-point synchronization </br> |
| 56 | +✔️ Interpret data flow parallelism using the data-driven-task construct </br> |
| 57 | + |
| 58 | +☑ <i>Mini project 4 : Using Phasers to Optimize Data-Parallel Applications</i> |
| 59 | + |
| 60 | +--- |
43 | 61 |
|
44 | 62 | ## Concurrent Programming in Java |
45 | 63 |
|
46 | 64 | <b><u>Week 1 : Threads and Locks</u></b> |
47 | | -- [ ] Understand the role of Java threads in building concurrent programs |
48 | | -- [ ] Create concurrent programs using Java threads and the synchronized statement (structured locks) |
49 | | -- [ ] Create concurrent programs using Java threads and lock primitives in the java.util.concurrent library (unstructured locks) |
50 | | -- [ ] Analyze programs with threads and locks to identify liveness and related concurrency bugs |
51 | | -- [ ] Evaluate different approaches to solving the classical Dining Philosophers Problem |
| 65 | + |
| 66 | +✔️ Understand the role of Java threads in building concurrent programs </br> |
| 67 | +✔️ Create concurrent programs using Java threads and the synchronized statement (structured locks) </br> |
| 68 | +✔️ Create concurrent programs using Java threads and lock primitives in the java.util.concurrent library (unstructured locks) </br> |
| 69 | +✔️ Analyze programs with threads and locks to identify liveness and related concurrency bugs </br> |
| 70 | +✔️ Evaluate different approaches to solving the classical Dining Philosophers Problem </br> |
52 | 71 |
|
53 | 72 | ✅ <i>Mini project 1 : Locking and Synchronization</i> |
54 | 73 |
|
55 | 74 | <b><u>Week 2 : Critical Sections and Isolation</u></b> |
56 | | -- [ ] Create concurrent programs with critical sections to coordinate accesses to shared resources |
57 | | -- [ ] Create concurrent programs with object-based isolation to coordinate accesses to shared resources with more overlap than critical sections |
58 | | -- [ ] Evaluate different approaches to implementing the Concurrent Spanning Tree algorithm |
59 | | -- [ ] Create concurrent programs using Java's atomic variables |
60 | | -- [ ] Evaluate the impact of read vs. write operations on concurrent accesses to shared resources |
| 75 | + |
| 76 | +✔️ Create concurrent programs with critical sections to coordinate accesses to shared resources </br> |
| 77 | +✔️ Create concurrent programs with object-based isolation to coordinate accesses to shared resources with more overlap than critical sections </br> |
| 78 | +✔️ Evaluate different approaches to implementing the Concurrent Spanning Tree algorithm </br> |
| 79 | +✔️ Create concurrent programs using Java's atomic variables </br> |
| 80 | +✔️ Evaluate the impact of read vs. write operations on concurrent accesses to shared resources </br> |
61 | 81 |
|
62 | 82 | ✅ <i>Mini project 2 : Global and Object-Based Isolation</i> |
63 | 83 |
|
64 | 84 | <b><u>Week 3 : Actors</u></b> |
65 | | -- [ ] Understand the Actor model for building concurrent programs |
66 | | -- [ ] Create simple concurrent programs using the Actor model |
67 | | -- [ ] Analyze an Actor-based implementation of the Sieve of Eratosthenes program |
68 | | -- [ ] Create Actor-based implementations of the Producer-Consumer pattern |
69 | | -- [ ] Create Actor-based implementations of concurrent accesses on a bounded resource |
| 85 | + |
| 86 | +✔️ Understand the Actor model for building concurrent programs </br> |
| 87 | +✔️ Create simple concurrent programs using the Actor model </br> |
| 88 | +✔️ Analyze an Actor-based implementation of the Sieve of Eratosthenes program </br> |
| 89 | +✔️ Create Actor-based implementations of the Producer-Consumer pattern </br> |
| 90 | +✔️ Create Actor-based implementations of concurrent accesses on a bounded resource </br> |
70 | 91 |
|
71 | 92 | ✅ <i>Mini project 3 : Sieve of Eratosthenes Using Actor Parallelism</i> |
72 | 93 |
|
73 | 94 | <b><u>Week 4 : Concurrent Data Structures</u></b> |
74 | | -- [ ] Understand the principle of optimistic concurrency in concurrent algorithms |
75 | | -- [ ] Understand implementation of concurrent queues based on optimistic concurrency |
76 | | -- [ ] Understand linearizability as a correctness condition for concurrent data structures |
77 | | -- [ ] Create concurrent Java programs that use the java.util.concurrent.ConcurrentHashMap library |
78 | | -- [ ] Analyze a concurrent algorithm for computing a Minimum Spanning Tree of an undirected graph |
79 | 95 |
|
80 | | -✅ <i>Mini project 4 : Parallelization of Boruvka's Minimum Spanning Tree Algorithm</i> |
| 96 | +✔️ Understand the principle of optimistic concurrency in concurrent algorithms </br> |
| 97 | +✔️ Understand implementation of concurrent queues based on optimistic concurrency </br> |
| 98 | +✔️ Understand linearizability as a correctness condition for concurrent data structures </br> |
| 99 | +✔️ Create concurrent Java programs that use the java.util.concurrent.ConcurrentHashMap library </br> |
| 100 | +✔️ Analyze a concurrent algorithm for computing a Minimum Spanning Tree of an undirected graph </br> |
| 101 | + |
| 102 | +☑ <i>Mini project 4 : Parallelization of Boruvka's Minimum Spanning Tree Algorithm</i> |
| 103 | + |
| 104 | +--- |
81 | 105 |
|
82 | 106 | ## Distributed Programming in Java |
83 | 107 |
|
84 | 108 | <b><u>Week 1 : Distributed Map Reduce</u></b> |
85 | | -- [ ] Explain the MapReduce paradigm for analyzing data represented as key-value pairs |
86 | | -- [ ] Apply the MapReduce paradigm to programs written using the Apache Hadoop framework |
87 | | -- [ ] Create Map Reduce programs using the Apache Spark framework |
88 | | -- [ ] Acknowledge the TF-IDF statistic used in data mining, and how it can be computed using the MapReduce paradigm |
89 | | -- [ ] Create an implementation of the PageRank algorithm using the Apache Spark framework |
90 | 109 |
|
91 | | -✅ <i>Mini project 1 : Page Rank with Spark</i> |
| 110 | +✔️ Explain the MapReduce paradigm for analyzing data represented as key-value pairs </br> |
| 111 | +✔️ Apply the MapReduce paradigm to programs written using the Apache Hadoop framework </br> |
| 112 | +✔️ Create Map Reduce programs using the Apache Spark framework </br> |
| 113 | +✔️ Acknowledge the TF-IDF statistic used in data mining, and how it can be computed using the MapReduce paradigm </br> |
| 114 | +✔️ Create an implementation of the PageRank algorithm using the Apache Spark framework </br> |
| 115 | + |
| 116 | +☑ <i>Mini project 1 : Page Rank with Spark</i> |
92 | 117 |
|
93 | 118 | <b><u>Week 2 : Client-Server Programming</u></b> |
94 | | -- [ ] Generate distributed client-server applications using sockets |
95 | | -- [ ] Demonstrate different approaches to serialization and deserialization of data structures for distributed programming |
96 | | -- [ ] Recall the use of remote method invocations as a higher-level primitive for distributed programming (compared to sockets) |
97 | | -- [ ] Evaluate the use of multicast sockets as a generalization of sockets |
98 | | -- [ ] Employ distributed publish-subscribe applications using the Apache Kafka framework |
| 119 | + |
| 120 | +✔️ Generate distributed client-server applications using sockets </br> |
| 121 | +✔️ Demonstrate different approaches to serialization and deserialization of data structures for distributed programming </br> |
| 122 | +✔️ Recall the use of remote method invocations as a higher-level primitive for distributed programming (compared to sockets) </br> |
| 123 | +✔️ Evaluate the use of multicast sockets as a generalization of sockets </br> |
| 124 | +✔️ Employ distributed publish-subscribe applications using the Apache Kafka framework </br> |
99 | 125 |
|
100 | 126 | ✅ <i>Mini project 2 : Filer Server</i> |
101 | 127 |
|
102 | 128 | <b><u>Week 3 : Message Passing</u></b> |
103 | | -- [ ] Create distributed applications using the Single Program Multiple Data (SPMD) model |
104 | | -- [ ] Create message-passing programs using point-to-point communication primitives in MPI |
105 | | -- [ ] Identify message ordering and deadlock properties of MPI programs |
106 | | -- [ ] Evaluate the advantages of non-blocking communication relative to standard blocking communication primitives |
107 | | -- [ ] Explain collective communication as a generalization of point-to-point communication |
108 | 129 |
|
109 | | -✅ <i>Mini project 3 : Matrix Multiply in MPI</i> |
| 130 | +✔️ Create distributed applications using the Single Program Multiple Data (SPMD) model </br> |
| 131 | +✔️ Create message-passing programs using point-to-point communication primitives in MPI </br> |
| 132 | +✔️ Identify message ordering and deadlock properties of MPI programs </br> |
| 133 | +✔️ Evaluate the advantages of non-blocking communication relative to standard blocking communication primitives </br> |
| 134 | +✔️ Explain collective communication as a generalization of point-to-point communication </br> |
| 135 | + |
| 136 | +☑ <i>Mini project 3 : Matrix Multiply in MPI</i> |
110 | 137 |
|
111 | 138 | <b><u>Week 4 : Combining Distribution and Multuthreading</u></b> |
112 | | -- [ ] Distinguish processes and threads as basic building blocks of parallel, concurrent, and distributed Java programs |
113 | | -- [ ] Create multithreaded servers in Java using threads and processes |
114 | | -- [ ] Demonstrate how multithreading can be combined with message-passing programming models like MPI |
115 | | -- [ ] Analyze how the actor model can be used for distributed programming |
116 | | -- [ ] Assess how the reactive programming model can be used for distrubted programming |
| 139 | + |
| 140 | +✔️ Distinguish processes and threads as basic building blocks of parallel, concurrent, and distributed Java programs </br> |
| 141 | +✔️ Create multithreaded servers in Java using threads and processes </br> |
| 142 | +✔️ Demonstrate how multithreading can be combined with message-passing programming models like MPI </br> |
| 143 | +✔️ Analyze how the actor model can be used for distributed programming </br> |
| 144 | +✔️ Assess how the reactive programming model can be used for distrubted programming </br> |
117 | 145 |
|
118 | 146 | ✅ <i>Mini project 4 : Multi-Threaded File Server</i> |
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