Computer science is approaching a crisis that some CS experts say could fuel a renaissance of ideas. Very soon, mainstream computers will need an easy-to-use parallel programming model to tap into ...

Learn some of the best practices for parallel programming in computer science, such as choosing the right model, balancing the workload, reducing overhead, optimizing memory, and testing performance.

Learn how parallel processing can speed up, scale up, and enhance your program's performance, and how to use different tools and techniques for parallel processing in different operating systems.

Microsoft initiative invests in parallel computing. news. May 18, 2010 3 mins. ... “Parallel programs are extremely difficult to write, test, and troubleshoot,” Muglia said.

Back in 1965, Intel cofounder Gordon Moore predicted that the semiconductor industry could double the number of transistors on a chip every 12 months (he later amended it to 24 months) for about the ...

This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory ...

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on its own GPUs (graphics processing units).CUDA enables developers to speed up compute ...

You can take specialized courses in object-orientation, computer security, intelligent systems, computer networks, advanced Internet, neural networks, computer music (digital music), parallel ...

SemiDefinite Programs (SDP) are an extension of Linear Programs to the Hilbert space, ... Parallel Computing for Large-scale Semidefinite Programs. Tweet; Research. Published: February 28, 2013.

Computational fluid dynamics (CFD) is one of the important application fields of high performance computing. Parallelization of CFD programs can significantly increase the speed of computation, but ...