GPU computing, and specifically CUDA, is designed to process massive data parallelism with huge data sets efficiently. It’s exactly the embarrassingly parallel problems that exhibit massive data ...

It is crucial for Python to provide high-performance parallelism. This talk will expose to both data-scientists and library developers the current state of affairs and the recent advances for parallel ...

Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.

Get to know the basics of an HPC system. Users will learn how to work with common high performance computing systems they may encounter in future efforts. This includes navigating filesystems, working ...

Intel director James Reinders explains the difference between task and data parallelism ... with parallelism today. They are terms that you'll hear when you start working with parallel programming ...

When Calvin computer science professor Joel Adams launched Calvin’s first parallel computing course in the late ’90s ... “If you give students enough examples of how parallelism is used to solve ...

Parallel computing has long been a stumbling block for scaling big data and AI applications (not to mention HPC), and Ray provides a simplified path forward. “There’s a huge gap between what it takes ...

Modern computing has many foundational building blocks, including central processing units (CPUs), graphics processing units (GPUs) and data processing units (DPUs). However, what almost all modern ...