Modern software takes computational speed for granted. But modern microprocessors can only speed up by increasing the number of cores. To take full advantage of multiple cores, software developers ...

Recently, I had the good fortune to present a class at the ACM Conference for Computer Science Educators (SIGCSE). While I definitely shared my enthusiasm for parallel programming, I had two key goals ...

NVIDIA’s CUDA is a general purpose parallel computing platform and programming model that accelerates deep learning and other compute-intensive apps by taking advantage of the parallel processing ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

Multicore chip designs, large symmetrical multiprocessing (SMP) systems, and clustering can bring many processors to bear on an application. But without proper software, they're simply large ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

One thing DDS systems do better than most parallel-programming environments is handle transient connections, because they support best-effort delivery. In many applications, it’s sufficient to retain ...

From your smartphone to your laptop, today’s tech devices glean their computing power from multi-core processors. Supercomputers contain thousands of cores, and within three to four years a computer ...

Computer chips have stopped getting faster: The regular performance improvements we've come to expect are now the result of chipmakers' adding more cores, or processing units, to their chips, rather ...