An Overview of Trilinos

Total Page:16

File Type:pdf, Size:1020Kb

An Overview of Trilinos SAND REPORT SAND2003-2927 Unlimited Release Printed August 2003 An Overview of Trilinos Michael Heroux, Roscoe Bartlett, Vicki Howle Robert Hoekstra, Jonathan Hu, Tamara Kolda, Richard Lehoucq, Kevin Long, Roger Pawlowski, Eric Phipps, Andrew Salinger, Heidi Thornquist, Ray Tuminaro, James Willenbring and Alan Williams Sandia National Laboratories P.O. Box 5800 Albuquerque, NM 87185-1110 Prepared by Sandia National Laboratories Albuquerque, New Mexico 87185 and Livermore, California 94550 Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under Contract DE-AC04-94AL85000. Approved for public release; further dissemination unlimited. Issued by Sandia National Laboratories, operated for the United States Department of Energy by Sandia Corporation. NOTICE: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, make any warranty, express or implied, or assume any legal liability or re- sponsibility for the accuracy, completeness, or usefulness of any information, appara- tus, product, or process disclosed, or represent that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Govern- ment, any agency thereof, or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof, or any of their contractors. Printed in the United States of America. This report has been reproduced directly from the best available copy. Available to DOE and DOE contractors from U.S. Department of Energy Office of Scientific and Technical Information P.O. Box 62 Oak Ridge, TN 37831 Telephone: (865) 576-8401 Facsimile: (865) 576-5728 E-Mail: [email protected] Online ordering: http://www.doe.gov/bridge Available to the public from U.S. Department of Commerce National Technical Information Service 5285 Port Royal Rd Springfield, VA 22161 Telephone: (800) 553-6847 Facsimile: (703) 605-6900 E-Mail: [email protected] Online ordering: http://www.ntis.gov/ordering.htm T O EN F E TM N R E A R P G E Y D U ¥ ¥ N A I C T I E R D E S M T A ATES OF SAND2003-2927 Unlimited Release Printed August 2003 An Overview of Trilinos Michael Heroux, Jonathan Hu, Richard Lehoucq, Heidi Thornquist, Ray Tuminaro, and James Willenbring Computational Mathematics and Algorithms Department Roscoe Bartlett Optimization and Uncertainty Estimation Department Vicki Howle, Tamara Kolda, and Kevin Long Computational Sciences and Mathematics Research Department Robert Hoekstra, Roger Pawlowski, Eric Phipps, and Andrew Salinger Computational Sciences Department Alan Williams High Performance Computing and Networking Department Sandia National Laboratories P.O. Box 5800 Albuquerque, NM 87185-1110 Abstract The Trilinos Project is an effort to facilitate the design, development, inte- gration and ongoing support of mathematical software libraries. In particular, our goal is to develop parallel solver algorithms and libraries within an object- oriented software framework for the solution of large-scale, complex multi- physics engineering and scientific applications. Our emphasis is on developing 3 TrilinosTM Overview robust, scalable algorithms in a software framework, using abstract interfaces for flexible interoperability of components while providing a full-featured set of concrete classes that implement all abstract interfaces. Trilinos uses a two-level software structure designed around collections of packages. A Trilinos package is an integral unit usually developed by a small team of experts in a particular algorithms area such as algebraic precondition- ers, nonlinear solvers, etc. Packages exist underneath the Trilinos top level, which provides a common look-and-feel, including configuration, documenta- tion, licensing, and bug-tracking. Trilinos packages are primarily written in C++, but provide some C and Fortran user interface support. We provide an open architecture that allows easy integration with other solver packages and we deliver our software to the outside community via the Gnu Lesser General Public License (LGPL) [20]. This report provides an overview of Trilinos, discussing the objectives, history, current development and future plans of the project. 4 TrilinosTM Overview Acknowledgments The authors would like to acknowledge the support of the ASCI and LDRD pro- grams that funded development of Trilinos. 5 TrilinosTM Overview Intentionally Left Blank 6 CONTENTS TrilinosTM Overview Contents Nomenclature . 9 1 Background . 11 2 Introduction . 12 3 Trilinos Design Philosophy . 12 3.1 Services Provided by Trilinos . 14 4 Petra and TSF: Two Special Package Collections . 15 4.1 Epetra . 16 4.2 TSFCore and TSFExtended . 16 5 Common Tools Package: Teuchos . 17 6 Trilinos Package Interoperability Mechanisms . 19 7 Overview of Current Package Development . 21 7.1 The Petra Object Model . 21 Epetra: Essential Implementation of Petra Object Model . 21 Tpetra: Templated C++ Implementation of Petra Object Model . 21 Jpetra: Java Implementation of Petra Object Model . 22 7.2 TSF: The Trilinos Abstract Class Packages . 23 7.3 AztecOO: Concrete Preconditioned Iterative Solvers . 24 7.4 Belos: Generic Implementation of Krylov Methods . 25 7.5 Amesos: Object-oriented Interface to Direct Solvers . 25 7.6 Komplex: Solver Suite for Complex-valued Linear Systems . 26 7.7 Ifpack: Parallel Algebraic Preconditioners . 27 7.8 ML: Multi-level Preconditioner Package . 27 7.9 Meros . 28 7.10 NOX: Nonlinear Solver Package . 28 7.11 LOCA: Library of Continuation Algorithms . 29 7.12 Anasazi: Eigensolver package . 30 7.13 Future Packages . 31 8 Conclusions. 31 References. 37 Appendix A Brief Overview of Some Object-Oriented Concepts . 39 Object-oriented Programming . 39 7 TrilinosTM Overview FIGURES Some Key OOP Terms . 40 Figures 1 Current collection of Trilinos Packages . 13 8 FIGURES TrilinosTM Overview Nomenclature Trilinos The name of the project. Also a Greek term which, loosely translated means “a string of pearls,” meant to evoke an image that each Trilinos package is a pearl in its own right, but is even more valuable when combined with other packages. Package A self-contained collection of software in Trilinos focused on one primary class of numerical methods. Also a fundamental, integral unit in the Trilinos framework. new package A sample Trilinos package containing all of the infrastructure to install a new package into the Trilinos framework. Contains the basic directory structure, a collection of sample configuration and build files and a sample “Hello World” package. Also a website. Anasazi An extensible and interoperable framework for large-scale eigenvalue algo- rithms.The motivation for this framework is to provide a generic interface to a collection of algorithms for solving large-scale eigenvalue problems. AztecOO Linear solver package based on preconditioned Krylov methods. A follow-on to the Aztec solver package [48]. Supports all Aztec interfaces and function- ality, but also provides significant new functionality. Belos A Greek term meaning “arrow.” Belos is the next generation of iterative solvers. Belos solvers are written using “generic” programming techniques. In other words, Belos is written using TSF abstract interfaces and therefore has no explicit dependence on any concrete linear algebra library. Instead, Belos solvers can be used with any concrete linear algebra library that imple- ments the TSF abstract interfaces. Ifpack Object-oriented algebraic preconditioner, compatible with Epetra and AztecOO. Supports construction and use of parallel distributed memory precondition- ers such as overlapping Schwarz domain decomposition, Jacobi scaling and local Gauss-Seidel relaxations. Komplex Complex linear equation solver using equivalent real formulations [11], built on top of Epetra and AztecOO. LOCA Library of continuation algorithms. A package of scalable stability analysis algorithms (available as part of the NOX nonlinear solver package). When integrated into an application code, LOCA enables the tracking of solution branches as a function of system parameters and the direct tracking of bifur- cation points. 9 TrilinosTM Overview FIGURES Meros Segregated preconditioning package. Provides scalable block precondition- ing for problems that couple simultaneous solution variables such as Navier- Stokes problems. ML Algebraic multi-level preconditioner package. Provides scalable precondition- ing capabilities for a variety of problem classes. NOX A collection of nonlinear solvers, designed to be easily integrated into an application and used with many different linear solvers. Petra A Greek term meaning “foundation.” Trilinos has three Petra libraries: Epetra, Tpetra and Jpetra that provide basic classes for constructing and manipulat- ing matrix, graph and vector objects. Epetra is the current production version that is split into two packages, one core and one extensions. Epetra Current C++ production implementation of the Petra Object Model. The “E” in Epetra stands for “essential” implying that this version provides the most important capabilities
Recommended publications
  • Accelerating the LOBPCG Method on Gpus Using a Blocked Sparse Matrix Vector Product
    Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product Hartwig Anzt and Stanimire Tomov and Jack Dongarra Innovative Computing Lab University of Tennessee Knoxville, USA Email: [email protected], [email protected], [email protected] Abstract— the computing power of today’s supercomputers, often accel- erated by coprocessors like graphics processing units (GPUs), This paper presents a heterogeneous CPU-GPU algorithm design and optimized implementation for an entire sparse iter- becomes challenging. ative eigensolver – the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) – starting from low-level GPU While there exist numerous efforts to adapt iterative lin- data structures and kernels to the higher-level algorithmic choices ear solvers like Krylov subspace methods to coprocessor and overall heterogeneous design. Most notably, the eigensolver technology, sparse eigensolvers have so far remained out- leverages the high-performance of a new GPU kernel developed side the main focus. A possible explanation is that many for the simultaneous multiplication of a sparse matrix and a of those combine sparse and dense linear algebra routines, set of vectors (SpMM). This is a building block that serves which makes porting them to accelerators more difficult. Aside as a backbone for not only block-Krylov, but also for other from the power method, algorithms based on the Krylov methods relying on blocking for acceleration in general. The subspace idea are among the most commonly used general heterogeneous LOBPCG developed here reveals the potential of eigensolvers [1]. When targeting symmetric positive definite this type of eigensolver by highly optimizing all of its components, eigenvalue problems, the recently developed Locally Optimal and can be viewed as a benchmark for other SpMM-dependent applications.
    [Show full text]
  • Present and Future Leadership Computers at OLCF
    Present and Future Leadership Computers at OLCF Buddy Bland OLCF Project Director Presented at: SC’14 November 17-21, 2014 New Orleans ORNL is managed by UT-Battelle for the US Department of Energy Oak Ridge Leadership Computing Facility (OLCF) Mission: Deploy and operate the computational resources required to tackle global challenges Providing world-leading computational and data resources and specialized services for the most computationally intensive problems Providing stable hardware/software path of increasing scale to maximize productive applications development Providing the resources to investigate otherwise inaccessible systems at every scale: from galaxy formation to supernovae to earth systems to automobiles to nanomaterials With our partners, deliver transforming discoveries in materials, biology, climate, energy technologies, and basic science SC’14 Summit - Bland 2 Our Science requires that we continue to advance OLCF’s computational capability over the next decade on the roadmap to Exascale. Since clock-rate scaling ended in 2003, HPC Titan and beyond deliver hierarchical parallelism with performance has been achieved through increased very powerful nodes. MPI plus thread level parallelism parallelism. Jaguar scaled to 300,000 cores. through OpenACC or OpenMP plus vectors OLCF5: 5-10x Summit Summit: 5-10x Titan ~20 MW Titan: 27 PF Hybrid GPU/CPU Jaguar: 2.3 PF Hybrid GPU/CPU 10 MW Multi-core CPU 9 MW CORAL System 7 MW 2010 2012 2017 2022 3 SC’14 Summit - Bland Today’s Leadership System - Titan Hybrid CPU/GPU architecture, Hierarchical Parallelism Vendors: Cray™ / NVIDIA™ • 27 PF peak • 18,688 Compute nodes, each with – 1.45 TF peak – NVIDIA Kepler™ GPU - 1,311 GF • 6 GB GDDR5 memory – AMD Opteron™- 141 GF • 32 GB DDR3 memory – PCIe2 link between GPU and CPU • Cray Gemini 3-D Torus Interconnect • 32 PB / 1 TB/s Lustre® file system 4 SC’14 Summit - Bland Scientific Progress at all Scales Fusion Energy Liquid Crystal Film Stability A Princeton Plasma Physics Laboratory ORNL Postdoctoral fellow Trung Nguyen team led by C.S.
    [Show full text]
  • Exploring Capabilities Within Fortrilinos by Solving the 3D Burgers Equation
    Scientific Programming 20 (2012) 275–292 275 DOI 10.3233/SPR-2012-0353 IOS Press Exploring capabilities within ForTrilinos by solving the 3D Burgers equation Karla Morris a,∗, Damian W.I. Rouson a, M. Nicole Lemaster a and Salvatore Filippone b a Sandia National Laboratories, Livermore, CA, USA b Università di Roma “Tor Vergata”, Roma, Italy Abstract. We present the first three-dimensional, partial differential equation solver to be built atop the recently released, open-source ForTrilinos package (http://trilinos.sandia.gov/packages/fortrilinos). ForTrilinos currently provides portable, object- oriented Fortran 2003 interfaces to the C++ packages Epetra, AztecOO and Pliris in the Trilinos library and framework [ACM Trans. Math. Softw. 31(3) (2005), 397–423]. Epetra provides distributed matrix and vector storage and basic linear algebra cal- culations. Pliris provides direct solvers for dense linear systems. AztecOO provides iterative sparse linear solvers. We demon- strate how to build a parallel application that encapsulates the Message Passing Interface (MPI) without requiring the user to make direct calls to MPI except for startup and shutdown. The presented example demonstrates the level of effort required to set up a high-order, finite-difference solution on a Cartesian grid. The example employs an abstract data type (ADT) calculus [Sci. Program. 16(4) (2008), 329–339] that empowers programmers to write serial code that lower-level abstractions resolve into distributed-memory, parallel implementations. The ADT calculus uses compilable Fortran constructs that resemble the mathe- matical formulation of the partial differential equation of interest. Keywords: ForTrilinos, Trilinos, Fortran 2003/2008, object oriented programming 1. Introduction Burgers [4].
    [Show full text]
  • The Latest in Tpetra: Trilinos' Parallel Sparse Linear Algebra
    Photos placed in horizontal position with even amount of white space between photos The latest in Tpetra: Trilinos’ and header parallel sparse linear algebra Photos placed in horizontal position with even amount of white space Mark Hoemmen between photos and header Sandia National Laboratories 23 Apr 2019 SAND2019-4556 C (UUR) Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. Tpetra: parallel sparse linear algebra § “Parallel”: MPI + Kokkos § Tpetra implements § Sparse graphs & matrices, & dense vecs § Parallel kernels for solving Ax=b & Ax=λx § MPI communication & (re)distribution § Tpetra supports many linear solvers § Key Tpetra features § Can manage > 2 billion (10^9) unknowns § Can pick the type of values: § Real, complex, extra precision § Automatic differentiation § Types for stochastic PDE discretizations 2 Over 15 years of Tpetra 2004-5: Stage 1 rewrite of Tpetra to use Paul 2005-10: Mid 2010: Assemble Deprecate & Sexton ill-tested I start Kokkos 2.0 new Tpetra purge for starts research- work on Kokkos team; gather Trilinos 13 Tpetra ware Tpetra 2.0 requirements 2005 2007 2009 2011 2013 2015 2017 2019 Trilinos 10.0 Trilinos 11.0 Trilinos 12.0 Trilinos 13.0 ??? Trilinos 9.0 2006 2008 2010 2012 2014 2016 2018 2020 2008: Chris Baker Fix bugs; rewrite Stage 2: purge Improve GPU+MPI
    [Show full text]
  • Warthog: a MOOSE-Based Application for the Direct Code Coupling of BISON and PROTEUS (MS-15OR04010310)
    ORNL/TM-2015/532 Warthog: A MOOSE-Based Application for the Direct Code Coupling of BISON and PROTEUS (MS-15OR04010310) Alexander J. McCaskey Approved for public release. Stuart Slattery Distribution is unlimited. Jay Jay Billings September 2015 DOCUMENT AVAILABILITY Reports produced after January 1, 1996, are generally available free via US Department of Energy (DOE) SciTech Connect. Website: http://www.osti.gov/scitech/ Reports produced before January 1, 1996, may be purchased by members of the public from the following source: National Technical Information Service 5285 Port Royal Road Springfield, VA 22161 Telephone: 703-605-6000 (1-800-553-6847) TDD: 703-487-4639 Fax: 703-605-6900 E-mail: [email protected] Website: http://www.ntis.gov/help/ordermethods.aspx Reports are available to DOE employees, DOE contractors, Energy Technology Data Ex- change representatives, and International Nuclear Information System representatives from the following source: Office of Scientific and Technical Information PO Box 62 Oak Ridge, TN 37831 Telephone: 865-576-8401 Fax: 865-576-5728 E-mail: [email protected] Website: http://www.osti.gov/contact.html This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal lia- bility or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or rep- resents that its use would not infringe privately owned rights. Refer- ence herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not nec- essarily constitute or imply its endorsement, recommendation, or fa- voring by the United States Government or any agency thereof.
    [Show full text]
  • Geeng Started with Trilinos
    Geng Started with Trilinos Michael A. Heroux Photos placed in horizontal position Sandia National with even amount of white space Laboratories between photos and header USA Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND NO. 2011-XXXXP Outline - Some (Very) Basics - Overview of Trilinos: What it can do for you. - Trilinos Software Organization. - Overview of Packages. - Package Management. - Documentation, Building, Using Trilinos. 1 Online Resource § Trilinos Website: https://trilinos.org § Portal to Trilinos resources. § Documentation. § Mail lists. § Downloads. § WebTrilinos § Build & run Trilinos examples in your web browser! § Need username & password (will give these out later) § https://code.google.com/p/trilinos/wiki/TrilinosHandsOnTutorial § Example codes: https://code.google.com/p/trilinos/w/list 2 WHY USE MATHEMATICAL LIBRARIES? 3 § A farmer had chickens and pigs. There was a total of 60 heads and 200 feet. How many chickens and how many pigs did the farmer have? § Let x be the number of chickens, y be the number of pigs. § Then: x + y = 60 2x + 4y = 200 § From first equaon x = 60 – y, so replace x in second equaon: 2(60 – y) + 4y = 200 § Solve for y: 120 – 2y + 4y = 200 2y = 80 y = 40 § Solve for x: x = 60 – 40 = 20. § The farmer has 20 chickens and 40 pigs. 4 § A restaurant owner purchased one box of frozen chicken and another box of frozen pork for $60. Later the owner purchased 2 boxes of chicken and 4 boxes of pork for $200.
    [Show full text]
  • Block Locally Optimal Preconditioned Eigenvalue Xolvers BLOPEX
    Block Locally Optimal Preconditioned Eigenvalue Xolvers I.Lashuk, E.Ovtchinnikov, M.Argentati, A.Knyazev Block Locally Optimal Preconditioned Eigenvalue Xolvers BLOPEX Ilya Lashuk, Merico Argentati, Evgenii Ovtchinnikov, Andrew Knyazev (speaker) Department of Mathematical Sciences and Center for Computational Mathematics University of Colorado at Denver and Health Sciences Center Supported by the Lawrence Livermore National Laboratory and the National Science Foundation Center for Computational Mathematics, University of Colorado at Denver Block Locally Optimal Preconditioned Eigenvalue Xolvers I.Lashuk, E.Ovtchinnikov, M.Argentati, A.Knyazev Abstract Block Locally Optimal Preconditioned Eigenvalue Xolvers (BLOPEX) is a package, written in C, that at present includes only one eigenxolver, Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG). BLOPEX supports parallel computations through an abstract layer. BLOPEX is incorporated in the HYPRE package from LLNL and is availabe as an external block to the PETSc package from ANL as well as a stand-alone serial library. Center for Computational Mathematics, University of Colorado at Denver Block Locally Optimal Preconditioned Eigenvalue Xolvers I.Lashuk, E.Ovtchinnikov, M.Argentati, A.Knyazev Acknowledgements Supported by the Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (LLNL–CASC) and the National Science Foundation. We thank Rob Falgout, Charles Tong, Panayot Vassilevski, and other members of the Hypre Scalable Linear Solvers project team for their help and support. We thank Jose E. Roman, a member of SLEPc team, for writing the SLEPc interface to our Hypre LOBPCG solver. The PETSc team has been very helpful in adding our BLOPEX code as an external package to PETSc. Center for Computational Mathematics, University of Colorado at Denver Block Locally Optimal Preconditioned Eigenvalue Xolvers I.Lashuk, E.Ovtchinnikov, M.Argentati, A.Knyazev CONTENTS 1.
    [Show full text]
  • A New Overview of the Trilinos Project
    Scientific Programming 20 (2012) 83–88 83 DOI 10.3233/SPR-2012-0355 IOS Press A new overview of the Trilinos project Michael A. Heroux ∗ and James M. Willenbring Sandia National Laboratories, Albuquerque, NM, USA Abstract. Since An Overview of the Trilinos Project [ACM Trans. Math. Softw. 31(3) (2005), 397–423] was published in 2005, Trilinos has grown significantly. It now supports the development of a broad collection of libraries for scalable computational science and engineering applications, and a full-featured software infrastructure for rigorous lean/agile software engineering. This growth has created significant opportunities and challenges. This paper focuses on some of the most notable changes to the Trilinos project in the last few years. At the time of the writing of this article, the current release version of Trilinos was 10.12.2. Keywords: Software libraries, software frameworks 1. Introduction teams. Scientific libraries have a natural scope such that one or a few people work closely together on a The Trilinos project is a community of develop- cohesive collection of concerns. In 1999 the Acceler- ers and a collection of reusable software components ated Strategic Computing Initiative (ASCI) was fund- called packages. Trilinos provides a large and grow- ing many new algorithmic and software efforts. These ing collection of open-source software libraries for efforts included an explicit requirement to increase the scalable parallel computational science and engineer- level of formal software engineering practices and pro- ing applications, as well as a software infrastructure cesses, including efforts of small development teams. that supports a rigorous lean/agile software lifecycle Even before this time, there were numerous disjoint model [13].
    [Show full text]
  • The Deal.II Library, Version 9.3, 2021
    The deal.II Library, Version 9.3 Daniel Arndt1*, Wolfgang Bangerth2,3, Bruno Blais4, Marc Fehling2, Rene Gassmöller5, Timo Heister6, Luca Heltai7, Uwe Köcher8, Martin Kronbichler9,10, Matthias Maier11, Peter Munch9,12, Jean-Paul Pelteret13, Sebastian Proell9, Konrad Simon14, Bruno Turcksin1*, David Wells15, and Jiaqi Zhang6 1Scalable Algorithms and Coupled Physics Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd., TN 37831, USA. arndtd/[email protected] 2Department of Mathematics, Colorado State University, Fort Collins, CO 80523-1874, USA. bangerth/[email protected] 3Department of Geosciences, Colorado State University, Fort Collins, CO 80523, USA. 4Research Unit for Industrial Flows Processes (URPEI), Department of Chemical Engineering, Polytechnique Montréal, PO Box 6079, Stn Centre-Ville, Montréal, Québec, Canada, H3C 3A7. [email protected] 5Department of Geological Sciences, University of Florida, 1843 Stadium Road, Gainesville, FL, 32611, USA. [email protected] 6School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, 29634, USA heister/[email protected] 7SISSA, International School for Advanced Studies, Via Bonomea 265, 34136, Trieste, Italy. [email protected] 8Chair of Numerical Mathematics, Helmut-Schmidt-University, University of the Federal Armed Forces Hamburg, Holstenhofweg 85, 22043 Hamburg, Germany. [email protected] 9Institute for Computational Mechanics, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany. kronbichler/munch/[email protected] 10Department of Information Technology, Uppsala University, Box 337, 751 05 Uppsala, Sweden. [email protected] 11Department of Mathematics, Texas A&M University, 3368 TAMU, College Station, TX 77845, USA. [email protected] 12Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Max-Planck-Str.
    [Show full text]
  • Trilinos Configure, Build, Test, and Install Reference Guide
    Trilinos Configure, Build, Test, and Install Reference Guide Author: Roscoe A. Bartlett ([email protected]) Contact: [email protected] Abstract This document contains reference information on how to configure, build, test, and install Trilinos using the TriBITS CMake build sys- tem. The primary audience are users of Trilinos that need to con- figure and build the software. The secondary audience are actual developers of Trilinos. Contents 1 Introduction1 2 Trilinos-specific options1 2.1 Enabling float and complex Scalar types..............2 2.2 Enabling/disabling time monitors..................2 3 Getting set up to use CMake3 3.1 Installing a binary release of CMake [casual users]........3 3.2 Installing CMake from source [developers and experienced users]3 4 Getting CMake Help3 4.1 Finding CMake help at the website.................3 4.2 Building CMake help locally.....................3 5 Configuring (Makefile Generator)4 5.1 Setting up a build directory.....................4 5.2 Basic configuration..........................4 5.3 Selecting the list of packages to enable...............6 5.3.1 Determine the list of packages that can be enabled....6 5.3.2 Print package dependencies.................7 5.3.3 Enable a set of packages...................8 5.3.4 Enable to test all effects of changing a given package(s).8 5.3.5 Enable all packages with tests and examples........8 5.3.6 Disable a package and all its dependencies.........9 5.3.7 Remove all package enables in the cache..........9 5.4 Selecting compiler and linker options................ 10 1 5.4.1 Configuring to build with default debug or release compiler flags.............................
    [Show full text]
  • Sphynx: a Parallel Multi-GPU Graph Partitioner for Distributed-Memory Systems
    Sphynx: a parallel multi-GPU graph partitioner for distributed-memory systems Seher Acer1,∗, Erik G. Boman1,∗, Christian A. Glusa1, Sivasankaran Rajamanickam1 Center for Computing Research, Sandia National Laboratories, Albuquerque, NM, U.S.A. Abstract Graph partitioning has been an important tool to partition the work among several processors to minimize the communication cost and balance the workload. While accelerator-based supercomputers are emerging to be the standard, the use of graph partitioning becomes even more important as applications are rapidly moving to these architectures. However, there is no distributed-memory- parallel, multi-GPU graph partitioner available for applications. We developed a spectral graph partitioner, Sphynx, using the portable, accelerator-friendly stack of the Trilinos framework. In Sphynx, we allow using different preconditioners and exploit their unique advantages. We use Sphynx to systematically evaluate the various algorithmic choices in spectral partitioning with a focus on the GPU performance. We perform those evaluations on two distinct classes of graphs: regular (such as meshes, matrices from finite element methods) and irregular (such as social networks and web graphs), and show that different settings and preconditioners are needed for these graph classes. The experimental results on the Summit supercomputer show that Sphynx is the fastest alternative on irregular graphs in an application-friendly setting and obtains a partitioning quality close to ParMETIS on regular graphs. When compared to nvGRAPH on a single GPU, Sphynx is faster and obtains better balance and better quality partitions. Sphynx provides a good and robust partitioning method across a wide range of graphs for applications looking for a GPU-based partitioner.
    [Show full text]
  • Solving Sparse Linear Systems Part 2: Trilinos Solver Overview
    Solving Sparse Linear Systems Part 2: Trilinos Solver Overview Michael A. Heroux Sandia National LaBoratories http://www.users.csBsju.edu/~mheroux/HartreeTutorialPart2.pdF Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, 1 for the United States Department of Energy under contract DE-AC04-94AL85000. Outline § Sources oF sparse linear systems. § Sparse solver ecosystem. § Details aBout sparse data structures. § Overview oF Trilinos. § Overview oF Trilinos linear solvers. 2 If You Know Some Linear Algebra § If this intro material is not new, visit the Trilinos Tutorial Site: w https://github.com/trilinos/Trilinos_tutorial/wiki/TrilinosHandsOnTutorial w Lots of examples. w A Web portal for compiling, linking and running Trilinos examples. w Pointers to Trilinos User Group videos: Coverage of many Trilinos topics. § If you know Linux well: w Start installing Trilinos from the instructions. w Install these packages: • Kokkos, Tpetra, Epetra, EpetraExt, AztecOO, ML, Belos, Muelu, Ifpack, Ifpack2, Zoltan, Zoltan2, Amesos • Rob Allan has already done this on Tutorial systems, so he can assist you. § If you are into Docker: w Go https://hub.docker.com/r/sjdeal/webtrilinos/ for image. § If you want to start on the Epetra, AztecOO example: w Scroll down the tutorial page and start using and studying them. 3§ Explore… Setting up Linux Environment § Commands: w module avail # (look for Trilinos install) w module load intel # (get Intel compilers) w module load trilinos/12.2.1 w env # (look for path to Trilinos) § Copy and paste sample makefile from Hands-on page w http://trilinos.org/oldsite/Export_Makefile_example.txt w Get rid of references to libmyappLib w Define: MYAPP_TRILINOS_DIR=/gpfs/stfc/local/apps/intel/trilinos/12.2.1 § Try to make.
    [Show full text]