For more on this topic see Using task parallelism in multicore LabView and Overcoming multicore programming challenges with LabVIEW. As hardware designers turn toward multicore processors to improve ...

Data parallelism is an approach towards parallel processing that depends on being able to break up data between multiple compute units (which could be cores in a processor, processors in a computer… ...

For embarrassingly parallel problems, for example digital tomography, an under-$10,000 Tesla personal supercomputer can beat a $5 million Sun CalcUA. CUDA makes the parallel programming tractable.

Week 1 : Introduction to parallel computing: motivation for parallel computing, options of parallel computing, economics of parallel computing, basic concepts of parallel algorithms. Introduction to ...

When Calvin computer science professor Joel Adams launched Calvin’s first parallel computing course in the late 90s, the field was “an esoteric elective kind of thing.” Supercomputers, the main ...

In this video, Torsten Hoefler from ETH Zurich presents: Scientific Benchmarking of Parallel Computing Systems. "Measuring and reporting performance of parallel computers constitutes the basis for ...

Parallel computing has long been a stumbling block for scaling big data and AI applications (not to mention HPC), and Ray provides a simplified path forward. “There’s a huge gap between what it takes ...

Space–time parallel computing: An approach that concurrently exploits parallelism in both spatial and temporal discretizations, thereby enhancing the overall efficiency of solving complex ...

Task Parallelism in LabVIEW The LabVIEW graphical programming paradigm makes parallel programming easy, even for novice users. Two separate tasks that are not dependent on one another for data run in ...