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A finite element code with a polycrystal plasticity model for simulating deformation processing of metals has been developed for parallel computers using High Performance Fortran (HPF). The conversion of the code from an original implementation on the Connection Machine systems using CM Fortran is described. The sections of the code requiring minimal inter-processor communication are easily parallelized, by changing only the syntax for specifying data layout. However, the solver routine based on the conjugate gradient method required additional modifications, which are discussed in detail. The performance of the code on a massively parallel distributed-memory Intel PARAGON supercomputer is evaluated through timing statistics. Published by Elsevier Science Ltd.

Front tracking is a method for solving conservation laws in which the evolution of discontinuities is determined through the solution of Riemann problems. This method often does not require highly refined grids, and it has no numerical diffusion. We show the success of this method through a comparison of simulations of the Richtmyer-Meshkov instability, an unstable material interface, with experimental data. Good simulations of such instabilities are notoriously difficult, and we also demonstrate for the same physical problem that grid orientations have no effect on the numerical solution. We also present the first results of our three-dimensional front tracking code by simulating an important aspect of the computer chip manufacturing process: material deposition and etching. Our two- and three-dimensional front tracking code is parallelized for MIMD architectures and runs on our 128 node Intel Paragon.

Advances in parallel supercomputing now make possible molecular-based engineering and science calculations that will soon revolutionize many technologies, such as those involving polymers and those involving aqueous electrolytes. We have developed a suite of message-passing codes for classical molecular simulation of these industrially-important systems on the Intel Paragon and summarize some of the results. Nonequilibrium, multiple time step molecular dynamics lets us investigate the rheology of molecular fluids. Chain molecule Monte Carlo simulations in the Gibbs ensemble permit calculation of phase equilibrium of long-chain molecular systems. Complementary equilibrium molecular dynamics yields fundamental insight into the technologically-important problem of liquid-liquid phase separation in polymer blends. Parallel codes for quaternion dynamics using techniques for handling long-range Coulombic forces allow study of ion pairing in supercritical aqueous electrolyte solutions.

We present an overview of the locally self-consistent multiple scattering (LSMS) method. The method is based on real space multiple scattering theory, is naturally highly parallel, and has been implemented on Intel Paragon parallel platforms within the Center for Computational Sciences at Oak Ridge National Laboratory. O[ N ]-scaling is demonstrated for unit cells as large as 1000 atoms. The LSMS method can be extended to treat noncollinear magnetic states of materials. We applied the method to calculating the ground state magnetic structure of Fe 0.65 Ni 0.35 alloys. The result indicates the possible existence of noncollinear arrangements of magnetic moments in these alloys.

This paper considers key ideas in the design of out-of-core dense LU factorization routines. A left-looking variant of the LU factorization algorithm is shown to require less I/O to disk than the right-looking variant, and is used to develop a parallel, out-of-core implementation. This implementation makes use of a small library of parallel I/O routines, together with ScaLAPACK and PBLAS routines. Results for runs on an Intel Paragon are presented and interpreted using a simple performance model.

In this paper, we present parallel solvers for large linear systems arising from the finite-element discretization of three-dimensional groundwater flow problems. We have tested our parallel implementations on the Intel Paragon XP/S 150 supercomputer using up to 1024 parallel processors. Our solvers are based on multigrid and Krylov subspace methods. Our goal is to combine powerful algorithms and current generation high performance computers to enhance the capabilities of computer models for groundwater modeling. We show that multigrid can be a scalable algorithm on distributed memory machines. We demonstrate the effectiveness of parallel multigrid based solvers by solving problems requiring more than 64 million nodes in less than a minute. Our results show that multigrid as a stand alone solver works best for problems with smooth coefficients, but for rough coefficients it is best used as a preconditioner for a Krylov subspace method.

We describe a scalable parallel algorithm for numerical simulations of turbulent, radiative, magnetized, and reactive fluid + particle systems on message-passing, distributed-memory computers. Accurate simulation of such complex flows has applications in engine combustion, industrial pulverized coal burners, astrophysics, inertial confinement fusion, nuclear systems, and many other strategically and economically important areas. Our algorithm has been developed based on a widely-used combustion code KIVA-3, a plasma and radiation hydrodynamics code R-MHD, a classical particle dynamics code CMDT, and a discrete ordinates particle transport code TORT. The development is being done on the Intel Paragon with PVM and MPI extensions. We report high levels of parallel efficiency and scalability (up to 1024 nodes) for a baseline engine test case, using our current message-passing reactive and turbulent flow code. The three-dimensional extension of radiation magnetohydrodynamics component is still being worked at and we hope to report further progress in the future.

We present a parallel method for matrix multiplication on distributed-memory MIMD architectures based on Strassen's method. Our timing tests, performed on a 56-node Intel Paragon, demonstrate the realization of the potential of the Strassen's method with a complexity of 4.7 M2.807 at the system level rather than the node level at which several earlier works have been focused. The parallel efficiency is nearly perfect when the processor number is the power of 7. The parallelized Strassen's method seems always faster than the traditional matrix multiplication methods whose complexity is 2M3 coupled with the BMR method and the Ring method at the system level. The speed gain depends on matrix order M: 20% for M ≈ 1000 and more than 100% for M ≈ 5000.

A key to reducing the risks and costs associated with oil and gas exploration is the fast, accurate imaging of complex geologies, such as salt domes in the Gulf of Mexico and overthrust regions in U.S. onshore regions. Prestack depth migration generally yields the most accurate images, and one approach to this is to solve the scalar-wave equation using finite differences. Current industry computational capabilities are insufficient for the application of finite-difference, 3-D, prestack, depth-migration algorithms. A 3-D seismic data can be several terabytes in size, and the multiple runs necessary to refine the velocity model may take many years. The oil companies and seismic contractors need to perform complete velocity field refinements in weeks and single iterations overnight. High-performance computers and state-of-the-art algorithms and software are required to meet this need. As part of an ongoing ACTI project funded by the U.S. Department of Energy, we have developed a finite-difference, 3-D prestack, depth-migration code for the Intel Paragon. The goal of this work is to demonstrate that massively parallel computers (thousands of processors) can be used efficiently for seismic imaging, and that sufficient computing power exists (or soon will exist) to make finite-difference, prestack, depth migration practical for oil and gas exploration.

The paper shows the implementation of a 3D simulation code for turbulent flow and combustion processes in full-scale utility boilers on an Intel Paragon XP/S computer. For the portable parallelization, an explicit approach is chosen using a domain decomposition method for the static subdivision of the numerical grid together with the SPMD programming model. The measured speedup for the presented case using a coarse grid is good, although some numerical requirements restrict the implemented message passing to strongly synchronized communication. On the Paragon, the NX message passing library is used for the computations. Furthermore, MPI and PVM are applied and their pros and cons on this computer are described. In addition to the basic message passing techniques for local and global communication, other possibilities are investigated. Besides the applicability of the vectorizing capability of the compiler, the influence of the I/O performance during computations is demonstrated. The scalability of the parallel application is presented for a refined discretization.

We have designed and implemented a set of highly efficient and highly scalable algorithms for an unstructured computational package, the PSAS data simulation package, as demonstrated by detailed performance analysis of systematic runs up to 512 nodes of an Intel Paragon. The preconditioned Conjugate Gradient solver achieves a sustained 18 Gflops performance. Consequently, we achieve an unprecedented 100-fold reduction in time to solution on the Intel Paragon over a single head of a Cray C90. This not only exceeds the daily performance requirement of the Data Assimilation Office at NASA's Goddard Space Flight Center, but also makes it possible to explore much larger and challenging data assimilation problems which are unthinkable on a traditional computer platform such as the Cray C90.