Algorithms exist for compiling Fortran D for MIMD distributed-memory machines, but are significantly restricted in the presence of procedure calls. This paper presents interprocedural analysis, optimization, and code generation algorithms for Fortran D that limit compilation to only one pass over each procedure. This is accomplished by collecting summary information after edits, then compiling procedures in reverse topological order to propagate necessary information. Delaying instantiation of the computation partition, communication, and dynamic data decomposition is key to enabling interprocedural optimization. Recompilation analysis preserves the benefits of separate compilation. Empirical results show that interprocedural optimization is crucial in achieving acceptable performance for a common application.

This paper presents Fortran D, a version of Fortran enhanced with data decomposition specifications. It is designed to support two fundamental stages of writing a data-parallel program: problem mapping using sophisticated array alignments, and machine mapping through a rich set of data distribution functions. We believe that Fortran D provides a simple machine-independent programming model for most numerical computations. We intend to evaluate its usefulness for both programmers and advanced compilers on a variety of parallel architectures.

An important problem facing numerous research projects on parallelizing compilers for distributed memory machines is that of automatically determining a suitable data partitioning scheme for a program. Most of the current projects leave this tedious problem almost entirely to the user. In this paper, we present a novel approach to the problem of automatic data partitioning. We introduce the notion of constraints on data distribution, and show how, based on performance considerations, a compiler identifies constraints to be imposed on the distribution of various data structures. These constraints are then combined by the compiler to obtain a complete and consistent picture of the data distribution scheme, one that offers good performance in terms of the overall execution time. We present results of a study we performed on Fortran programs taken from the Linpack and Eispack libraries and the Perfect Benchmarks to determine the applicability of our approach to real programs. The results a...

Massively parallel MIMD distributed-memory machines can provide enormous computation power. However, the difficulty of developing parallel programs for these machines has limited their accessibility. This paper presents compiler algorithms to automatically derive efficient message-passing programs based on data decompositions. Optimizations are presented to minimize load imbalance and communication costs for both loosely synchronous and pipelined loops. These techniques are employed in the compiler being developed at Rice University for Fortran D, a version of Fortran enhanced with data decomposition specifications. 1 Introduction It is widely recognized that parallel computing represents the only plausible way to continue to increase the computational power available to computational scientists and engineers. However, parallel computers are not likely to be widely successful until they are easy to program. A major component in the success of vector supercomputers is the ability of ...

CODE 2.0 is a graphical parallel programming system that targets the three goals of ease of use, portability, and production of efficient parallel code. Ease of use is provided by an integrated graphical/textual interface, a powerful dynamic model of parallel computation, and an integrated concept of program component reuse. Portability is approached by the declarative expression of synchronization and communication operators at a high level of abstraction in a manner which cleanly separates overall computation structure from the primitive sequential computations that make up a program. Execution efficiency is approached through a systematic class hierarchy that supports hierarchical translation refinement including special case recognition. This paper reports results obtained through experimental use of a prototype implementation of the CODE 2.0 system. CODE 2.0 represents a major conceptual advance over its predecessor systems (CODE 1.0 and CODE 1.2) in terms of the expressive power ...

The Fortran D compiler uses data decomposition specifications to automatically translate Fortran programs for execution on MIMD distributed-memory machines. This paper introduces and classifies a number of advanced optimizations needed to achieve acceptable performance; they are analyzed and empirically evaluated for stencil computations. Communication optimizations reduce communication overhead by decreasing the number of messages and hide communication overhead by overlapping the cost of remaining messages with local computation. Parallelism optimizations exploit parallel and pipelined computations, and may need to restructure the computation to increase parallelism. Profitability formulas are derived for each optimization. Empirical results show that exploiting parallelism for pipelined computations, reductions, and scans is vital. Message vectorization, collective communication, and efficient coarse-grain pipelining also significantly affect performance. Scalability of communicatio...

This paper addresses the issue of compiling concurrent loop nests in the presence of complicated array references and irregularly distributed arrays. Arrays accessed within loops may contain accesses that make it impossible to precisely determine the reference pattern at compile time. This paper proposes a run time support mechanism that is used e ectively by a compiler to generate e cient code in these situations. The compiler accepts as input aFortran 77 program enhanced with speci cations for distributing data, and outputs a message passing program that runs on the nodes of a distributed memory machine. The runtime support for the compiler consists of a library of primitives designed to support irregular patterns of distributed array accesses and irregularly distributed array partitions. Avariety of performance results on the Intel iPSC/860 are presented.

The success of large-scale parallel architectures is limited by the difficulty of developing machine-independent parallel programs. We have developed Fortran D, a version of Fortran extended with data decomposition specifications, to provide a portable data-parallel programming model. This paper presents the design of two key components of the Fortran D programming system: a prototype compiler and an environment to assist automatic data decomposition. The Fortran D compiler addresses program partitioning, communication generation and optimization, data decomposition analysis, run-time support for unstructured computations, and storage management. The Fortran D programming environment provides a static performance estimator and an automatic data partitioner. We believe that the Fortran D programming system will significantly ease the task of writing machine-independent data-parallel programs.

The ParaScope project is developing an integrated collection of tools to help scientific programmers implement correct and efficient parallel programs. The centerpiece of this collection is the ParaScope Editor, an intelligent interactive editor for parallel Fortran programs. The ParaScope Editor reveals to users potential hazards of a proposed parallelization in a program. It also provides a variety of powerful interactive program transformations that have been shown useful in converting programs to parallel form. In addition, the ParaScope Editor supports general user editing through a hybrid text and structure editing facility that incrementally analyzes the modified program for potential hazards. The ParaScope Editor is a new kind of program construction tool -- one that not only manages text, but also presents the user with information about the correctness of the parallel program under development. As such, it can support an exploratory programming style in which users get immediate feedback on their various strategies for parallelization.

This dissertation addresses a number of important issues related to interprocedural optimization. Interprocedural optimization is an integral component in a compilation system for high-performance computing. The importance of interprocedural optimization stems from two sources: it increases the context available to the optimizing compiler, and it enables programmers to use procedure calls without the concern of hurting execution time. While important, interprocedural optimization can introduce some significant compile-time costs. When interprocedural information is used to optimize a procedure, the procedure is then dependent on those interprocedural facts. Thus, even if the procedure is not edited, it may require recompilation due to changes in the interprocedural facts. In addition to these effects on recompilation, interprocedural information can also be expensive to compute. Furthermore, interprocedural optimizations can increase program size which can in turn increase compile tim...