High Performance Fortran (HPF) was developed to support data parallel programming for single-instruction multiple-data (SIMD) and multiple-instruction multiple-data (MIMD) machines with distributed memory. The programmer is provided a familiar uniform logical address space and specifies the data distribution by directives. The compiler then exploits these directives to allocate arrays in the local memories, to assign computations to elementary processors, and to migrate data between processors when required. We show here that linear algebra is a powerful framework to encode HPF directives and to synthesize distributed code with space-efficient array allocation, tight loop bounds, and vectorized communications for INDEPENDENT loops. The generated code includes traditional optimizations such as guard elimination, message vectorization and aggregation, and overlap analysis. The systematic use of an affine framework makes it possible to prove the compilation scheme correct.

Corinne Ancourt

Fabien Coelho

Fran&#xc7;ois Irigoin

Ronan Keryell

A Linear Algebra Framework for Static High Performance Fortran Code Distribution

Problem topology is the key to efficient parallelization support for partially regular applications. Specifically, problem topology provides the information necessary for automatic data distribution and regular application optimization of a large class of partially regular applications. Problem topology is the connectivity of the problem. This research focuses on composite grid applications and strives to take advantage of their partial regularity in the parallelization and compilation process. Composite grid problems arise in important application areas, e.g., reactor and aerodynamic simulation. Related physical phenomena are inherently parallel and their simulations are computationally intensive. We present algorithms that automatically determine data distributions for composite grid problems. Our algorithm's alignment and distribution specifications may be used as input to a High Performance Fortran program to apply the mapping for execution of the simulation code. These algorithms eliminate the need for user-specified data distribution for this large class of complex topology problems. We test the algorithms using a number of topological descriptions from aerodynamic and water-cooled nuclear reactor simulations. Speedup-bound predictions with and without communication, based on the automatically generated distributions, indicate that significant speedups are possible using these algorithms.

Lorie M. Liebrock

Ken Kennedy

Automatic Data Distribution for Composite Grid Applications

Robert Schreiber

Piyush Mehrotra

High Performance Fortran Comes of Age: Guest Editors' Introduction

High Performance Fortran (HPF) is the first widely supported, efficient, and portable parallel programming language for shared and distributed memory systems. HPF is realized through a set of directive-based extensions to Fortran 90. It enables application developers and Fortran end-users to write compact, portable, and efficient software that will compile and execute on workstations, shared memory servers, clusters, traditional supercomputers, or massively parallel processors. This article describes a production-quality HPF compiler for a set of parallel machines. Compilation techniques such as data and computation distribution, communication generation, run-time support, and optimization issues are elaborated as the basis for an HPF compiler implementation on distributed memory machines. The performance of this compiler on benchmark programs demonstrates that high efficiency can be achieved executing HPF code on parallel architectures.

Zeki Bozkus

Larry Meadows

Steven Nakamoto

Vincent Schuster

Mark Young

PGHPF &#x2013; An Optimizing High Performance Fortran Compiler for Distributed Memory Machines

This article motivates the usage of graphics and visualization for efficient utilization of High Performance Fortran's (HPF's) data distribution facilities. It proposes a graphical toolkit consisting of exploratory and estimation tools which allow the programmer to navigate through complex distributions and to obtain graphical ratings with respect to load distribution and communication. The toolkit has been implemented in a mapping design and visualization tool which is coupled with a compilation system for the HPF predecessor Vienna Fortran. Since this language covers a superset of HPF's facilities, the tool may also be used for visualization of HPF data structures.

Rainer Koppler

Siegfried Grabner

Jens Volkert

Visualization of Distributed Data Structures for High Performance Fortran-Like Languages

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.

Eric De Sturler

Volker Strumpen

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

Applications that get their inputs from sensors are an important and often overlooked application domain for High Performance Fortran (HPF). Such sensor-based applications typically perform regular operations on dense arrays, and often have latency and through put requirements that can only be achieved with parallel machines. This article describes a study of sensor-based applications, including the fast Fourier transform, synthetic aperture radar imaging, narrowband tracking radar processing, multibaseline stereo imaging, and medical magnetic resonance imaging. The applications are written in a dialect of HPF developed at Carnegie Mellon, and are compiled by the Fx compiler for the Intel Paragon. The main results of the study are that (1) it is possible to realize good performance for realistic sensor-based applications written in HPF and (2) the performance of the applications is determined by the performance of three core operations: independent loops (i.e., loops with no dependences between iterations), reductions, and index permutations. The article discusses the implications for HPF implementations and introduces some simple tests that implementers and users can use to measure the efficiency of the loops, reductions, and index permutations generated by an HPF compiler.

David R. O'hallaron

Jon Webb

Jaspal Subhlok

Performance Issues in High Performance Fortran Implementations of Sensor-Based Applications

This article describes the main features and implementation of our automatic data distribution research tool. The tool (DDT) accepts programs written in Fortran 77 and generates High Performance Fortran (HPF) directives to map arrays onto the memories of the processors and parallelize loops, and executable statements to remap these arrays. DDT works by identifying a set of computational phases (procedures and loops). The algorithm builds a search space of candidate solutions for these phases which is explored looking for the combination that minimizes the overall cost; this cost includes data movement cost and computation cost. The movement cost reflects the cost of accessing remote data during the execution of a phase and the remapping costs that have to be paid in order to execute the phase with the selected mapping. The computation cost includes the cost of executing a phase in parallel according to the selected mapping and the owner computes rule. The tool supports interprocedural analysis and uses control flow information to identify how phases are sequenced during the execution of the application.

Eduard Ayguad&#xc9;

Jordi Garcia

Merc&#xc9; Giron&#xc8;s

M. Luz Grande

Jes&#xda;s Labarta

DDT: A Research Tool for Automatic Data Distribution in High Performance Fortran

To efficiently parallelize a scientific application with a data-parallel compiler requires certain structural properties in the source program, and conversely, the absence of others. A recent parallelization effort of ours reinforced this observation and motivated this correspondence. Specifically, we have transformed a Fortran 77 version of GROMOS, a popular dusty-deck program for molecular dynamics, into Fortran D, a data-parallel dialect of Fortran. During this transformation we have encountered a number of difficulties that probably are neither limited to this particular application nor do they seem likely to be addressed by improved compiler technology in the near future. Our experience with GROMOS suggests a number of points to keep in mind when developing software that may at some time in its life cycle be parallelized with a data-parallel compiler. This note presents some guidelines for engineering data-parallel applications that are compatible with Fortran D or High Performance Fortran compilers.

Terry W. Clark

Reinhard v. Hanxleden

Experiences in Data-Parallel Programming

We have developed a compilation system which extends High Performance Fortran (HPF) in various aspects. We support the parallelization of well-structured problems with loop distribution and alignment directives similar to HPF's data distribution directives. Such directives give both additional control to the user and simplify the compilation process. For the support of unstructured problems, we provide directives for dynamic data distribution through user-defined mappings. The compiler also allows integration of message-passing interface (MPI) primitives. The system is part of a complete programming environment which also comprises a parallel debugger and a performance monitor and analyzer. After an overview of the compiler, we describe the language extensions and related compilation mechanisms in detail. Performance measurements demonstrate the compiler's applicability to a variety of application classes.

T. Kamachi

A. M&#xdc;ller

R. R&#xdc;hl

Y. Seo

K. Suehiro

M. Tamura

Kemari: A Portable High Performance Fortran System for Distributed Memory Parallel Processors

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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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