3.1. Introduction

CVODE is part of a software family called SUNDIALS: SUite of Nonlinear and DIfferential/ALgebraic equation Solvers [81]. This suite consists of CVODE, ARKODE, KINSOL, and IDA, and variants of these with sensitivity analysis capabilities.

3.1.1. Historical Background

Fortran solvers for ODE initial value problems are widespread and heavily used. Two solvers that have been written at LLNL in the past are VODE [24] and VODPK [30]. VODE is a general purpose solver that includes methods for both stiff and nonstiff systems, and in the stiff case uses direct methods (full or banded) for the solution of the linear systems that arise at each implicit step. Externally, VODE is very similar to the well known solver LSODE [113]. VODPK is a variant of VODE that uses a preconditioned Krylov (iterative) method, namely GMRES, for the solution of the linear systems. VODPK is a powerful tool for large stiff systems because it combines established methods for stiff integration, nonlinear iteration, and Krylov (linear) iteration with a problem-specific treatment of the dominant source of stiffness, in the form of the user-supplied preconditioner matrix [25]. The capabilities of both VODE and VODPK have been combined in the C-language package CVODE [41].

At present, CVODE may utilize a variety of Krylov methods provided in SUNDIALS that can be used in conjunction with Newton iteration: these include the GMRES (Generalized Minimal RESidual) [122], FGMRES (Flexible Generalized Minimum RESidual) [121], Bi-CGStab (Bi-Conjugate Gradient Stabilized) [157], TFQMR (Transpose-Free Quasi-Minimal Residual) [64], and PCG (Preconditioned Conjugate Gradient) [76] linear iterative methods. As Krylov methods, these require almost no matrix storage for solving the Newton equations as compared to direct methods. However, the algorithms allow for a user-supplied preconditioner matrix, and for most problems preconditioning is essential for an efficient solution. For very large stiff ODE systems, the Krylov methods are preferable over direct linear solver methods, and are often the only feasible choice. Among the Krylov methods in SUNDIALS, we recommend GMRES as the best overall choice. However, users are encouraged to compare all options, especially if encountering convergence failures with GMRES. Bi-CGStab and TFQMR have an advantage in storage requirements, in that the number of workspace vectors they require is fixed, while that number for GMRES depends on the desired Krylov subspace size. FGMRES has an advantage in that it is designed to support preconditioners that vary between iterations (e.g. iterative methods). PCG exhibits rapid convergence and minimal workspace vectors, but only works for symmetric linear systems.

In the process of translating the VODE and VODPK algorithms into C, the overall CVODE organization has been changed considerably. One key feature of the CVODE organization is that the linear system solvers comprise a layer of code modules that is separated from the integration algorithm, allowing for easy modification and expansion of the linear solver array. A second key feature is a separate module devoted to vector operations; this facilitated the extension to multiprosessor environments with minimal impacts on the rest of the solver, resulting in PVODE [33], the parallel variant of CVODE.

Around 2002, the functionality of CVODE and PVODE were combined into one single code, simply called CVODE. Development of this version of CVODE was concurrent with a redesign of the vector operations module across the SUNDIALS suite. The key feature of the N_Vector module is that it is written in terms of abstract vector operations with the actual vector kernels attached by a particular implementation (such as serial or parallel) of N_Vector. This allows writing the SUNDIALS solvers in a manner independent of the actual N_Vector implementation (which can be user-supplied), as well as allowing more than one N_Vector module linked into an executable file. SUNDIALS (and thus CVODE) is supplied with a wide range of different N_Vector implementations, including: serial, MPI-parallel, both OpenMP and Pthreads thread-parallel N_Vector implementations, a Hypre parallel implementation, a PETSc implementation, and various GPU-enabled implementations.

3.1.2. Changes to SUNDIALS in release 7.7.0

New Features and Enhancements

The default number of stages for the SSP Runge-Kutta methods ARKODE_LSRK_SSP_S_2 and ARKODE_LSRK_SSP_S_3 in LSRKStep were changed from 10 and 9, respectively, to their minimum allowable values of 2 and 4. Users may revert to the previous values by calling LSRKStepSetNumSSPStages().

Added the optional function ARKodeInit() to ARKODE to enable data allocation before the first call to ARKodeEvolve() (but after all other optional input routines have been called), to support users who measure memory usage before beginning a simulation.

Added the function ARKodeGetStageIndex() that returns the index of the stage currently being processed, and the total number of stages in the method, for users who wish to compute auxiliary quantities in their IVP right-hand side functions during some stages and not others (e.g., in all but the first or last stage).

Added the functions ARKodeGetLastTime() and ARKodeGetLastState() to return the last successful time and state achieved by ARKODE, respectively.

ARKODE now allows users to supply functions that will be called before each internal time step attempt (ARKodeSetPreStepFn()), after each successful time step (ARKodeSetPostStepFn()), before right-hand side routines are called on an updated state (ARKodeSetPreRhsFn()), and/or once each internal step/stage is computed (ARKodeSetPostprocessStepFn()/ ARKodeSetPostprocessStageFn()). These are considered advanced functions, as they should treat the state vector as read-only, otherwise all theoretical guarantees of solution accuracy and stability will be lost. As a result of these new functions, the values of multiple ARKODE return codes (e.g., ARK_INTERP_FAIL) have been updated; users who key off of the named constants will not be affected, but users who rely on the values themselves should update their codes accordingly.

Note to users utilizing the previously undocumented ARKodeSetPostprocessStepFn() function, the supplied function is now called on the newly computed state vector for all step attempts not just successful steps. To obtain the previous behavior of only calling a function on successful steps, switch to using ARKodeSetPostStepFn().

Added SUNLogger_Set{Error,Warning,Info,Debug}File functions to allow setting logger output streams with a FILE*.

Updated the Kokkos N_Vector to support Kokkos 5.x versions.

Bug Fixes

Fixed a CMake bug where the SuperLU_MT interface would not be built and installed without setting the SUPERLUMT_WORKS option to TRUE.

Fixed the embedded coefficients for the ARKODE_TSITOURAS_7_4_5 Butcher table.

Fixed a bug in LSRKStep where an incorrect state vector could be passed to a user-supplied dominant eigenvalue function on the first step unless the output vector passed to ARKodeEvolve() contained the initial condition and when an eigenvalue estimate is requested on the first step in a subsequent call to ARKodeEvolve() unless the output vector passed contained the most recently returned solution.

Fixed a potential bug in LSRKStep’s ARKODE_LSRK_SSP_S_3 method, where a real number was used instead of an integer, potentially resulting in a rounding error.

Fixed a bug in MRIStep for estimating the first “slow” time step in an adaptive multirate calculation.

Fixed a bug in MRIStep when using a custom inner integrator that relies on the input state being the initial condition for the fast integration rather than retaining the result from the last inner integration or most recent reset call and the output vector passed to ARKodeEvolve() does not contain the initial condition on the first call or the last returned solution on subsequent calls.

Added a missing call to SUNNonlinSolSetup() in MRIStep when using an IMEX-MRI-SR method.

Fixed a bug in the ARKODE discrete adjoint checkpointing where an incorrect state would be stored on the first step if the output vector passed to ARKodeEvolve() did not contain the initial condition on the first call.

Removed extraneous copy of output vector when using ARKODE in ARK_ONE_STEP mode.

Removed an extraneous copy of the output vector in each step with SplittingStep.

Fixed a bug in logging output from ARKODE, where for some time stepping modules, the current “time” output in the logger was incorrect.

Fixed a bug where passing an empty string to SUNLogger_Set{Error,Warning,Info,Debug}Filename did not disable the corresponding logging stream Issue #844.

Deprecation Notices

The CVodeSetMonitorFn and CVodeSetMonitorFrequency functions have been deprecated and will be removed in the next major release.

Several CMake options have been deprecated in favor of namespaced versions prefixed with SUNDIALS_ to avoid naming collisions in applications that include SUNDIALS directly within their CMake builds. Additionally, a consistent naming convention (SUNDIALS_ENABLE) is now used for all boolean options. The table below lists the old CMake option names and the new replacements.

Old Option

New Option

BUILD_ARKODE

SUNDIALS_ENABLE_ARKODE

BUILD_CVODE

SUNDIALS_ENABLE_CVODE

BUILD_CVODES

SUNDIALS_ENABLE_CVODES

BUILD_IDA

SUNDIALS_ENABLE_IDA

BUILD_IDAS

SUNDIALS_ENABLE_IDAS

BUILD_KINSOL

SUNDIALS_ENABLE_KINSOL

ENABLE_MPI

SUNDIALS_ENABLE_MPI

ENABLE_OPENMP

SUNDIALS_ENABLE_OPENMP

ENABLE_OPENMP_DEVICE

SUNDIALS_ENABLE_OPENMP_DEVICE

OPENMP_DEVICE_WORKS

SUNDIALS_ENABLE_OPENMP_DEVICE_CHECKS

ENABLE_PTHREAD

SUNDIALS_ENABLE_PTHREAD

ENABLE_CUDA

SUNDIALS_ENABLE_CUDA

ENABLE_HIP

SUNDIALS_ENABLE_HIP

ENABLE_SYCL

SUNDIALS_ENABLE_SYCL

ENABLE_LAPACK

SUNDIALS_ENABLE_LAPACK

LAPACK_WORKS

SUNDIALS_ENABLE_LAPACK_CHECKS

ENABLE_GINKGO

SUNDIALS_ENABLE_GINKGO

GINKGO_WORKS

SUNDIALS_ENABLE_GINKGO_CHECKS

ENABLE_MAGMA

SUNDIALS_ENABLE_MAGMA

MAGMA_WORKS

SUNDIALS_ENABLE_MAGMA_CHECKS

ENABLE_SUPERLUDIST

SUNDIALS_ENABLE_SUPERLUDIST

SUPERLUDIST_WORKS

SUNDIALS_ENABLE_SUPERLUDIST_CHECKS

ENABLE_SUPERLUMT

SUNDIALS_ENABLE_SUPERLUMT

SUPERLUMT_WORKS

SUNDIALS_ENABLE_SUPERLUMT_CHECKS

ENABLE_KLU

SUNDIALS_ENABLE_KLU

KLU_WORKS

SUNDIALS_ENABLE_KLU_CHECKS

ENABLE_HYPRE

SUNDIALS_ENABLE_HYPRE

HYPRE_WORKS

SUNDIALS_ENABLE_HYPRE_CHECKS

ENABLE_PETSC

SUNDIALS_ENABLE_PETSC

PETSC_WORKS

SUNDIALS_ENABLE_PETSC_CHECKS

ENABLE_TRILINOS

SUNDIALS_ENABLE_TRILINOS

ENABLE_RAJA

SUNDIALS_ENABLE_RAJA

ENABLE_XBRAID

SUNDIALS_ENABLE_XBRAID

XBRAID_WORKS

SUNDIALS_ENABLE_XBRAID_CHECKS

ENABLE_ONEMKL

SUNDIALS_ENABLE_ONEMKL

ONEMKL_WORKS

SUNDIALS_ENABLE_ONEMKL_CHECKS

ENABLE_CALIPER

SUNDIALS_ENABLE_CALIPER

ENABLE_ADIAK

SUNDIALS_ENABLE_ADIAK

ENABLE_KOKKOS

SUNDIALS_ENABLE_KOKKOS

KOKKOS_WORKS

SUNDIALS_ENABLE_KOKKOS_CHECKS

ENABLE_KOKKOS_KERNELS

SUNDIALS_ENABLE_KOKKOS_KERNELS

KOKKOS_KERNELS_WORKS

SUNDIALS_ENABLE_KOKKOS_KERNELS_CHECKS

BUILD_FORTRAN_MODULE_INTERFACE

SUNDIALS_ENABLE_FORTRAN

SUNDIALS_BUILD_WITH_PROFILING

SUNDIALS_ENABLE_PROFILING

SUNDIALS_BUILD_WITH_MONITORING

SUNDIALS_ENABLE_MONITORING

SUNDIALS_BUILD_PACKAGE_FUSED_KERNELS

SUNDIALS_ENABLE_PACKAGE_FUSED_KERNELS

EXAMPLES_ENABLE_C

SUNDIALS_ENABLE_C_EXAMPLES

EXAMPLES_ENABLE_CXX

SUNDIALS_ENABLE_CXX_EXAMPLES

EXAMPLES_ENABLE_F2003

SUNDIALS_ENABLE_FORTRAN_EXAMPLES

EXAMPLES_ENABLE_CUDA

SUNDIALS_ENABLE_CUDA_EXAMPLES

EXAMPLES_INSTALL

SUNDIALS_ENABLE_EXAMPLES_INSTALL

EXAMPLES_INSTALL_PATH

SUNDIALS_EXAMPLES_INSTALL_PATH

BUILD_BENCHMARKS

SUNDIALS_ENABLE_BENCHMARKS

BENCHMARKS_INSTALL_PATH

SUNDIALS_BENCHMARKS_INSTALL_PATH

SUNDIALS_BENCHMARK_OUTPUT_DIR

SUNDIALS_BENCHMARKS_OUTPUT_DIR

SUNDIALS_BENCHMARK_CALIPER_OUTPUT_DIR

SUNDIALS_BENCHMARKS_CALIPER_OUTPUT_DIR

SUNDIALS_BENCHMARK_NUM_CPUS

SUNDIALS_BENCHMARKS_NUM_CPUS

SUNDIALS_BENCHMARK_NUM_GPUS

SUNDIALS_BENCHMARKS_NUM_GPUS

ENABLE_ALL_WARNINGS

SUNDIALS_ENABLE_ALL_WARNINGS

ENABLE_WARNINGS_AS_ERRORS

CMAKE_COMPILE_WARNING_AS_ERROR

ENABLE_ADDRESS_SANITIZER

SUNDIALS_ENABLE_ADDRESS_SANITIZER

ENABLE_MEMORY_SANITIZER

SUNDIALS_ENABLE_MEMORY_SANITIZER

ENABLE_LEAK_SANITIZER

SUNDIALS_ENABLE_LEAK_SANITIZER

Following the updated CMake options, the macros listed below have been deprecated and replaced with versions that align with the new CMake options.

Old Macro

New Macro

SUNDIALS_BUILD_WITH_PROFILING

SUNDIALS_ENABLE_PROFILING

SUNDIALS_BUILD_WITH_MONITORING

SUNDIALS_ENABLE_MONITORING

SUNDIALS_BUILD_PACKAGE_FUSED_KERNELS

SUNDIALS_ENABLE_PACKAGE_FUSED_KERNELS

For changes in prior versions of SUNDIALS see §18.

3.1.3. Reading this User Guide

This user guide is a combination of general usage instructions. Specific example programs are provided as a separate document. We expect that some readers will want to concentrate on the general instructions, while others will refer mostly to the examples, and the organization is intended to accommodate both styles.

There are different possible levels of usage of CVODE. The most casual user, with a small IVP problem only, can get by with reading §3.2.1, then §3.4 through §3.4.3.9 only, and looking at examples in [83].

In a different direction, a more expert user with an IVP problem may want to (a) use a package preconditioner (§3.4.5), (b) supply his/her own Jacobian or preconditioner routines (§3.4.4.6), (c) do multiple runs of problems of the same size (§3.4.3.13), (d) supply a new N_Vector module (§8), (e) supply new SUNLinearSolver and/or SUNMatrix modules (§9 and §10), or even (f) supply new SUNNonlinearSolver modules (§11).

The structure of this document is as follows:

  • In §3.2, we give short descriptions of the numerical methods implemented by CVODE for the solution of initial value problems for systems of ODEs, and continue with short descriptions of preconditioning (§3.2.3), stability limit detection (§3.2.4), and rootfinding (§3.2.5).

  • The following chapter describes the software organization of the CVODE solver (§3.3).

  • §3.4 is the main usage document for CVODE for C applications. It includes a complete description of the user interface for the integration of ODE initial value problems.

  • In §20, we describe the use of CVODE with Fortran applications.

  • §8 gives a brief overview of the generic N_Vector module shared among the various components of SUNDIALS, and details on the N_Vector implementations provided with SUNDIALS.

  • §9 gives a brief overview of the generic SUNMatrix module shared among the various components of SUNDIALS, and details on the SUNMatrix implementations provided with SUNDIALS: a dense implementation (§9.9), a banded implementation (§9.12) and a sparse implementation (§9.14).

  • §10 gives a brief overview of the generic SUNLinearSolver module shared among the various components of SUNDIALS. This chapter contains details on the SUNLinearSolver implementations provided with SUNDIALS. The chapter also contains details on the SUNLinearSolver implementations provided with SUNDIALS that interface with external linear solver libraries.

  • §11 describes the SUNNonlinearSolver API and nonlinear solver implementations shared among the various components of SUNDIALS.

  • Finally, in the appendices, we provide detailed instructions for the installation of CVODE, within the structure of SUNDIALS (§1.1), as well as a list of all the constants used for input to and output from CVODE functions (§3.5).

Finally, the reader should be aware of the following notational conventions in this user guide: program listings and identifiers (such as CVodeInit()) within textual explanations are hyperlinked to their definitions directly; fields in C structures (such as content) appear in italics; and packages or modules, such as CVLS, are written in all capitals.

3.1.4. SUNDIALS License and Notices

All SUNDIALS packages are released open source, under the BSD 3-Clause license for more details see the LICENSE and NOTICE files provided with all SUNDIALS packages.

3.1.5. Acknowledgments

We wish to acknowledge the contributions to previous versions of the CVODE and PVODE codes and their user guides by Scott D. Cohen [40] and George D. Byrne [32].