@@ -5,92 +5,114 @@ Parallel, Concurrent, and Distributed Programming in Java | Coursera
55## Parallel Programming in Java
66
77<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
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+ ✅ <i >Mini project 1 : Reciproncal-Array-Sum using the Java Fork/Join Framework</i >
1315
1416<b ><u >Week 2 : Functional Parallelism</u ></b >
1517
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
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
23+ 24+ ✅ <i >Mini project 2 : Analysing Student Statistics Using Java Parallel Streams</i >
2125
2226<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
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
32+ 33+ ✅ <i >Mini project 3 : Parallelizing Matrix-Matrix Multiply Using Loop Parallelism</i >
2834
2935<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
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
3541
42+ ✅ <i >Mini project 4 : Using Phasers to Optimize Data-Parallel Applications</i >
3643
3744## Concurrent Programming in Java
3845
3946<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
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
52+ 53+ ✅ <i >Mini project 1 : Locking and Synchronization</i >
4554
4655<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- 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
61+ 62+ ✅ <i >Mini project 2 : Global and Object-Based Isolation</i >
63+ 5364<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
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
70+ 71+ ✅ <i >Mini project 3 : Sieve of Eratosthenes Using Actor Parallelism</i >
5972
6073<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
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+ 80+ ✅ <i >Mini project 4 : Parallelization of Boruvka's Minimum Spanning Tree Algorithm</i >
6681
6782## Distributed Programming in Java
6883
6984<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
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+ 91+ ✅ <i >Mini project 1 : Page Rank with Spark</i >
7592
7693<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
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
99+ 100+ ✅ <i >Mini project 2 : Filer Server</i >
82101
83102<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
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+ 109+ ✅ <i >Mini project 3 : Matrix Multiply in MPI</i >
89110
90111<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
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
96117
118+ ✅ <i >Mini project 4 : Multi-Threaded File Server</i >
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