Learn how to compare parallel and distributed computing based on problem characteristics, resource constraints, and performance goals. Find out which approach is best for your situation.

Learn about the most common applications of parallel and distributed computing in different domains, such as science, engineering, business, and entertainment.

The Parallel & Distributed Computing Lab (PDCL) conducts research at the intersection of high performance computing and big data processing. Our group works in the broad area of Parallel & Distributed ...

Welcome to my Parallel and Distributed Computing repository! This repository serves as a learning hub where I explore various fundamental and advanced concepts in parallelism, concurrency, and ...

In recognition of this seismic shift in the computing landscape, the ACM/IEEE CS Curriculum 2013 (CS 2013) contains a new knowledge area named Parallel and Distributed Computing (PDC), with 15 hours ...

This paper presents the establishment of cluster computing lab at a minority serving institution that aims to provide computing resources to support undergraduate computer science curriculum. We ...

There are some problems of accelerator simulation (for example, computer design of new accelerator projects, optimization of working machines and quasi-real time accelerator control) which demand high ...

The parallel and distributed computing group is composed of researchers who have interests in many areas, such as hardware systems, networking, programming languages, algorithms and applications. The ...

Assoc Prof Anwitaman Datta College of Computing and Data Science [email protected] Anwitaman is an Associate Professor in the College of Computing & Data Science at NTU Singapore. He currently ...

📘 README - MPI Program Collection This repository includes various MPI-based parallel computing programs, each illustrating key MPI techniques such as point-to-point communication, collective ...