학술논문

Distributed memory implementation of elliptic partial differential equations in a dataparallel functional language
Document Type
Conference
Source
Programming Models for Massively Parallel Computers Programming models for massively parallel computers Programming Models for Massively Parallel Computers, 1995. :142-150 1995
Subject
Computing and Processing
Partial differential equations
Programming profession
System recovery
Message passing
Parallel processing
Data structures
Genetic expression
Partitioning algorithms
Educational programs
Load management
Language
Abstract
We show that the numerical solution of partial differential equations can be elegantly and efficiently addressed in a functional language. Two statistical numerical methods are considered. We discuss why current parallel imperative languages are difficult to use and why general (expression parallel) functional languages are not efficient enough. The key point of our approach is to offer "unique" arrays and some operations on them which allow to handle their elements in parallel, including operations which exchange the partitions of an array between the processors. These operations constitute a deadlock-free high-level way of communication.