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Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.

The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized ...

Huge volumes of data need near-supercomputer power to process and analyze it all. You can get there with the .NET Task Parallel Library. As Web and mobile applications face the challenge of quickly ...

Integrating distributed, in-memory computing with distributed caching can easily extend LINQ semantics to create important new capabilities for real-time analytics on fast-changing data. In the age of ...

Hadoop, an open source framework that enables distributed computing, has changed the way we deal with big data. Parallel processing with this set of tools can improve performance several times over.

Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.