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 ...

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 ...

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 ...

In a new paper, researchers from Tencent AI Lab Seattle and the University of Maryland, College Park, present a reinforcement learning technique that enables large language models (LLMs) to utilize ...

Victor Eijkhout: I see several problems with the state of parallel programming. For starters, we have too many different programming models, such as threading, message passing, and SIMD or SIMT ...