In this slidecast, Torsten Hoefler from ETH Zurich presents: Data-Centric Parallel Programming. "To maintain performance portability in the future, it is imperative to decouple architecture-specific ...

Parallel programming is a more specific form of asynchronous programming -- running the same operation multiple times (i.e., in parallel). This can be running the same calculation across multiple CPU ...

Parallel programming exploits the capabilities of multicore systems by dividing computational tasks into concurrently executed subtasks. This approach is fundamental to maximising performance and ...

Reinders said. He added that he believes parallel programming may lead to new companies attacking problems with new approaches to compete with existing businesses. Exploiting Parallelism ...

With GPGPU, general-purpose applications are executed directly on the streaming processors of video cards. Under the stream processing paradigm, a data set is named a stream. You can think of it much ...

Two Google Fellows just published a paper in the latest issue of Communications of the ACM about MapReduce, the parallel programming model used to process more than 20 petabytes of data every day ...

This online research computing specialization introduces learners to the fundamentals of high performance and parallel computing and includes big data analysis, machine learning, parallel programming, ...

The Multicore Association, the processor standards body with a focus on multicore processor implementations, has announced the availability of an enhanced implementation of its Multicore Task ...

In the task-parallel model represented by OpenMP, the user specifies the distribution of iterations among processors and then the data travels to the computations. In data-parallel programming, the ...