The spelling of the term "parallel algorithm" is quite straightforward. The word "parallel" is spelled with three syllables: /ˈpær.ə.lel/. The first syllable begins with the /p/ consonant sound, followed by the short "a" vowel sound /æ/. The second syllable contains the long "a" vowel sound /ɑː/, and the final syllable features the /l/ consonant sound. Similarly, "algorithm" is also spelled with three syllables: /ˈæl.ɡə.rɪðm/. The initial syllable contains the short "a" vowel sound /æ/, followed by the /l/ consonant sound. The second syllable consists of the soft "g" sound /dʒ/ and the schwa sound /ə/. Finally, the last syllable features the /r/ consonant sound and the /ðm
A parallel algorithm is a computational method designed to solve problems simultaneously by dividing them into smaller tasks that can be executed concurrently on multiple processors or computing devices. It is specifically tailored to exploit the capabilities of parallel processing systems, which consist of multiple processing units capable of executing tasks in parallel.
Unlike sequential algorithms, which execute instructions in a step-by-step fashion on a single processor, parallel algorithms solve complex problems by simultaneously executing multiple subtasks on separate processors. This allows for increased speed and efficiency in solving large-scale computational problems.
Parallel algorithms employ various techniques to achieve parallelism, such as divide-and-conquer, pipelining, and data parallelism. They are typically designed to minimize the need for communication and synchronization between processors, as interprocessor communication can lead to performance bottlenecks in parallel processing systems.
Parallel algorithms can be categorized as either shared-memory or distributed-memory algorithms, depending on the nature of the underlying parallel architecture. In shared-memory systems, multiple processors share a common memory space, which allows for direct communication between processors. In distributed-memory systems, each processor has its own private memory and communicates with other processors through message passing.
Overall, the primary objective of parallel algorithms is to improve computing efficiency and enable scalable solutions to computationally intensive problems. By effectively utilizing the resources of parallel processing systems, parallel algorithms offer the potential for significant speedup and enhanced performance in a wide range of computational tasks.
The word "parallel" is derived from the Latin word "parallelus", which is itself derived from the Greek word "parallēlos". In Greek, "para" means "beside" or "alongside", and "allēlos" means "one another". "Parallēlos" was used to describe two lines that run alongside each other and never intersect.
The term "algorithm" has its roots in the Latin word "algorithmus", which comes from the surname of the Persian mathematician Muhammad ibn Musa al-Khwarizmi. Al-Khwarizmi was a mathematician and astronomer from the 9th century whose work heavily influenced the development of algorithms in mathematics and computation.