\We are developing a software system called PASSION: Parallel And Scalable Software for Input-Output which provides software support for high performance parallel I/O. PASSION provides support at the language, compiler, runtime as well as file system level. PASSION provides runtime procedures for parallel access to files (read/write), as well as for out-of-core computations. These routines can either be used together with a compiler to translate out-of-core data parallel programs written in a language like HPF, or used directly by application programmers. A number of optimizations such as Two-Phase Access, Data Sieving, Data Prefetching and Data Reuse have been incorporated in the PASSION Runtime Library for improved performance. PASSION also provides an initial framework for runtime support for out-of-core irregular problems. The goal of the PASSION compiler is to automatically translate out- of-core data parallel programs to node programs for distributed memory machines, with calls to the PASSION Runtime Library. At the language level, PASSION suggests extensions to HPF for out-of-core programs. At the file system level, PASSION provides support for buffering and prefetching data from disks. A portable parallel file system is also being developed as part of this project, which can be used across homogeneous or heterogeneous networks of workstations. PASSION also provides support for integrating data and task parallelism using parallel I/O techniques. We have used PASSION to implement a number of out-of-core applications such as a Laplace's equation solver, 2D FFT, Matrix Multiplication, LU Decomposition, image processing applications as well as unstructured mesh kernels in molecular dynamics and computational fluid dynamics. We are currently in the process of using PASSION in applications in CFD (3D turbulent flows), molecular structure calculations, seismic computations, and earth and space science applications such as Four-Dimensional Data Assimilation. PASSION is currently available on the Intel Paragon, Touchstone Delta and iPSC/860. Efforts are underway to port it to the IBM SP-1 and SP-2 using the Vesta Parallel File System.

This paper describes two new ideas by which a High Performance Fortran compiler can deal with irregular computa-tions effectively. The first mechanism invokes a user specified mapping procedure via a set of proposed compiler directives. The directives allow use of program arrays to describe graph connec-tivity, spatial location of array elements, and computational load. The second mechanism is a conservative method for compiling irregular loops in which dependence arises only due to reduction operations. This mechanism in many cases enables a compiler to recognize that it is possible to reuse previously computed infor-mation from inspectors (e.g., communication schedules, loop it-eration partitions, and information that associates off-processor data copies with on-processor buffer locations). This paper also presents performance results for these mechanisms from a For-tran 90D compiler implementation.

In adaptive irregular problems the data arrays are accessed via indirection arrays, and data access patterns change during computation. Implementing such problems on distributed memory machines requires support for dynamic data partitioning, efficient preprocessing and fast data migration. This research presents efficient runtime primitives for such problems. This new set of primitives is part of the CHAOS library. It subsumes the previous PARTI library which targeted only static irregular problems. To demonstrate the efficacy of the runtime support, two real adaptive irregular applications have been parallelized using CHAOS primitives: a molecular dynamics code (CHARMM) and a particle-in-cell code (DSMC). The paper also proposes extensions to Fortran D which can allow compilers to generate more efficient code for adaptive problems. These language extensions have been implemented in the Syracuse Fortran 90D/HPF prototype compiler. The performance of the compiler parallelized codes is compared with the hand parallelized versions.

. We describe a spectral nested dissection algorithm for computing orderings appropriate for parallel factorization of sparse, symmetric matrices. The algorithm makes use of spectral properties of the Laplacian matrix associated with the given matrix to compute separators. We evaluate the quality of the spectral orderings with respect to several measures: fill, elimination tree height, height and weight balances of elimination trees, and clique tree heights. Spectral orderings compare quite favorably with commonly used orderings, outperforming them by a wide margin for some of these measures. These results are confirmed by computing a multifrontal numerical factorization with the different orderings on a Cray Y-MP with eight processors. Keywords. graph partitioning, graph spectra, Laplacian matrix, ordering algorithms, parallel orderings, parallel sparse Cholesky factorization, sparse matrix, vertex separator AMS(MOS) subject classifications. 65F50, 65F05, 65F15, 68R10 1. Introducti...

We are developing a compiler and runtime support system called PASSION: Parallel And Scalable Software for Input-Output, to translate out-of-core data-parallel programs to message passing node programs with explicit parallel I/O. This paper describes the design and implementation of the runtime system used in PASSION. We explain the basic model used by the compiler and runtime system. We describe the runtime routines for regular problems and provide an initial framework for runtime support for out-of-core irregular problems. Preliminary performance results are presented for 2D FFT and Laplace equation solver, and a kernel from a molecular dynamics code.

Parallel computing is maturing to become a viable approach for increasing CFD mesh resolution and reducing turnaround times -- many of todays commercial programs are already available for parallel architecture machines, and software tools exist to help with automated parallelization of irregular (unstructured) scientific problems. However, despite numerous impressive demonstrations of applications, it is still not exactly clear when and how these will really impact on the way we do turbomachinery CFD. The current paper does not even attempt to answer such a general question, but instead presents the authors experience and views obtained while developing parallel codes for both industry and in conjunction with their own research work. We give a brief review of events leading up to the present situation, and explain why parallel processing is the accepted route to follow. We then describe our parallel unstructured code; discuss practical issues relating to parallel performance; present some sample test case calculations; and nally consider a possible scenario regarding the role and requirements for similar computations in the future.