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.

For parallel programming in C++, we use a library, called PASL, that we have been developing over the past 5 years.The implementation of the library uses advanced scheduling techniques to run parallel ...

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.

The move to parallel computing represents a terrific opportunity to move from your current, internally siloed operations. You could leap to horizontal, customer-centric organizational groups, like ...

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 ...

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 ...

In the first report from last week’s PRACEdays15 conference in Dublin, Tom Wilkie considers why so much Exascale software will be open source and why engineers are not using parallel programs ...