Both parallel programming and asynchronous programming are examples of concurrent programming, meaning more than one operation is running at the same time. Parallel programming is a more specific form ...

This document serves as a log of the progress and knowledge I gained while working on GPU programming and studying the PMPP (Parallel Programming and Optimization) book. Implemented vector addition by ...

The concept of reduction in parallel programming. Techniques for minimizing warp divergence and balancing workload across threads. How to use shared memory effectively in reduction operations.

We engage a finite difference method to solve this equation numerically. The performance of its parallel implementation is considered using Message Passing Interface (MPI), Compute Unified Device ...

In this talk you will learn how to speed up your Python programs using Nvidia's CUDA platform. PyTexas2019 The PyTexas Foundation was organized as a Texas non-profit corporation in 2014 and received ...

Every light switch in your house operates in parallel with the others. There’s a new edition of a book, titled Parallel Programming for FPGAs that explores that topic in depth and it is under ...

In high performance computing, machine learning, and a growing set of other application areas, accelerated, heterogeneous systems are becoming the norm. With that state come several parallel ...

TITLE: An Approach to Parallelization of SIFT Algorithm on GPUs for Real-Time Applications AUTHORS: Raghu Raj Prasanna Kumar, Suresh Muknahallipatna, John McInroy KEYWORDS: Scale Invariant Feature ...