In this article we describe a series of algorithms ap- propriate for fine-grained parallel computers with general communications. We call these algorithms data parallel algorithms because their parallelism comes from simultaneous operations across large sets of data, rather than from multiple threads of control.

Jul 31, 2023 · The parallel algorithm model solves the large problem by dividing it into smaller parts and then solving each independent sub-task simultaneously by using its own approach. Each parallel algorithm model uses its own data partitioning and data processing strategy.

Data-Parallel Algorithms • Efficient algorithms require efficient building blocks • Five data-parallel building blocks • Map • Gather & Scatter • Reduce • Scan • Sort • Advanced data structures: • Sparse matrices • Hash tables • Task queues

Data parallelism entails partitioning a large data set among multiple processing nodes, with each one operating on an assigned chunk of data, before participating in the process of combining the partial results.

The basic concept of pipelined data-parallel algorithms is introduced by contrasting the algorithms with other styles of computation and by a simple example (a pipeline image distance transformation algorithm).

In order to solve a problem efficiently on a parallel machine, it is usually necessary to design an algorithm that specifies multiple operations on each step, i.e., a parallel algorithm. As an example, consider the problem of computing the sum of a sequence A of n numbers.

•How to program a parallel algorithm •In a simple, efficient, and elegant way •Still some engineering work to do. What are they? •More parallel algorithms •Scan, filter, pack, partition, sorting •Parallel thinking 41

This book advocates the concept of parallel processing and parallel algorithms. It aims to cover well one aspect of the analysis of parallel computers, which is the essence of the architectures.

(Guy Steele): The data-parallel programming style is an approach to organizing programs suitable for execution on massively parallel computers. see how to fit these building blocks together to make useful algorithms. All programs consist of code and data put together.

Data parallelism is a model of parallel computing in which the same set of instructions is applied to all the elements in a data set. A sampling of data parallel algorithms is presented. The examples are certainly not exhaustive, but address many issues involved in designing data parallel algorithms.