Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
Spezialisierung Parallel, Concurrent, and Distributed Programming in Java
Boost Your Programming Expertise with Parallelism. Learn the fundamentals of parallel, concurrent, and distributed programming.
von
Kompetenzen, die Sie erwerben
Kurse, die komplett online stattfinden
Flexibler Zeitplan
Stufe „Mittel“
Ca. 2 Monate zum Abschließen
Englisch
So funktioniert die Spezialisierung
Kurse absolvieren
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Praxisprojekt
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Zertifikat erwerben
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Es gibt 3 Kurse in dieser Spezialisierung
Parallel Programming in Java
This course teaches learners (industry professionals and students) the fundamental concepts of parallel programming in the context of Java 8. Parallel programming enables developers to use multicore computers to make their applications run faster by using multiple processors at the same time. By the end of this course, you will learn how to use popular parallel Java frameworks (such as ForkJoin, Stream, and Phaser) to write parallel programs for a wide range of multicore platforms including servers, desktops, or mobile devices, while also learning about their theoretical foundations including computation graphs, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism. Why take this course? • All computers are multicore computers, so it is important for you to learn how to extend your knowledge of sequential Java programming to multicore parallelism. • Java 7 and Java 8 have introduced new frameworks for parallelism (ForkJoin, Stream) that have significantly changed the paradigms for parallel programming since the early days of Java. • Each of the four modules in the course includes an assigned mini-project that will provide you with the necessary hands-on experience to use the concepts learned in the course on your own, after the course ends. • During the course, you will have online access to the instructor and the mentors to get individualized answers to your questions posted on forums. The desired learning outcomes of this course are as follows: • Theory of parallelism: computation graphs, work, span, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism • Task parallelism using Java’s ForkJoin framework • Functional parallelism using Java’s Future and Stream frameworks • Loop-level parallelism with extensions for barriers and iteration grouping (chunking) • Dataflow parallelism using the Phaser framework and data-driven tasks Mastery of these concepts will enable you to immediately apply them in the context of multicore Java programs, and will also provide the foundation for mastering other parallel programming systems that you may encounter in the future (e.g., C++11, OpenMP, .Net Task Parallel Library).
Concurrent Programming in Java
This course teaches learners (industry professionals and students) the fundamental concepts of concurrent programming in the context of Java 8. Concurrent programming enables developers to efficiently and correctly mediate the use of shared resources in parallel programs. By the end of this course, you will learn how to use basic concurrency constructs in Java such as threads, locks, critical sections, atomic variables, isolation, actors, optimistic concurrency and concurrent collections, as well as their theoretical foundations (e.g., progress guarantees, deadlock, livelock, starvation, linearizability). Why take this course? • It is important for you to be aware of the theoretical foundations of concurrency to avoid common but subtle programming errors. • Java 8 has modernized many of the concurrency constructs since the early days of threads and locks. • During the course, you will have online access to the instructor and mentors to get individualized answers to your questions posted on the forums. • Each of the four modules in the course includes an assigned mini-project that will provide you with the necessary hands-on experience to use the concepts learned in the course on your own, after the course ends. The desired learning outcomes of this course are as follows: • Concurrency theory: progress guarantees, deadlock, livelock, starvation, linearizability • Use of threads and structured/unstructured locks in Java • Atomic variables and isolation • Optimistic concurrency and concurrent collections in Java (e.g., concurrent queues, concurrent hashmaps) • Actor model in Java Mastery of these concepts will enable you to immediately apply them in the context of concurrent Java programs, and will also help you master other concurrent programming system that you may encounter in the future (e.g., POSIX threads, .NET threads).
Distributed Programming in Java
This course teaches learners (industry professionals and students) the fundamental concepts of Distributed Programming in the context of Java 8. Distributed programming enables developers to use multiple nodes in a data center to increase throughput and/or reduce latency of selected applications. By the end of this course, you will learn how to use popular distributed programming frameworks for Java programs, including Hadoop, Spark, Sockets, Remote Method Invocation (RMI), Multicast Sockets, Kafka, Message Passing Interface (MPI), as well as different approaches to combine distribution with multithreading. Why take this course? • All data center servers are organized as collections of distributed servers, and it is important for you to also learn how to use multiple servers for increased bandwidth and reduced latency. • In addition to learning specific frameworks for distributed programming, this course will teach you how to integrate multicore and distributed parallelism in a unified approach. • Each of the four modules in the course includes an assigned mini-project that will provide you with the necessary hands-on experience to use the concepts learned in the course on your own, after the course ends. • During the course, you will have online access to the instructor and the mentors to get individualized answers to your questions posted on forums. The desired learning outcomes of this course are as follows: • Distributed map-reduce programming in Java using the Hadoop and Spark frameworks • Client-server programming using Java's Socket and Remote Method Invocation (RMI) interfaces • Message-passing programming in Java using the Message Passing Interface (MPI) • Approaches to combine distribution with multithreading, including processes and threads, distributed actors, and reactive programming Mastery of these concepts will enable you to immediately apply them in the context of distributed Java programs, and will also provide the foundation for mastering other distributed programming frameworks that you may encounter in the future (e.g., in Scala or C++).
Dozent
Vivek Sarkar
ProfessorÜber Rice University
Häufig gestellte Fragen
Wie erfolgen Rückerstattungen?
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Ja! Um loszulegen, klicken Sie auf die Kurskarte, die Sie interessiert, und melden Sie sich an. Sie können sich anmelden und den Kurs absolvieren, um ein teilbares Zertifikat zu erwerben, oder Sie können als Gast teilnehmen, um die Kursmaterialien gratis einzusehen. Wenn Sie einen Kurs abonnieren, der Teil einer Spezialisierung ist, abonnieren Sie automatisch die gesamte Spezialisierung. Auf Ihrem Kursteilnehmer-Dashboard können Sie Ihren Fortschritt verfolgen.
Ist finanzielle Unterstützung möglich?
Kann ich kostenlos an diesem Kurs teilnehmen?
Findet dieser Kurs wirklich ausschließlich online statt? Muss ich zu irgendwelchen Sitzungen persönlich erscheinen?
Dieser Kurs findet ausschließlich online statt, Sie müssen also zu keiner Sitzung persönlich erscheinen. Sie können jederzeit und überall über das Netz oder Ihr Mobilgerät auf Ihre Vorträge, Lektüren und Aufgaben zugreifen.
Wie lange dauert es, die Spezialisierung abzuschließen?
There are 3 courses in this Specialization. Based on a weekly commitment of 4-8 hours, you should be able to complete the Specialization in 12 weeks.
What background knowledge is necessary?
The Specialization is targeted at an audience that is already familiar with sequential programming in Java, including a basic knowledge of Java 8 lambdas.
Do I need to take the courses in a specific order?
No, you can take the courses in this Specialization in any order.
Erhalte ich akademische Leistungspunkte für den Abschluss der Spezialisierung?
You will not earn university credit for completing the Specialization.
What will I be able to do upon completing the Specialization?
This course teaches industry professionals and students the fundamental concepts of parallel programming in the context of Java 8. Parallel programming enables developers to use multicore computers to make their applications run faster by using multiple processors at the same time. By the end of this course, you will learn how to use popular parallel Java frameworks such as ForkJoin and Stream to write parallel programs for a wide range of multicore platforms whether for servers, desktops, or mobile devices, while also learning about their theoretical foundations (e.g., deadlock freedom, data race freedom, determinism).
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