Actualités

Distributed Collapsed Gibbs Sampling (CGS) in Latent Dirichlet Allocation (LDA) training usually prefers a “customized” design with sophisticated asynchronization support. However, with both algorithm ...
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 ...
Data Parallelization vs Model Parallelization. Data parallelism is used more often than model parallelism. As we know that in synchronous Distributed SGD, synchronizing the operations becomes a ...
There are many programming models available, such as shared memory, message passing, data parallel, task parallel, map-reduce, stream processing, and actor model.
Implementation of different programming models - PRAM, Shared memory, Message Passing for different algorithms PHW1 - Basic Naive Matrix, BlocK Matrix and KMeans Clustering Implementation Brief ...
The Dryad and DryadLINQ systems offer a new programming model for large scale data-parallel computing. They generalize previous execution environments such as SQL and MapReduce in three ways: by ...
The goal of this paper, is to present a new massively parallel virtual machine model, designed for parallel and distributed high performance computing on a distributed system. The proposed model ...
The performance of parallel distributed data management systems becomes increasingly important with the rise of Big Data. Parallel joins have been widely studied both in the parallel processing and ...
In recent years, the Massively Parallel Computation (MPC) model has gained significant attention. However, most distributed and parallel graph algorithms in the MPC model are designed for static ...