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 ...

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 ...

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, ...

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

Summary form only given. Parallel computing for high performance scientific applications gained widespread adoption and deployment about two decades ago. Computer systems based on shared memory and ...

The recent increase in interest on big data and data intensive computing makes it important for CS undergraduate students to receive education in Parallel and Distributed Computing. The increase in ...

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 ...

Each year, the Association for Computing Machinery honors a computer scientist for his or her contributions to the field. The prize, which comes with $250,000 thanks to Google and Intel, is named ...