Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

Learn how to use Python’s async functions, threads, and multiprocessing capabilities to juggle tasks and improve the responsiveness of your applications. If you program in Python, you have most likely ...

Intel director James Reinders explains the difference between task and data parallelism, and how there is a way around the limits imposed by Amdahl's Law... I'm James Reinders, and I'm going to cover ...

Parallelism used to be the domain of supercomputers working on weather simulations or plutonium decay. It is now part of the architecture of most SoCs. But just how efficient, effective and widespread ...

Parallel computing is an idea whose time has finally come, but not for the obvious reasons. Parallelism is a computer science concept that is older Moore’s Law. In fact, it first appeared in print in ...

‘Helix Parallelism’ can process millions of words and support 32x more concurrent users. It’s a breakthrough, but is it useful for enterprise? Have a question that needs to process an ...