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.

We further develop system-aware parallel graph algorithms that enable runtime optimizations for faster and safer processing, hence bridging high-performance computing and big data analytics. Finally, ...

This is a characteristic of a multiple instruction stream, multiple data stream problem. Computing a single cell on a VAX 750 took about 300 bytes of input and took between a few seconds and ...

Parallel computing has been with us since the 1950s. Today, it lives in various forms, and its use in corporations can be widened through a number of new developments.

Computing is moving from serial processing, where each step has to be completed before the next is started, to massively parallel processing. The resulting leap in computer power will have a major ...

Modern computing has many foundational building blocks, including central processing units (CPUs), graphics processing units (GPUs) and data processing units (DPUs).. However, what almost all modern ...

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