LPARX provides efficient run-time support for dynamic, non-uniform scientific calculations running on MIMD distributed memory architectures. It extends HPF's data decomposition model to provide support for dynamic, block irregular data structures. LPARX represents data decompositions as first-class objects and expresses data dependencies in a manner which is logically independent of data decomposition and problem dimension. LPARX applications are portable across a diversity of MIMD machines. We have implemented a number of applications in LPARX--- including a 3d particle calculation and 2d and 3d adaptive multigrid solvers---which could not have been efficiently implemented in HPF.

Run-time data redistribution can enhance algorithm performance in distributedmemory machines. Explicit redistribution of data can be performed between algorithm phases when a different data decomposition is expected to deliver increased performance for a subsequent phase of computation. Redistribution, however, represents increased program overhead as algorithm computation is discontinued while data are exchanged among processor memories. In this paper, we present a technique that minimizes the amount of data exchange for BLOCK to CYCLIC(c) (or vice-versa) redistributions of arbitrary number of dimensions. Preserving the semantics of the target (destination) distribution pattern, the technique manipulates the data to logical processor mapping of the target pattern. When implemented on an IBM SP-x, the mapping technique demonstrates redistribution performance improvements of approximately 40% over traditional data to processor mapping. Relative to the traditional mapping technique, the ...

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Data distribution has been one of the most important research topics in parallelizing compilers for distributed memory parallel computers. Good data distribution schema should consider both the computation load balance and the communication overhead. In this paper, we show that data re-distribution is necessary for executing a sequence of Do-loops if the communication cost due to performing this sequence of Do-loops is larger than a threshold value. Based on this observation, we can prune the searching space and derive efficient dynamic programming algorithms for determining effective data distribution schema to execute a sequence of Do-loops with a general structure. Experimental studies on a 32-node nCUBE-2 computer are also presented. Keywords: component alignment, data distribution, distributed memory computer, Do-loops, dynamic programming algorithm for data distribution, parallelizing compiler. 1 Introduction This paper is concerned with designing efficient algorithms for data ...

This paper evaluates the High-Performance Fortran (HPF) language as a candidate for implementing computational fluid dynamics (CFD) software on parallelarchitecture computer systems. The paper reviews major HPF language features and discusses general algorithmic issues common to broad classes of CFD codes. Broader application areas, such as those covered by the NAS parallel benchmarks are also reviewed for suitability for HPF implementation. HPF is shown to provide convenient language structures for implementing several widely-used CFD algorithms, including finite-difference and finite-volume solvers that use regular grids. Other CFD algorithms --- including multi-block, multi-grid and unstructured-mesh approaches --- are most conveniently expressed using extensions to the initial HPF language specification.

This paper motivates the usage of graphics and visualization for efficient utilization of HPF's data distribution facilities. It proposes a graphical tooltkit consisting of exploratory tools 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.

ions for Structured Adaptive Mesh Methods Scott R. Kohn Department of Chemistry and Biochemistry University of California, San Diego La Jolla, CA 92093-0340 Scott B. Baden Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 92093-0114 This work was supported by NSF contract ASC-9110793 and ONR contract N00014-93-1-0152. Intel Paragon and Cray C-90 time at the San Diego Supercomputer Center was provided by a UCSD School of Engineering Block Grant. Access to the IBM SP2 was provided by the Cornell Theory Center. Kohn and Baden, "Parallel Software Abstractions : : :" 2 Proposed running head: Parallel Software Abstractions for Adaptive Meshes Corresponding Author: Scott Kohn Department of Chemistry and Biochemistry University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0340 Tel: (619) 534-2026 Fax: (619) 534-7244 Email: skohn@chem.ucsd.edu Abstract We describe a software infrastructure for implementing portable structur...

We evaluate the High-Performance Fortran (HPF) language for the compact expression and efficient implementation of conjugate gradient iterative matrix-solvers on High Performance Computing and Communications(HPCC) platforms. We discuss the use of intrinsic functions, data distribution directives and explicitly parallel constructs to optimize performance by minimizing communications requirements in a portable manner. We focus on implementations using the existing HPF definitions but also discuss issues arising that may influence a revised definition for HPF-2. Some of the codes discussed are available on the World Wide Web at http://www.npac.syr.edu/hpfa/ alongwith other educational and discussion material related to applications in HPF. This work sponsored in part by ARPA 1 Introduction High Performance Fortran (HPF)[13] is a language definition agreed upon in 1993, and being widely adopted by systems suppliers as a mechanism for users to exploit parallel computation through the...

Data Decomposition involves the mapping of array elements to processors of a Distributed Memory Machine with the goal to obtain the best possible performance of a program by keeping communication costs low while exploiting parallelism. Data decomposition is typically divided into two subproblems: alignment and partitioning. Alignment deals with the relative allocation of different arrays. Partitioning is concerned with the actual distribution of the array elements among processors. Conflicting alignments may cause communication. This paper presents a technique for reducing communication by honoring multiple alignments and applies this approach in a distributed memory implementation of the strict functional language Sisal. Multiple alignment leads to recomputation and replication of array elements, which is safe in a functional, and hence side effect free, setting. We present performance improvements of up to 80% for one dimensional arrays, and up to 50% for two dimensional arrays, comp...

We evaluate the Fortran-90 and High-Performance Fortran (HPF) languages for the compact expression and efficient implementation of conjugate gradient iterative matrix-solvers on High Performance Computing and Communications(HPCC) platforms. We discuss the use of intrinsic functions, data distribution directives and explicitly parallel constructs to optimize performance by minimizing communications requirements in a portable manner. We also consider computational and data storage issues arising from variations of the basic conjugate gradient algorithm as well as surveying typical application problems that require an iterative solution of large matrix-formulated problems. Some of the codes discussed are available on the World Wide Web at http://www.npac.syr.edu/hpfa/ alongwith other educational and discussion material related to applications in HPF. Introduction High Performance Fortran (HPF)[13] is a language definition agreed upon in 1993, and being widely adopted by systems suppliers...