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Prof. Viktor Kuncak
and
Dr. Aleksandar Prokopec
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.
Read more
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.
Read more
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.
Learning Outcomes. By the end of this course you will be able to:
- reason about task and data parallel programs,
- express common algorithms in a functional style and solve them in parallel,
- competently microbenchmark parallel code,
- write programs that effectively use parallel collections to achieve performance
Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
Register for this course and see more details by visiting:
OpenCourser.com/course/34lotu/parallel
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What's inside
Syllabus
Parallel Programming
We motivate parallel programming and introduce the basic constructs for building parallel programs on JVM and Scala. Examples such as array norm and Monte Carlo computations illustrate these concepts. We show how to estimate work and depth of parallel programs as well as how to benchmark the implementations.
Read more
Syllabus
Parallel Programming
We motivate parallel programming and introduce the basic constructs for building parallel programs on JVM and Scala. Examples such as array norm and Monte Carlo computations illustrate these concepts. We show how to estimate work and depth of parallel programs as well as how to benchmark the implementations.
Read more
Basic Task Parallel Algorithms
We continue with examples of parallel algorithms by presenting a parallel merge sort. We then explain how operations such as map, reduce, and scan can be computed in parallel. We present associativity as the key condition enabling parallel implementation of reduce and scan.
Data-Parallelism
We show how data parallel operations enable the development of elegant data-parallel code in Scala. We give an overview of the parallel collections hierarchy, including the traits of splitters and combiners that complement iterators and builders from the sequential case.
Data Structures for Parallel Computing
We give a glimpse of the internals of data structures for parallel computing, which helps us understand what is happening under the hood of parallel collections.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops functional skills for data parallel programming, which are core skills for data scientists and engineers
Taught by experts in the field of parallel programming, ensuring high-quality instruction
Combines theory and practice, empowering students to understand and implement parallel programming concepts
Emphasizes hands-on learning, equipping students to apply concepts to real-world data analysis tasks
Provides benchmarks to assess performance, enabling students to optimize their code for speed and efficiency
Assumes proficiency in Java or C#, potentially limiting accessibility for those with minimal programming experience
Requires prior knowledge of functional programming concepts, which may be a barrier for some students
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Parallel programming (Scala 2 version) with these
activities:
Review Java or C# programming
Show steps
Refreshing your knowledge of Java or C# will strengthen your foundation for understanding parallel programming concepts.
Browse courses on
Java
Show steps
-
Go over the basics of Java or C# syntax and data structures.
-
Practice writing simple Java or C# programs.
-
Review common programming patterns and algorithms.
Review basic Java or C# concepts
Show steps
Having a strong mastery of Java or C# will help you understand the concepts in this course more easily.
Browse courses on
Java
Show steps
-
Review basic syntax and data types.
-
Practice writing simple Java or C# programs.
-
Run through some Java or C# tutorials.
Organize your course materials
Show steps
Organizing your course materials will help you stay on top of the content and make it easier to review later.
Show steps
-
Create a system for organizing your notes, assignments, quizzes, and exams.
-
File and label your materials in a logical and consistent manner.
-
Review your materials regularly to reinforce your understanding.
18 other activities
Expand to see all activities and additional details
Show all 21 activities
Review advanced topics in linear algebra
Show steps
Refresh foundational knowledge in linear algebra, which will be used heavily throughout the course.
Browse courses on
Linear Algebra
Show steps
-
Review vector spaces, matrices, and determinants.
-
Practice solving linear equations and systems of equations.
-
Recall concepts of eigenvalues, eigenvectors, and diagonalization.
Complete tutorials on parallel programming
Show steps
Guided tutorials allow you to learn about specific aspects of parallel programming and reinforce your understanding through practical exercises.
Browse courses on
Parallel Programming
Show steps
-
Find a reputable source for parallel programming tutorials.
-
Select a tutorial that covers a topic you are interested in learning more about.
-
Follow the tutorial step-by-step and complete all the exercises.
-
Experiment with the code and try to apply it to your own projects.
Join a study group to discuss parallel programming concepts
Show steps
Consolidate understanding by engaging in discussions and sharing knowledge with peers.
Browse courses on
Parallel Programming
Show steps
-
Find or create a study group with other course participants.
-
Meet regularly to discuss course material, share insights, and work on problems together.
-
Take turns presenting concepts and leading discussions.
Practice with parallel collections
Show steps
Practice using parallel collections to strengthen your understanding of their behavior and capabilities.
Browse courses on
Parallel Collections
Show steps
-
Choose a small dataset and iterate over it using parallel collections.
-
Apply different operations (e.g., map, filter, reduce) to the dataset using parallel collections.
-
Compare the performance of parallel collections to sequential collections.
Solve parallel programming problems on LeetCode
Show steps
Reinforce concepts of task and data parallelism by solving coding challenges.
Browse courses on
Parallel Programming
Show steps
-
Choose problems related to parallel programming.
-
Break down the problem into smaller tasks.
-
Implement solutions using parallel constructs.
-
Analyze performance and identify areas for optimization.
Practice parallel programming exercises
Show steps
Solving parallel programming exercises will help you develop a deeper understanding of the concepts and techniques covered in this course.
Show steps
-
Find online resources or textbooks with parallel programming exercises.
-
Work through the exercises, implementing solutions in Java or Scala.
-
Debug and optimize your solutions to improve performance.
Follow tutorials on advanced Scala parallel programming techniques
Show steps
Expand knowledge of Scala's parallel programming capabilities through guided tutorials.
Browse courses on
Scala
Show steps
-
Search for tutorials on specific parallel programming techniques.
-
Follow the tutorials step-by-step, implementing examples.
-
Experiment with different techniques and compare their performance.
Solve parallel programming problems
Show steps
Solving parallel programming problems will challenge your understanding and help you develop problem-solving skills.
Browse courses on
Parallel Programming
Show steps
-
Find a platform or website that offers parallel programming problems.
-
Select a problem that matches your skill level.
-
Design and implement a solution to the problem.
-
Test and debug your solution.
-
Analyze the performance and efficiency of your solution.
Participate in parallel programming competitions
Show steps
Challenge yourself and test your skills against others in a competitive setting.
Browse courses on
Parallel Programming
Show steps
-
Identify relevant parallel programming competitions.
-
Form a team or participate individually.
-
Develop and submit solutions to competition problems.
-
Analyze results and learn from both successes and failures.
Develop a data-parallel algorithm for a real-world problem
Show steps
Apply course concepts to solve a practical problem, strengthening understanding of data-parallel programming.
Browse courses on
Data Parallelism
Show steps
-
Identify a problem that benefits from data parallelism.
-
Design a data-parallel algorithm using Scala's parallel collections.
-
Implement and test the algorithm.
-
Evaluate performance and discuss potential improvements.
Write a blog post or article about a parallel programming topic
Show steps
Explaining parallel programming concepts to others will help you solidify your understanding and identify areas where you need further clarification.
Show steps
-
Choose a specific parallel programming topic to focus on.
-
Research the topic thoroughly and gather relevant information.
-
Write a clear and concise blog post or article explaining the topic.
-
Publish your blog post or article online and share it with others.
Write a blog post on a parallel programming topic
Show steps
Explaining a parallel programming topic to others will deepen your understanding and identify areas where you need more practice.
Browse courses on
Task Parallelism
Show steps
-
Choose a specific parallel programming topic to write about.
-
Research the topic and gather relevant information.
-
Write a draft of your blog post, explaining the topic clearly and concisely.
-
Revise and edit your blog post, ensuring it is well-written and easy to understand.
-
Publish your blog post and share it with others.
Contribute to an open-source parallel programming library
Show steps
Gain practical experience and contribute to the community by working on an open-source project.
Browse courses on
Parallel Programming
Show steps
-
Identify an open-source parallel programming library.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute.
-
Implement your contribution and submit a pull request.
Build a parallel programming project
Show steps
Building a project will allow you to apply your parallel programming skills to a real-world problem.
Browse courses on
Parallel Programming
Show steps
-
Identify a problem or opportunity that can be solved using parallel programming.
-
Design and implement a parallel solution.
-
Test and debug your solution.
-
Deploy and evaluate your solution.
-
Document your project and share it with others.
Mentor other students learning parallel programming
Show steps
Mentoring other students will help you reinforce your understanding of parallel programming concepts and develop your communication and teaching skills.
Show steps
-
Join or create a study group or online forum for parallel programming learners.
-
Offer to help other students with their questions and provide guidance.
-
Organize workshops or presentations to share your knowledge and insights with others.
Participate in a parallel programming competition
Show steps
Participating in a competition will push your skills to the limit and expose you to new challenges and approaches.
Browse courses on
Parallel Programming
Show steps
-
Find a parallel programming competition that aligns with your interests.
-
Form a team or participate individually.
-
Develop a strategy for solving the competition problems.
-
Implement your solution and submit it for evaluation.
-
Analyze the results and learn from your experience.
Participate in a parallel programming competition
Show steps
Participating in a parallel programming competition will challenge you to apply your skills and knowledge in a competitive environment, helping you identify areas for improvement and gain recognition for your abilities.
Show steps
-
Find a parallel programming competition that aligns with your interests and skill level.
-
Form a team or work individually.
-
Develop a strategy for solving the competition problems.
-
Implement your solutions and submit them for evaluation.
-
Analyze the results and learn from your experience.
Contribute to an open-source parallel programming project
Show steps
Contributing to an open-source parallel programming project will give you hands-on experience working on real-world parallel programming challenges and collaborating with others in the field.
Show steps
-
Identify an open-source parallel programming project that aligns with your interests and skill level.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute, such as fixing bugs, implementing new features, or improving documentation.
-
Submit a pull request with your contributions.
-
Work with the project maintainers to get your contributions merged.
Review Java or C# programming
Show steps
Refreshing your knowledge of Java or C# will strengthen your foundation for understanding parallel programming concepts.
Browse courses on
Java
Show steps
Show steps
Show steps
- Go over the basics of Java or C# syntax and data structures.
- Practice writing simple Java or C# programs.
- Review common programming patterns and algorithms.
Review basic Java or C# concepts
Show steps
Having a strong mastery of Java or C# will help you understand the concepts in this course more easily.
Browse courses on
Java
Show steps
Show steps
Show steps
- Review basic syntax and data types.
- Practice writing simple Java or C# programs.
- Run through some Java or C# tutorials.
Organize your course materials
Show steps
Organizing your course materials will help you stay on top of the content and make it easier to review later.
Show steps
Show steps
Show steps
- Create a system for organizing your notes, assignments, quizzes, and exams.
- File and label your materials in a logical and consistent manner.
- Review your materials regularly to reinforce your understanding.
18 other activities
Expand to see all activities and additional details
Show all 21 activities
Review advanced topics in linear algebra
Show steps
Refresh foundational knowledge in linear algebra, which will be used heavily throughout the course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review vector spaces, matrices, and determinants.
- Practice solving linear equations and systems of equations.
- Recall concepts of eigenvalues, eigenvectors, and diagonalization.
Complete tutorials on parallel programming
Show steps
Guided tutorials allow you to learn about specific aspects of parallel programming and reinforce your understanding through practical exercises.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Find a reputable source for parallel programming tutorials.
- Select a tutorial that covers a topic you are interested in learning more about.
- Follow the tutorial step-by-step and complete all the exercises.
- Experiment with the code and try to apply it to your own projects.
Join a study group to discuss parallel programming concepts
Show steps
Consolidate understanding by engaging in discussions and sharing knowledge with peers.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Find or create a study group with other course participants.
- Meet regularly to discuss course material, share insights, and work on problems together.
- Take turns presenting concepts and leading discussions.
Practice with parallel collections
Show steps
Practice using parallel collections to strengthen your understanding of their behavior and capabilities.
Browse courses on
Parallel Collections
Show steps
Show steps
Show steps
- Choose a small dataset and iterate over it using parallel collections.
- Apply different operations (e.g., map, filter, reduce) to the dataset using parallel collections.
- Compare the performance of parallel collections to sequential collections.
Solve parallel programming problems on LeetCode
Show steps
Reinforce concepts of task and data parallelism by solving coding challenges.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Choose problems related to parallel programming.
- Break down the problem into smaller tasks.
- Implement solutions using parallel constructs.
- Analyze performance and identify areas for optimization.
Practice parallel programming exercises
Show steps
Solving parallel programming exercises will help you develop a deeper understanding of the concepts and techniques covered in this course.
Show steps
Show steps
Show steps
- Find online resources or textbooks with parallel programming exercises.
- Work through the exercises, implementing solutions in Java or Scala.
- Debug and optimize your solutions to improve performance.
Follow tutorials on advanced Scala parallel programming techniques
Show steps
Expand knowledge of Scala's parallel programming capabilities through guided tutorials.
Browse courses on
Scala
Show steps
Show steps
Show steps
- Search for tutorials on specific parallel programming techniques.
- Follow the tutorials step-by-step, implementing examples.
- Experiment with different techniques and compare their performance.
Solve parallel programming problems
Show steps
Solving parallel programming problems will challenge your understanding and help you develop problem-solving skills.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Find a platform or website that offers parallel programming problems.
- Select a problem that matches your skill level.
- Design and implement a solution to the problem.
- Test and debug your solution.
- Analyze the performance and efficiency of your solution.
Participate in parallel programming competitions
Show steps
Challenge yourself and test your skills against others in a competitive setting.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Identify relevant parallel programming competitions.
- Form a team or participate individually.
- Develop and submit solutions to competition problems.
- Analyze results and learn from both successes and failures.
Develop a data-parallel algorithm for a real-world problem
Show steps
Apply course concepts to solve a practical problem, strengthening understanding of data-parallel programming.
Browse courses on
Data Parallelism
Show steps
Show steps
Show steps
- Identify a problem that benefits from data parallelism.
- Design a data-parallel algorithm using Scala's parallel collections.
- Implement and test the algorithm.
- Evaluate performance and discuss potential improvements.
Write a blog post or article about a parallel programming topic
Show steps
Explaining parallel programming concepts to others will help you solidify your understanding and identify areas where you need further clarification.
Show steps
Show steps
Show steps
- Choose a specific parallel programming topic to focus on.
- Research the topic thoroughly and gather relevant information.
- Write a clear and concise blog post or article explaining the topic.
- Publish your blog post or article online and share it with others.
Write a blog post on a parallel programming topic
Show steps
Explaining a parallel programming topic to others will deepen your understanding and identify areas where you need more practice.
Browse courses on
Task Parallelism
Show steps
Show steps
Show steps
- Choose a specific parallel programming topic to write about.
- Research the topic and gather relevant information.
- Write a draft of your blog post, explaining the topic clearly and concisely.
- Revise and edit your blog post, ensuring it is well-written and easy to understand.
- Publish your blog post and share it with others.
Contribute to an open-source parallel programming library
Show steps
Gain practical experience and contribute to the community by working on an open-source project.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Identify an open-source parallel programming library.
- Review the project's documentation and codebase.
- Identify an area where you can contribute.
- Implement your contribution and submit a pull request.
Build a parallel programming project
Show steps
Building a project will allow you to apply your parallel programming skills to a real-world problem.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Identify a problem or opportunity that can be solved using parallel programming.
- Design and implement a parallel solution.
- Test and debug your solution.
- Deploy and evaluate your solution.
- Document your project and share it with others.
Mentor other students learning parallel programming
Show steps
Mentoring other students will help you reinforce your understanding of parallel programming concepts and develop your communication and teaching skills.
Show steps
Show steps
Show steps
- Join or create a study group or online forum for parallel programming learners.
- Offer to help other students with their questions and provide guidance.
- Organize workshops or presentations to share your knowledge and insights with others.
Participate in a parallel programming competition
Show steps
Participating in a competition will push your skills to the limit and expose you to new challenges and approaches.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Find a parallel programming competition that aligns with your interests.
- Form a team or participate individually.
- Develop a strategy for solving the competition problems.
- Implement your solution and submit it for evaluation.
- Analyze the results and learn from your experience.
Participate in a parallel programming competition
Show steps
Participating in a parallel programming competition will challenge you to apply your skills and knowledge in a competitive environment, helping you identify areas for improvement and gain recognition for your abilities.
Show steps
Show steps
Show steps
- Find a parallel programming competition that aligns with your interests and skill level.
- Form a team or work individually.
- Develop a strategy for solving the competition problems.
- Implement your solutions and submit them for evaluation.
- Analyze the results and learn from your experience.
Contribute to an open-source parallel programming project
Show steps
Contributing to an open-source parallel programming project will give you hands-on experience working on real-world parallel programming challenges and collaborating with others in the field.
Show steps
Show steps
Show steps
- Identify an open-source parallel programming project that aligns with your interests and skill level.
- Review the project's documentation and codebase.
- Identify an area where you can contribute, such as fixing bugs, implementing new features, or improving documentation.
- Submit a pull request with your contributions.
- Work with the project maintainers to get your contributions merged.
Career center
Learners who complete Parallel programming (Scala 2 version) will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use advanced statistical knowledge and programming skills to analyze data and develop machine learning models. The Parallel Programming (Scala 2 version) course provides a strong foundation in parallel programming techniques, which are essential for building scalable and efficient data processing pipelines. By learning how to parallelize data-intensive tasks, Data Scientists can accelerate the development and deployment of complex machine learning models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The Parallel Programming (Scala 2 version) course provides a solid understanding of parallel programming principles and techniques, which are becoming increasingly important in modern software development. By mastering these concepts, Software Engineers can build high-performance and scalable software applications that can leverage multiple cores and processors effectively.
Big Data Engineer
Big Data Engineer
Big Data Engineers design and manage large-scale data processing systems. The Parallel Programming (Scala 2 version) course provides a foundation in parallel programming techniques, which are essential for handling and processing massive datasets efficiently. By learning how to parallelize data-intensive tasks, Big Data Engineers can build scalable and fault-tolerant systems that can handle the increasing volume and variety of data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models and algorithms. The Parallel Programming (Scala 2 version) course provides a solid foundation in parallel programming techniques, which are essential for training and deploying large-scale machine learning models. By mastering these concepts, Machine Learning Engineers can accelerate the development and deployment of complex machine learning solutions.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and patterns. The Parallel Programming (Scala 2 version) course provides a strong foundation in parallel programming techniques, which can be useful for handling and processing large datasets efficiently. By learning how to parallelize data-intensive tasks, Data Analysts can accelerate the analysis and delivery of insights from data.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud-based infrastructure and applications. The Parallel Programming (Scala 2 version) course provides a foundation in parallel programming techniques, which can be useful for optimizing the performance and scalability of cloud-based systems. By learning how to parallelize data-intensive tasks, Cloud Architects can build scalable and efficient cloud-based solutions.
DevOps Engineer
DevOps Engineer
DevOps Engineers bridge the gap between development and operations teams. The Parallel Programming (Scala 2 version) course provides a foundation in parallel programming techniques, which can be useful for optimizing the performance and scalability of software systems. By learning how to parallelize data-intensive tasks, DevOps Engineers can build scalable and efficient software delivery pipelines.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze financial data. The Parallel Programming (Scala 2 version) course provides a foundation in parallel programming techniques, which can be useful for handling and processing large financial datasets efficiently. By learning how to parallelize data-intensive tasks, Quantitative Analysts can accelerate the analysis and delivery of insights from financial data.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. The Parallel Programming (Scala 2 version) course provides a foundation in parallel programming techniques, which can be useful for analyzing and optimizing data-intensive business processes. By learning how to parallelize data-intensive tasks, Business Analysts can accelerate the delivery of insights and recommendations for business improvement.
Systems Analyst
Systems Analyst
Systems Analysts design and implement computer systems. The Parallel Programming (Scala 2 version) course provides a foundation in parallel programming techniques, which can be useful for designing and implementing scalable and efficient computer systems. By learning how to parallelize data-intensive tasks, Systems Analysts can build systems that can handle increasing workloads and data volumes.
Software Tester
Software Tester
Software Testers evaluate the quality and performance of software systems. The Parallel Programming (Scala 2 version) course provides a foundation in parallel programming techniques, which can be useful for testing the performance and scalability of software systems. By learning how to parallelize data-intensive tasks, Software Testers can accelerate the testing process and identify potential performance bottlenecks.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. The Parallel Programming (Scala 2 version) course may be useful for Technical Writers who need to understand and explain parallel programming concepts and techniques. By learning about parallel programming, Technical Writers can improve the accuracy and clarity of their documentation for software developers and users.
Product Manager
Product Manager
Product Managers define and manage the development and marketing of products. The Parallel Programming (Scala 2 version) course may be useful for Product Managers who need to understand the technical aspects of parallel programming and its potential impact on product design and development. By learning about parallel programming, Product Managers can make informed decisions about the use of parallel programming in their products.
Project Manager
Project Manager
Project Managers plan and execute projects. The Parallel Programming (Scala 2 version) course may be useful for Project Managers who need to understand the technical aspects of parallel programming and its potential impact on project timelines and resources. By learning about parallel programming, Project Managers can make informed decisions about the use of parallel programming in their projects.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns. The Parallel Programming (Scala 2 version) course is unlikely to be useful for Marketing Managers.
For more career information including salaries, visit:
OpenCourser.com/course/34lotu/parallel
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Parallel programming (Scala 2 version).
Provides a comprehensive overview of parallel computing systems. Covers topics such as parallel architectures, parallel programming languages, and performance optimization. Can help to gain a comprehensive understanding of the various aspects of parallel computing.
Provides a practical introduction to natural language processing. Covers topics such as text processing, machine learning, and deep learning. Can help to apply parallel programming techniques to natural language processing problems.
Provides a comprehensive overview of parallel algorithms. Covers topics such as parallel models, algorithm design, and analysis techniques. Can help to gain a deeper understanding of the theoretical foundations of parallel programming.
Provides a comprehensive overview of functional and reactive programming concepts and techniques. It useful resource for anyone who wants to learn more about functional and reactive programming or who wants to find solutions to specific problems.
Provides a comprehensive overview of deep learning with Python. Covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. Can help to apply parallel programming techniques to deep learning problems.
Provides a solid introduction to the fundamentals of parallel programming that can help to supplement this course. It covers a wide range of topics, from basic concepts to advanced techniques.
Provides a comprehensive overview of functional programming in Scala. Covers topics such as lambda expressions, higher-order functions, and lazy evaluation. Can help to strengthen the foundational knowledge of functional programming.
Save
Provides a comprehensive overview of high performance programming techniques in Scala. It useful resource for anyone who wants to learn more about high performance programming in Scala or who wants to find solutions to specific problems.
Save
Provides a comprehensive overview of Scala programming concepts and techniques. It useful resource for anyone who wants to learn more about Scala or who wants to find solutions to specific problems.
Provides a comprehensive overview of Scala programming concepts and techniques. It useful resource for anyone who wants to learn more about Scala or who wants to find solutions to specific problems.
Save
本书介绍了并发编程的基础知识和最佳实践。它对于希望学习并发编程或提高并发编程技能的读者来说是一个有价值的资源。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/34lotu/parallel
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Level
Intermediate
Via
Coursera
Institution
École Polytechnique Fédérale de Lausanne2
Instructors
Prof. Viktor Kuncak
Dr. Aleksandar Prokopec
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English
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Good to know
Develops functional skills for data parallel programming, which are core skills for data scientists and engineers
Taught by experts in the field of parallel programming, ensuring high-quality instruction
Combines theory and practice, empowering students to understand and implement parallel programming concepts
Emphasizes hands-on learning, equipping students to apply concepts to real-world data analysis tasks
Provides benchmarks to assess performance, enabling students to optimize their code for speed and efficiency
Assumes proficiency in Java or C#, potentially limiting accessibility for those with minimal programming experience
Requires prior knowledge of functional programming concepts, which may be a barrier for some students
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