Hybrid memory is a parallel programming model that combines shared memory and distributed memory. This model is suitable for applications that have both high and low degrees of data locality ...

Learn how to compare parallel and distributed computing based on problem characteristics, resource constraints, and performance goals. Find out which approach is best for your situation.

5_Pipelining: An introduction to pipeline parallelism, using the torch.distributed.pipeline module. We'll walk through the steps of taking our single-GPU EuroSAT example and converting it to use ...

For this tool, we develop and implement a transformation and lighting model to visualize and react with the grid. Herein, we propose a hybrid (shared memory and distributed memory) parallelization ...

Myriad of machine learning (ML) algorithms refine model parameters iteratively. Existing synchronous data-parallel frameworks can accelerate training with convergence guarantees. However, the ...