High Performance Fortran Compilation Techniques for Parallelizing Scientific Codes

1998 ◽  
Author(s):  
V. Adve ◽  
Guohua Jin ◽  
J. Mellor-Crummey ◽  
Qing Yi
Keyword(s):  
High Performance ◽  
High Performance Fortran ◽  
Compilation Techniques

scholarly journals Scientific Programming with High Performance Fortran: A Case Study Using the xHPF Compiler

1997 ◽  
Vol 6 (1) ◽  
pp. 127-152
Author(s):  
Eric De Sturler ◽  
Volker Strumpen
Keyword(s):  
High Performance ◽  
Parallel Implementation ◽  
Gaussian Elimination ◽  
Primary Objective ◽  
Matrix Product ◽  
Dense Matrix ◽  
High Performance Fortran ◽  
Partial Pivoting ◽  
Intel Paragon

Recently, the first commercial High Performance Fortran (HPF) subset compilers have appeared. This article reports on our experiences with the xHPF compiler of Applied Parallel Research, version 1.2, for the Intel Paragon. At this stage, we do not expect very High Performance from our HPF programs, even though performance will eventually be of paramount importance for the acceptance of HPF. Instead, our primary objective is to study how to convert large Fortran 77 (F77) programs to HPF such that the compiler generates reasonably efficient parallel code. We report on a case study that identifies several problems when parallelizing code with HPF; most of these problems affect current HPF compiler technology in general, although some are specific for the xHPF compiler. We discuss our solutions from the perspective of the scientific programmer, and presenttiming results on the Intel Paragon. The case study comprises three programs of different complexity with respect to parallelization. We use the dense matrix-matrix product to show that the distribution of arrays and the order of nested loops significantly influence the performance of the parallel program. We use Gaussian elimination with partial pivoting to study the parallelization strategy of the compiler. There are various ways to structure this algorithm for a particular data distribution. This example shows how much effort may be demanded from the programmer to support the compiler in generating an efficient parallel implementation. Finally, we use a small application to show that the more complicated structure of a larger program may introduce problems for the parallelization, even though all subroutines of the application are easy to parallelize by themselves. The application consists of a finite volume discretization on a structured grid and a nested iterative solver. Our case study shows that it is possible to obtain reasonably efficient parallel programs with xHPF, although the compiler needs substantial support from the programmer.

HPFIT: A set of integrated tools for the parallelization of applications using High Performance Fortran. Part I: HPFIT and the TransTOOL environment

1997 ◽  
Vol 23 (1-2) ◽  
pp. 71-87 ◽  
Author(s):  
T. Brandes ◽  
S. Chaumette ◽  
M.C. Counilh ◽  
J. Roman ◽  
A. Darte ◽  
...  
Keyword(s):  
High Performance ◽  
High Performance Fortran

scholarly journals Opus: A Coordination Language for Multidisciplinary Applications

1997 ◽  
Vol 6 (4) ◽  
pp. 345-362 ◽  
Author(s):  
Barbara Chapman ◽  
Matthew Haines ◽  
Piyush Mehrotra ◽  
Hans Zima ◽  
John Van Rosendale
Keyword(s):  
High Performance ◽  
Data Repository ◽  
Task Parallelism ◽  
Central Concept ◽  
Coordination Language ◽  
Data Parallel ◽  
High Performance Fortran ◽  
Parallel Languages ◽  
Wide Range ◽  
And Task

Data parallel languages, such as High Performance Fortran, can be successfully applied to a wide range of numerical applications.However, many advanced scientific and engineering applications are multidisciplinary and heterogeneous in nature, and thus do not fit well into the data parallel paradigm. In this paper we present Opus, a language designed to fill this gap. The central concept of Opus is a mechanism called ShareD Abstractions (SDA). An SDA can be used as a computation server, i.e., a locus of computational activity, or as a data repository for sharing data between asynchronous tasks. SDAs can be internally data parallel, providing support for the integration of data and task parallelism as well as nested task parallelism. They can thus be used to express multidisciplinary applications in a natural and efficient way. In this paper we describe the features of the language through a series of examples and give an overview of the runtime support required to implement these concepts in parallel and distributed environments.

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