Parallel Programming in Java

Coursera Courses ↗ · Coursera

Open Course on Coursera

Free to audit · Opens on Coursera

Parallel Programming in Java

Coursera · Intermediate ·📊 Data Analytics & Business Intelligence ·1mo ago
Skills: ML Pipelines70%
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
Watch on Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related AI Lessons

From Correlation to Causation
Learn to distinguish between correlation and causation in data science to make informed decisions
Medium · Data Science
Why Your Sales Forecast Is Always 20% Wrong (And How To Make It 12% Wrong)
Improve your sales forecasting accuracy by 8% using data science techniques and a structured approach
Medium · Data Science
Exploratory Data Analysis on Amazon Sales Data using Python
Learn to perform exploratory data analysis on Amazon sales data using Python with popular libraries like Pandas, Matplotlib, and Seaborn
Medium · Data Science
Exploratory Data Analysis on Amazon Sales Data using Python
Learn to perform exploratory data analysis on Amazon sales data using Python and popular libraries like Pandas, Matplotlib, and Seaborn
Medium · Python
Up next
Fixed Income Portfolio Management Analysis
Coursera
Watch →