This manual describes hypre, a software library of high performance preconditioners and solvers for the solution of large, sparse linear systems of equations on massively parallel computers [FaJY2004]. The hypre library was created with the primary goal of providing users with advanced parallel preconditioners. The library features parallel multigrid solvers for both structured and unstructured grid problems. For ease of use, these solvers are accessed from the application code via hypre’s conceptual linear system interfaces [FaJY2005] (abbreviated to conceptual interfaces throughout much of this manual), which allow a variety of natural problem descriptions.
This introductory chapter provides an overview of the various features in hypre, discusses further sources of information on hypre, and offers suggestions on how to get started.
Overview of Features
Scalable preconditioners provide efficient solution on today’s and tomorrow’s systems: hypre contains several families of preconditioner algorithms focused on the scalable solution of very large sparse linear systems. (Note that small linear systems, systems that are solvable on a sequential computer, and dense systems are all better addressed by other libraries that are designed specifically for them.) hypre includes “grey box” algorithms that use more than just the matrix to solve certain classes of problems more efficiently than general-purpose libraries. This includes algorithms such as structured multigrid.
Suite of common iterative methods provides options for a spectrum of problems: hypre provides several of the most commonly used Krylov-based iterative methods to be used in conjunction with its scalable preconditioners. This includes methods for nonsymmetric systems such as GMRES and methods for symmetric matrices such as Conjugate Gradient.
Intuitive grid-centric interfaces obviate need for complicated data structures and provide access to advanced solvers: hypre has made a major step forward in usability from earlier generations of sparse linear solver libraries in that users do not have to learn complicated sparse matrix data structures. Instead, hypre does the work of building these data structures for the user through a variety of conceptual interfaces, each appropriate to different classes of users. These include stencil-based structured/semi-structured interfaces most appropriate for finite-difference applications; a finite-element based unstructured interface; and a linear-algebra based interface. Each conceptual interface provides access to several solvers without the need to write new interface code.
User options accommodate beginners through experts: hypre allows a spectrum of expertise to be applied by users. The beginning user can get up and running with a minimal amount of effort. More expert users can take further control of the solution process through various parameters.
Configuration options to suit your computing system: hypre allows a simple
and flexible installation on a wide variety of computing systems. Users can
tailor the installation to match their computing system. Options include debug
and optimized modes, the ability to change required libraries such as MPI and
BLAS, a sequential mode, and modes enabling threads for certain solvers. On
most systems, however, hypre can be built by simply typing
make, or by using CMake [CMakeWeb].
Interfaces in multiple languages provide greater flexibility for applications: hypre is written in C (with the exception of the FEI interface, which is written in C++) and provides an interface for Fortran users.
Getting More Information
This user’s manual consists of chapters describing each conceptual interface, a chapter detailing the various linear solver options available, detailed installation information, and the API reference. In addition to this manual, a number of other information sources for hypre are available.
Reference Manual: This is equivalent to Chapter API in this user manual, but it can also be built as a separate document. The reference manual comprehensively lists all of the interface and solver functions available in hypre. It is ideal for determining the various options available for a particular solver or for viewing the functions provided to describe a problem for a particular interface.
Example Problems: A suite of example problems is provided with the hypre installation. These examples reside in the
examplessubdirectory and demonstrate various features of the hypre library. Associated documentation may be accessed by viewing the
README.htmlfile in that same directory.
Papers, Presentations, etc.: Articles and presentations related to the hypre software library and the solvers available in the library are available from the hypre web page at http://www.llnl.gov/CASC/hypre/.
Mailing List: The mailing list
hypre-announcecan be subscribed to through the hypre web page at http://www.llnl.gov/CASC/hypre/. The development team uses this list to announce new releases of hypre. It cannot be posted to by users.
How to get started
As previously noted, on most systems hypre can be built by simply typing
configure followed by
make in the top-level source directory.
Alternatively, the CMake system [CMakeWeb] can be used, and is the best
approach for building hypre on Windows systems in particular. For more detailed
instructions, read the
INSTALL file provided with the hypre distribution or
the General Information section of this manual. Note the following requirements:
To run in parallel, hypre requires an installation of MPI.
Configuration of hypre with threads requires an implementation of OpenMP. Currently, only a subset of hypre is threaded.
The hypre library currently does not directly support complex-valued systems.
Choosing a conceptual interface
An important decision to make before writing any code is to choose an appropriate conceptual interface. These conceptual interfaces are intended to represent the way that applications developers naturally think of their linear problem and to provide natural interfaces for them to pass the data that defines their linear system into hypre. Essentially, these conceptual interfaces can be considered convenient utilities for helping a user build a matrix data structure for hypre solvers and preconditioners. The top row of Figure 1 illustrates a number of conceptual interfaces. Generally, the conceptual interfaces are denoted by different types of computational grids, but other application features might also be used, such as geometrical information. For example, applications that use structured grids (such as in the left-most interface in Figure 1) typically view their linear problems in terms of stencils and grids. On the other hand, applications that use unstructured grids and finite elements typically view their linear problems in terms of elements and element stiffness matrices. Finally, the right-most interface is the standard linear-algebraic (matrix rows/columns) way of viewing the linear problem.
The hypre library currently supports four conceptual interfaces, and typically the appropriate choice for a given problem is fairly obvious, e.g. a structured-grid interface is clearly inappropriate for an unstructured-grid application.
Structured-Grid System Interface (Struct): This interface is appropriate for applications whose grids consist of unions of logically rectangular grids with a fixed stencil pattern of nonzeros at each grid point. This interface supports only a single unknown per grid point. See Chapter Structured-Grid System Interface (Struct) for details.
Semi-Structured-Grid System Interface (SStruct): This interface is appropriate for applications whose grids are mostly structured, but with some unstructured features. Examples include block-structured grids, composite grids in structured adaptive mesh refinement (AMR) applications, and overset grids. This interface supports multiple unknowns per cell. See Chapter Semi-Structured-Grid System Interface (SStruct) for details.
Finite Element Interface (FEI): This is appropriate for users who form their linear systems from a finite element discretization. The interface mirrors typical finite element data structures, including element stiffness matrices. Though this interface is provided in hypre, its definition was determined elsewhere (please send email to Alan Williams email@example.com for more information). See Chapter Finite Element Interface for details.
Linear-Algebraic System Interface (IJ): This is the traditional linear-algebraic interface. It can be used as a last resort by users for whom the other grid-based interfaces are not appropriate. It requires more work on the user’s part, though still less than building parallel sparse data structures. General solvers and preconditioners are available through this interface, but not specialized solvers which need more information. Our experience is that users with legacy codes, in which they already have code for building matrices in particular formats, find the IJ interface relatively easy to use. See Chapter Linear-Algebraic System Interface (IJ) for details.
Generally, a user should choose the most specific interface that matches their application, because this will allow them to use specialized and more efficient solvers and preconditioners without losing access to more general solvers. For example, the second row of Figure Figure 1 is a set of linear solver algorithms. Each linear solver group requires different information from the user through the conceptual interfaces. So, the geometric multigrid algorithm (GMG) listed in the left-most box, for example, can only be used with the left-most conceptual interface. On the other hand, the ILU algorithm in the right-most box may be used with any conceptual interface. Matrix requirements for each solver and preconditioner are provided in Chapter Solvers and Preconditioners and in Chapter API. Your desired solver strategy may influence your choice of conceptual interface. A typical user will select a single Krylov method and a single preconditioner to solve their system.
The third row of Figure Figure 1 is a list of data layouts or matrix/vector storage schemes. The relationship between linear solver and storage scheme is similar to that of the conceptual interface and linear solver. Note that some of the interfaces in hypre currently only support one matrix/vector storage scheme choice. The conceptual interface, the desired solvers and preconditioners, and the matrix storage class must all be compatible.
Writing your code
As discussed in the previous section, the following decisions should be made before writing any code:
Choose a conceptual interface.
Choose your desired solver strategy.
Look up matrix requirements for each solver and preconditioner.
Choose a matrix storage class that is compatible with your solvers and preconditioners and your conceptual interface.
Once the previous decisions have been made, it is time to code your application to call hypre. At this point, reviewing the previously mentioned example codes provided with the hypre library may prove very helpful. The example codes demonstrate the following general structure of the application calls to hypre:
Build any necessary auxiliary structures for your chosen conceptual interface. This includes, e.g., the grid and stencil structures if you are using the structured-grid interface.
Build the matrix, solution vector, and right-hand-side vector through your chosen conceptual interface. Each conceptual interface provides a series of calls for entering information about your problem into hypre.
Build solvers and preconditioners and set solver parameters (optional). Some parameters like convergence tolerance are the same across solvers, while others are solver specific.
Call the solve function for the solver.
Retrieve desired information from solver. Depending on your application, there may be different things you may want to do with the solution vector. Also, performance information such as number of iterations is typically available, though it may differ from solver to solver.
The subsequent chapters of this User’s Manual provide the details needed to more fully understand the function of each conceptual interface and each solver. Remember that a comprehensive list of all available functions is provided in Chapter API, and the provided example codes may prove helpful as templates for your specific application.