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 ...

Parallel and distributed computing are two approaches to solving complex problems using multiple processors or machines. They both aim to improve the performance, scalability, and reliability of ...

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 ...

The pre-computation of data cubes is critical to improving the response time of on-line analytical processing (OLAP) systems and can be instrumental in accelerating data mining tasks in large data ...

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