This is a schematic showing data parallelism vs. model parallelism, as they relate to neural network training. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases ...

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 ...

The architecture eliminates the need to decompose large models for distributed computing to train: Push-button AI? The hottest trend in AI is the emergence of massive models such as Open AI’s GPT-3.

Data parallelism is an approach towards parallel processing that depends on being able to break up data between multiple compute units (which could be cores in a processor, processors in a computer, ...

As hardware designers turn toward multicore processors to improve computing power, software programmers must find new programming strategies that harness the power of parallel computing. One technique ...

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Parallel Domain, a startup developing a ...

In the task-parallel model represented by OpenMP, the user specifies the distribution of iterations among processors and then the data travels to the computations. In data-parallel programming, the ...

Artificial intelligence in its deep learning form is producing neural networks that will have trillions and trillions of neural weights, or parameters, and the increasing scale presents special ...

Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end It’s tempting to think of machine learning as a ...

For more on this topic see Using pipelining in multicore LabView and Using data parallelism in multicore LabView. Until recently, advances in computing hardware have provided significant increases in ...