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 ...

This can be running the same calculation across multiple CPU cores or retrieving data across multiple inbound channels. In a lot of our programs, we run only one additional thing at a time and wait ...

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 ...

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 ...

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.

The MTAPI takes care of task scheduling and execution on embedded parallel systems. While the EMB2 provides generic building blocks for compute-intensive applications, such as image recognition, big ...

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, ...

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 ...