The spelling of the word "PGAS" may seem strange to native English speakers, but it makes sense when looking at the IPA phonetic transcription. The letters represent the sounds found in the word: /p/ for the plosive sound in "pea," /ɡ/ for the voiced velar stop in "go," /æ/ for the short vowel sound in "cat," and /s/ for the unvoiced hissing "s" sound. While uncommon in everyday language, using the IPA can help explain why certain spellings may seem confusing.
PGAS stands for Partitioned Global Address Space. It is a programming model that aims to simplify the development of parallel programs for high-performance computing (HPC) systems. The PGAS model provides a shared memory abstraction with a global address space concept across a large number of individual processing elements or cores, allowing for efficient data sharing and communication.
In the PGAS model, the global address space is partitioned into different segments that can be accessed by different processing elements. Each segment represents a distinct part of the memory that is local to a particular processing element. This partitioning allows for efficient data locality, which can enhance the performance of parallel programs.
PGAS is particularly useful for parallel programming on large-scale HPC systems, as it provides a more natural programming model than traditional message passing interfaces. It enables developers to write code with shared memory semantics, allowing data to be accessed and updated by multiple processing elements without explicit and intricate communication patterns.
The concept of PGAS can be implemented using different programming languages, such as Unified Parallel C (UPC) and Co-Array Fortran (CAF). These languages extend the syntax of traditional programming languages with additional constructs for managing the global address space and coordination among processing elements.
Overall, PGAS is a programming model that simplifies the development of parallel programs by providing a shared memory abstraction with a global address space, enabling efficient data sharing and communication in large-scale HPC systems.