Learn about common parallel algorithms and patterns in CUDA, such as data parallelism, task parallelism, map, scan, stencil, and reduce. Discover CUDA libraries and best practices.

In the era of Big Data, the computational demands of machine learning (ML) algorithms have grown exponentially, necessitating the development of efficient parallel computing techniques. This research ...

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on its own GPUs (graphics processing units).CUDA enables developers to speed up compute ...

Data is de-duplicated, error-corrected and randomised — so that the data sequence does not affect the learning process. It is split into “training” data (c 80 per cent) and “evaluation ...

Dask offers a variety of user interfaces, each with its own set of distributed computing parallel algorithms. Arrays built with parallel NumPy, Dataframes built with parallel pandas, and machine ...

PyTorch 1.10 is production ready, with a rich ecosystem of tools and libraries for deep learning, computer vision, natural language processing, and more. Here's how to get started with PyTorch.

Abstract: Hash tables are a fundamental data structure for effectively storing and accessing sparse data, with widespread usage in domains ranging from computer graphics to machine learning. This ...

Over at the SC17 Blog, Brian Ban begins his series of SC17 Session Previews with a look at a talk on High Performance Big Data. "Deep learning, using GPU clusters, is a clear example but many Machine ...

In this Invited Talk from SC17, Judy Qiu presents: Harp-DAAL – A Next Generation Platform for High Performance Machine Learning on HPC-Cloud.. Scientific discovery via advances in simulation and data ...