For this, Google uses a column-oriented database, which it found is faster and much better suited than a row-oriented database. The relevant data (one feature per column) can be pulled into the ...

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 ...

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 ...

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 ...

Metal is an alternative to OpenGL for graphics processing, but for general data-parallel programming for GPUs it is an alternative to OpenCL and Cuda. This (simple) example shows how to use Metal with ...

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 ...

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.

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 ...

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 ...