Intel has announced major updates for its C++ and Fortran tools, updates that are aimed at making it easier for programmers to exploit thread-level and data-level parallelism in multicore processors.

TAKING VECTORIZATION FROM EXTREME TO MAINSTREAM Data-level parallelism, or vectorization, accelerates analytics exponentially by performing the same operation on different sets of data ...

Pipelining and superscalar te chniques both exploit fine-grain instruction-level parallelism — pipelining by temporal means and superscalar by spatial means. Vector processing, in contrast, exploits ...

CPUs also have a limited ability to exploit instruction-level parallelism based on CPU width and data dependencies. These CPU performance bottlenecks are real, pervasive, and not easily resolved.

(1) A project was undertaken to improve the performance of Hydro2D. A number of factors were considered, including the memory subsystem, thread-level parallelism, data-level parallelism and ...

Parallel Domain’s synthetic data platform consists of two modes: training and testing. When training, customers will describe high-level parameters — for example, highway driving with 50% rain ...

A very common method is to use a standard set of directives known as OpenMP, in which the user expresses which sections of the code are to be parallel via a pragma ... because there is no means for ...

enabling two-times 8-way VLIW and up to 14,000 bits of data-level parallelism. It incorporates an advanced, deep pipeline architecture enabling operating speeds of 1.8 GHz at a 7-nm process node ...

For multicore processors with a private Vector Coprocessor (VP) per core, VP resources may not be highly utilized due to limited Data-Level Parallelism (DLP) in applications. Also, under low VP ...