As hardware designers turn toward multicore processors to improve computing power, software programmers must find new programming strategies that harness the power of parallel computing. One technique ...

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 ...

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 ...

One of the things to avoid when it comes to parallelism is working with raw threads. Abstraction offers a way around the issue, by avoiding the need to deal with low-level details of parallel systems, ...

Intel's James Reinders is an expert on parallelism; his most recent book covered the C++ extensions for parallelism provided by Intel Threaded Building Blocks. He's also the Director of Marketing and ...

Multi-core processors theoretically can run many threads of code in parallel, but some categories of operation currently bog down attempts to raise overall performance by parallelizing computing. Is ...

In this slidecast, Torsten Hoefler from ETH Zurich presents: Data-Centric Parallel Programming. The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the ...

Read a full transcript of the discussion. Find it on iTunes/iPod. Learn more. Sponsor: Greenplum. Internet-scale data collecting, swarms of sensors outputs, and content clouds from the mobile device ...