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.

Parallel and distributed computing offer many benefits, such as faster performance, lower cost, higher availability, and greater flexibility. However, they also have some limitations, such as ...

As a result, parallel (or high performance) computing was an elective area in the 2001 ACM/IEEE CS Curriculum, and relatively few universities offered undergraduate courses on the subject. However, ...

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 ...

Submission. Parallel and Distributed Software welcomes submissions of the following article types: Brief Research Report, Community Case Study, Conceptual Analysis, Correction, Data Report, Editorial, ...

Exploring the implementation of distributed and parallel sorting algorithms using the Message Passing Interface (MPI) into their parallel counterparts, thereby harnessing the computational power of ...

We propose to look at the evolution of ideas related to parallel systems, algorithms, and applications during the past three decades and then glimpse at the future. The journey starts with massively ...

The basics of distributed computing. Any time a workload is distributed between two or more computing devices or machines connected by some type of network, that’s distributed computing. There are a ...

In this video, Torsten Hoefler from ETH Zurich presents: Scientific Benchmarking of Parallel Computing Systems. "Measuring and reporting performance of parallel computers constitutes the basis for ...