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.

Data locality is critical to achieving high performance on large-scale parallel machines. Non-local data accesses result in communication that can greatly impact performance. Thus the mapping, or decomposition, of the computation and data onto the processors of a scalable parallel machine is a key issue in compiling programs for these architectures.

We present a unified approach to locality optimization that employs both data and control transformations. Data transformations include changing the array layout in memory. Control transformations involve changing the execution order of programs. We have developed new techniques for compiler optimizations for distributed shared-memory machines, although the same techniques can be used for sequential machines with a memory hierarchy. Our compiler optimizations are based on an algebraic representation of data mappings and a new data locality model. We present a pure data transformation algorithm and an algorithm unifying data and control transformations. While there has been much work on control transformations, the opportunities for data transformations have been largely neglected. In fact, data transformations have the advantage of being applicable to programs that cannot be optimized with control transformations. The unified algorithm, which performs data and control transformations s...

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...

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.

The PARADIGM compiler project provides an automated means to parallelize programs, written in a serial programming model, for efficient execution on distributed-memory multicomputers. In addition to performing traditional compiler optimizations, PARADIGM is unique in that it addresses many other issues within a unified platform: automatic data distribution, synthesis of high-level communication, communication optimizations, irregular computations, functional and data parallelism, and multithreaded execution. This paper describes the techniques used and provides experimental evidence of their effectiveness. 1 Introduction Distributed-memory massively parallel multicomputers can provide the high levels of performance required to solve the Grand Challenge computational science problems [16]. Distributed-memory multicomputers such as the Intel iPSC/860, the Intel Paragon, the IBM SP-1 and the Thinking Machines CM-5 offer significant advantages over shared-memory multiprocessors in terms...

This paper addresses the problem of partitioning data for distributed memory machines (multicomputers). In current day multicomputers, interprocessor communication is more time-consuming than instruction execution. If insufficient attention is paid to the data allocation problem, then the amount of time spent in interprocessor communication might be so high as to seriously undermine the benefits of parallelism. It is therefore worthwhile for a compiler to analyze patterns of data usage to determine allocation, in order to minimize interprocessor communication. We present a machineindependent analysis of communication-free partitions. We present a matrix notation to describe array accesses in fully parallel loops which lets us derive sufficient conditions for communication-free partitioning (decomposition) of arrays. In the case of a commonly occurring class of accesses, we present a problem formulation to minimize communication costs, when communication-free partitioning of arrays is not possible.

Because of the complexity and variety of parallel architectures, an efficient machine-independent parallel programming model is needed to make parallel computing truly usable for scientific programmers. We believe that Fortran D, a version of Fortran enhanced with data decomposition specifications, can provide such a programming model. This paper presents the design of a prototype Fortran D compiler for the iPSC/860, a MIMD distributed-memory machine. Issues addressed include data decomposition analysis, guard introduction, communications generation and optimization, program transformations, and storage assignment. A test suite of scientific programs will be used to evaluate the effectiveness of both the compiler technology and programming model for the Fortran D compiler.

: In scalable parallel machines, processors can make local memory accesses much faster than they can make remote memory accesses. In addition, when a number of remote accesses must be made, it is usually more efficient to use block transfers of data rather than to use many small messages. To run well on such machines, software must exploit these features. We believe it is too onerous for a programmer to do this by hand, so we have been exploring the use of restructuring compiler technology for this purpose. In this paper, we start with a language like HPF-FORTRAN with user-specified data distributionand develop a systematic loop transformation strategy called access normalization that restructures loop nests to exploit locality and block transfers. We demonstrate the power of our techniques using routines from the BLAS (Basic Linear Algebra Subprograms) library. An important feature of our approach is that we model loop transformations using invertible matrices and integer lattice theo...

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.