May 1, 2024
3 minute read
Data parallelism, a crucial concept in parallel programming, involves distributing computational tasks across multiple processors or cores to enhance efficiency. This approach is commonly employed in high-performance computing, where complex problems demand immense processing power, and in big data analytics, where massive datasets necessitate efficient processing.
Why Learn Data Parallelism?
Mastering data parallelism empowers learners and professionals alike with numerous advantages. It enables:
3o0s3y|
Find a path to becoming a Data Parallelism. Learn more at:
OpenCourser.com/topic/3o0s3y/data
Reading list
We've selected ten 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
Data Parallelism.
Provides a comprehensive overview of high-performance computing, including data parallelism and other parallel programming techniques. It is suitable for researchers and practitioners working in the field.
Covers parallel and distributed computing, including data parallelism. It provides a comprehensive overview of the field and discusses various parallel and distributed computing architectures.
Covers data parallelism in the context of high-performance computing using MPI, a message-passing interface standard. It is suitable for researchers and practitioners working in parallel programming.
Covers parallel programming for modern high-performance computing systems, including data parallelism. It provides a comprehensive overview of parallel programming techniques and their applications in various scientific domains.
Focuses on data parallelism in C++, providing practical guidance on how to write efficient and scalable parallel programs. It valuable resource for software engineers working with large datasets.
Focuses on data parallelism using OpenMP, a portable parallel programming model. It provides a comprehensive overview of OpenMP and its applications in various domains.
Covers concurrency and parallelism in Java, including data parallelism. It provides a comprehensive overview of Java concurrency features and best practices.
Covers data parallelism in C#, providing practical guidance on how to write efficient and scalable parallel programs using C# language features and libraries.
Introduces data parallelism using CUDA, a parallel computing platform for GPUs. It covers the fundamentals of CUDA programming and provides hands-on examples.
Provides a broad overview of parallel programming, including data parallelism. It covers various parallel programming models and their applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/3o0s3y/data
Save
