Learn how to compare parallel and sequential programming based on their pros and cons, challenges, and examples in this article.

This is known as sequential programming, and it has largely been the accepted model of computer science instruction at both the university and K–12 levels, in contrast with parallel computing, a model ...

Parallel programming, and OpenACC, is used in high-performance computing in the fields of bioinformatics, quantum chemistry, astrophysics and more. “The model was made to ensure that scientists spend ...

Developing complex computational-intensive and data-intensive scientific applications requires effective utilization of the computational power of the available computing platforms including grids, ...

This Innovative Practice Full Paper presents BlocklyPar, a set of three tutorial games to move from sequential to parallel programming using a block-based visual language. Block-based tutorial games ...

The simulation's algorithm was implemented in both sequential and parallel programming environments. This involved leveraging OpenMP and CUDA to parallelize the computational process. Comprehensive ...

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 ...

In sequential computing many standard languages such as C or Pascal do a reasonable job of bridging this gap, but in parallel languages building such a bridge has been significantly more difficult.

High Performance Computing (HPC) and parallel programming techniques underpin many of today’s most demanding computational tasks, from complex scientific simulations to data-intensive analytics.