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 ...

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 ...

How a 1974 parallel programming model may solve problems with the threading approach, including racing and non-determinism. Submit. Subscribe. Home; ... This means multiple parts of the code execute ...

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 ...

Intel and Sun say that parallel programming is about to go mainstream. Close. ... Sun is hoping to do the same thing with its developer base, ... Learn the top AI data analytics software to use.

Students taking the course for COMP_SCI 368 credit will work on labs that utilize advanced parallel programming, data layout, and algorithm decomposition concepts. Students taking the course for ...

Under the stream processing paradigm, a data set is named a stream. You can think of it much like “file streams” provided by an OS's pipe function. Streams can be any isolated piece of data, such as a ...

In data-parallel programming, all code is executed on every processor in parallel by default. The most widely used standard set of extensions for data-parallel programming are those of High ...

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 ...