3.1. Introduction
The ARKODE infrastructure provides adaptivestep time integration modules for stiff, nonstiff and mixed stiff/nonstiff systems of ordinary differential equations (ODEs). ARKODE itself is structured to support a wide range of onestep (but multistage) methods, allowing for rapid development of parallel implementations of stateoftheart time integration methods. At present, ARKODE is packaged with two timestepping modules, ARKStep and ERKStep.
ARKStep supports ODE systems posed in split, linearlyimplicit form,
where \(t\) is the independent variable, \(y\) is the set of dependent variables (in \(\mathbb{R}^N\)), \(M\) is a userspecified, nonsingular operator from \(\mathbb{R}^N\) to \(\mathbb{R}^N\), and the righthand side function is partitioned into up to two components:
\(f^E(t,y)\) contains the “nonstiff” time scale components to be integrated explicitly, and
\(f^I(t,y)\) contains the “stiff” time scale components to be integrated implicitly.
Either of these operators may be disabled, allowing for fully explicit, fully implicit, or combination implicitexplicit (ImEx) time integration.
The algorithms used in ARKStep are adaptive and fixedstep additive Runge–Kutta methods. Such methods are defined through combining two complementary Runge–Kutta methods: one explicit (ERK) and the other diagonally implicit (DIRK). Through appropriately partitioning the ODE righthand side into explicit and implicit components (3.1), such methods have the potential to enable accurate and efficient time integration of stiff, nonstiff, and mixed stiff/nonstiff systems of ordinary differential equations. A key feature allowing for high efficiency of these methods is that only the components in \(f^I(t,y)\) must be solved implicitly, allowing for splittings tuned for use with optimal implicit solver algorithms.
This framework allows for significant freedom over the constitutive methods used for each component, and ARKODE is packaged with a wide array of builtin methods for use. These builtin Butcher tables include adaptive explicit methods of orders 28, adaptive implicit methods of orders 25, and adaptive ImEx methods of orders 35.
ERKStep focuses specifically on problems posed in explicit form,
allowing for increased computational efficiency and memory savings. The algorithms used in ERKStep are adaptive and fixedstep explicit Runge–Kutta methods. As with ARKStep, the ERKStep module is packaged with adaptive explicit methods of orders 28.
MRIStep focuses specifically on problems posed in additive form,
where here the righthand side function is additively split into three components:
\(f^E(t,y)\) contains the “slownonstiff” components of the system (this will be integrated using an explicit method and a large time step \(h^S\)),
\(f^I(t,y)\) contains the “slowstiff” components of the system (this will be integrated using an implicit method and a large time step \(h^S\)), and
\(f^F(t,y)\) contains the “fast” components of the system (this will be integrated using a possibly different method than the slow time scale and a small time step \(h^F \ll h^S\)).
For such problems, MRIStep provides fixedstep slow step multirate infinitesimal step (MIS), multirate infinitesimal GARK (MRIGARK), and implicitexplicit MRIGARK (IMEXMRIGARK) methods, allowing for evolution of the problem (3.3) using multirate methods having orders of accuracy 24.
For ARKStep or MRIStep problems that include nonzero implicit term \(f^I(t,y)\), the resulting implicit system (assumed nonlinear, unless specified otherwise) is solved approximately at each integration step, using a SUNNonlinearSolver module, supplied either by the user or from the underlying SUNDIALS infrastructure. For nonlinear solver algorithms that internally require a linear solver, ARKODE may use a variety of SUNLinearSolver modules provided with SUNDIALS, or again may utilize a usersupplied module.
3.1.1. Changes from previous versions
3.1.1.1. Changes in v5.5.0
Added the functions ARKStepGetJac()
, ARKStepGetJacTime()
,
ARKStepGetJacNumSteps()
, MRIStepGetJac()
,
MRIStepGetJacTime()
, and MRIStepGetJacNumSteps()
to assist in
debugging simulations utilizing a matrixbased linear solver.
Added support for the SYCL backend with RAJA 2022.x.y.
Fixed an underflow bug during root finding.
A new capability to keep track of memory allocations made through the SUNMemoryHelper
classes has been added. Memory allocation stats can be accessed through the
SUNMemoryHelper_GetAllocStats()
function. See the documentation for
the SUNMemoryHelper
classes for more details.
Added support for CUDA v12.
Fixed an issue with finding oneMKL when using the icpx
compiler with the
fsycl
flag as the C++ compiler instead of dpcpp
.
Fixed the shape of the arrays returned by FN_VGetArrayPointer
functions as well
as the FSUNDenseMatrix_Data
, FSUNBandMatrix_Data
, FSUNSparseMatrix_Data
,
FSUNSparseMatrix_IndexValues
, and FSUNSparseMatrix_IndexPointers
functions.
Compiling and running code that uses the SUNDIALS Fortran interfaces with
bounds checking will now work.
Fixed an implicit conversion error in the Butcher table for ESDIRK5(4)7L[2]SA2.
3.1.1.2. Changes in v5.4.1
Fixed a bug with the Kokkos interfaces that would arise when using clang.
Fixed a compilation error with the Intel oneAPI 2022.2 Fortran compiler in the
Fortran 2003 interface test for the serial N_Vector
.
Fixed a bug in the SUNLINSOL_LAPACKBAND and SUNLINSOL_LAPACKDENSE modules which would cause the tests to fail on some platforms.
3.1.1.3. Changes in v5.4.0
CMake 3.18.0 or newer is now required for CUDA support.
A C++14 compliant compiler is now required for C++ based features and examples e.g., CUDA, HIP, RAJA, Trilinos, SuperLU_DIST, MAGMA, GINKGO, and KOKKOS.
Added support for GPU enabled SuperLU_DIST and SuperLU_DIST v8.x.x. Removed support for SuperLU_DIST v6.x.x or older. Fix mismatched definition and declaration bug in SuperLU_DIST matrix constructor.
Added support for the Ginkgo linear
algebra library. This support includes new SUNMatrix
and SUNLinearSolver
implementations, see the sections §10.16 and
§11.23.
Added new NVector
, dense SUNMatrix
, and dense SUNLinearSolver
implementations utilizing the Kokkos Ecosystem for
performance portability, see sections §9.19,
§10.17, and §11.24 for more information.
Added the functions ARKStepSetTableName()
,
ERKStepSetTableName()
, MRIStepCoupling_LoadTableByName()
,
ARKodeButcherTable_LoadDIRKByName()
, and
ARKodeButcherTable_LoadERKByName()
to load a table from a string.
Fixed a bug in the CUDA and HIP vectors where N_VMaxNorm()
would return
the minimum positive floatingpoint value for the zero vector.
Fixed memory leaks/out of bounds memory accesses in the ARKODE MRIStep module that could occur when attaching a coupling table after reinitialization with a different number of stages than originally selected.
3.1.1.4. Changes in v5.3.0
Added the functions ARKStepGetUserData()
, ERKStepGetUserData()
,
and MRIStepGetUserData()
to retrieve the user data pointer provided to
ARKStepSetUserData()
, ERKStepSetUserData()
, and
MRIStepSetUserData()
, respectively.
Fixed a bug in ERKStepReset()
, ERKStepReInit()
,
ARKStepReset()
, ARKStepReInit()
, MRIStepReset()
, and
MRIStepReInit()
where a previouslyset value of tstop (from a call to
ERKStepSetStopTime()
, ARKStepSetStopTime()
, or
MRIStepSetStopTime()
, respectively) would not be cleared.
Updated MRIStepReset()
to call the corresponding
MRIStepInnerResetFn
with the same \((t_R,y_R)\) arguments for the
MRIStepInnerStepper
object that is used to evolve the MRI “fast” time
scale subproblems.
Added a variety of embedded DIRK methods from [74] and [75].
Fixed the unituitive behavior of the USE_GENERIC_MATH
CMake option which
caused the double precision math functions to be used regardless of the value of
SUNDIALS_PRECISION
. Now, SUNDIALS will use precision appropriate math
functions when they are available and the user may provide the math library to
link to via the advanced CMake option SUNDIALS_MATH_LIBRARY
.
Changed SUNDIALS_LOGGING_ENABLE_MPI
CMake option default to be ‘OFF’.
3.1.1.5. Changes in v5.2.0
Added the SUNLogger
API which provides a SUNDIALSwide
mechanism for logging of errors, warnings, informational output,
and debugging output.
Deprecated ARKStepSetDiagnostics()
,
MRIStepSetDiagnostics()
, ERKStepSetDiagnostics()
,
SUNNonlinSolSetPrintLevel_Newton()
,
SUNNonlinSolSetInfoFile_Newton()
,
SUNNonlinSolSetPrintLevel_FixedPoint()
,
SUNNonlinSolSetInfoFile_FixedPoint()
,
SUNLinSolSetInfoFile_PCG()
, SUNLinSolSetPrintLevel_PCG()
,
SUNLinSolSetInfoFile_SPGMR()
, SUNLinSolSetPrintLevel_SPGMR()
,
SUNLinSolSetInfoFile_SPFGMR()
, SUNLinSolSetPrintLevel_SPFGMR()
,
SUNLinSolSetInfoFile_SPTFQM()
, SUNLinSolSetPrintLevel_SPTFQMR()
,
SUNLinSolSetInfoFile_SPBCGS()
, SUNLinSolSetPrintLevel_SPBCGS()
it is recommended to use the SUNLogger API instead. The SUNLinSolSetInfoFile_**
and SUNNonlinSolSetInfoFile_*
family of functions are now enabled
by setting the CMake option SUNDIALS_LOGGING_LEVEL
to a value >= 3
.
Added the function SUNProfiler_Reset()
to reset the region timings and
counters to zero.
Added the functions ARKStepPrintAllStats()
,
ERKStepPrintAllStats()
, and MRIStepPrintAll()
to output all of
the integrator, nonlinear solver, linear solver, and other statistics in one
call. The file scripts/sundials_csv.py
contains functions for parsing the
commaseparated value output files.
Added the functions ARKStepSetDeduceImplicitRhs()
and
MRIStepSetDeduceImplicitRhs()
to optionally remove an evaluation of the
implicit righthand side function after nonlinear solves. See
§3.2.10.1, for considerations on using this
optimization.
Added the function MRIStepSetOrder()
to select the default MRI method of
a given order.
The behavior of N_VSetKernelExecPolicy_Sycl()
has been updated to be
consistent with the CUDA and HIP vectors. The input execution policies are now
cloned and may be freed after calling N_VSetKernelExecPolicy_Sycl()
.
Additionally, NULL
inputs are now allowed and, if provided, will reset the
vector execution policies to the defaults.
Fixed the SUNContext
convenience class for C++ users to disallow copy
construction and allow move construction.
A memory leak in the SYCL vector was fixed where the execution policies were not freed when the vector was destroyed.
The include guard in nvector_mpimanyvector.h
has been corrected to enable
using both the ManyVector and MPIManyVector NVector implementations in the same
simulation.
Changed exported SUNDIALS PETSc CMake targets to be INTERFACE IMPORTED instead of UNKNOWN IMPORTED.
A bug was fixed in the functions
ARKStepGetNumNonlinSolvConvFails()
,
ARKStepGetNonlinSolvStats()
,
MRIStepGetNumNonlinSolvConvFails()
, and
MRIStepGetNonlinSolvStats()
where the number of nonlinear solver failures returned was the number of failed
steps due to a nonlinear solver failure i.e., if a nonlinear solve failed with
a stale Jacobian or preconditioner but succeeded after updating the Jacobian or
preconditioner, the initial failure was not included in the nonlinear solver
failure count. These functions have been updated to return the total number of
nonlinear solver failures. As such users may see an increase in the number of
failures reported.
The functions ARKStepGetNumStepSolveFails()
and
MRIStepGetNumStepSolveFails()
have been added to retrieve the number of
failed steps due to a nonlinear solver failure. The counts returned from these
functions will match those previously returned by
ARKStepGetNumNonlinSolvConvFails()
,
ARKStepGetNonlinSolvStats()
,
MRIStepGetNumNonlinSolvConvFails()
, and
MRIStepGetNonlinSolvStats()
.
3.1.1.6. Changes in v5.1.1
Fixed exported SUNDIALSConfig.cmake
.
Fixed Fortran interface to MRIStepInnerStepper
and MRIStepCoupling
structures and functions.
Added new Fortran example program,
examples/arkode/F2003_serial/ark_kpr_mri_f2003.f90
demonstrating MRI
capabilities.
3.1.1.7. Changes in v5.1.0
Added new reduction implementations for the CUDA and HIP NVECTORs that use
shared memory (local data storage) instead of atomics. These new implementations
are recommended when the target hardware does not provide atomic support for the
floating point precision that SUNDIALS is being built with. The HIP vector uses
these by default, but the N_VSetKernelExecPolicy_Cuda()
and
N_VSetKernelExecPolicy_Hip()
functions can be used to choose between
different reduction implementations.
SUNDIALS::<lib>
targets with no static/shared suffix have been added for use
within the build directory (this mirrors the targets exported on installation).
CMAKE_C_STANDARD
is now set to 99 by default.
Fixed exported SUNDIALSConfig.cmake
when profiling is enabled without Caliper.
Fixed sundials_export.h
include in sundials_config.h
.
Fixed memory leaks in the SUNLINSOL_SUPERLUMT linear solver.
3.1.1.8. Changes in v5.0.0
SUNContext
SUNDIALS v6.0.0 introduces a new SUNContext
object on which all other
SUNDIALS objects depend. As such, the constructors for all SUNDIALS packages,
vectors, matrices, linear solvers, nonlinear solvers, and memory helpers have
been updated to accept a context as the last input. Users upgrading to SUNDIALS
v6.0.0 will need to call SUNContext_Create()
to create a context object
with before calling any other SUNDIALS library function, and then provide this
object to other SUNDIALS constructors. The context object has been introduced to
allow SUNDIALS to provide new features, such as the profiling/instrumentation
also introduced in this release, while maintaining threadsafety. See the
documentation section on the SUNContext
for more details.
A script upgradetosundials6from5.sh
has been provided with the release
(obtainable from the GitHub release page) to help ease the transition to
SUNDIALS v6.0.0. The script will add a SUNCTX_PLACEHOLDER
argument to all of
the calls to SUNDIALS constructors that now require a SUNContext
object. It
can also update deprecated SUNDIALS constants/types to the new names. It can be
run like this:
> ./upgradetosundials6from5.sh <files to update>
SUNProfiler
A capability to profile/instrument SUNDIALS library code has been added. This
can be enabled with the CMake option SUNDIALS_BUILD_WITH_PROFILING
. A
builtin profiler will be used by default, but the Caliper library can also be used instead with the
CMake option ENABLE_CALIPER
. See the documentation section on
profiling for more details. WARNING: Profiling will impact performance, and
should be enabled judiciously.
SUNMemoryHelper
The SUNMemoryHelper
functions SUNMemoryHelper_Alloc()
,
SUNMemoryHelper_Dealloc()
, and SUNMemoryHelper_Copy()
have been
updated to accept an opaque handle as the last input. At a minimum, userdefined
SUNMemoryHelper
implementations will need to update these functions to
accept the additional argument. Typically, this handle is the execution stream
(e.g., a CUDA/HIP stream or SYCL queue) for the operation. The CUDA, HIP, and SYCL
implementations have been updated accordingly. Additionally, the constructor
SUNMemoryHelper_Sycl()
has been updated to remove the SYCL queue as an
input.
NVector
Two new optional vector operations, N_VDotProdMultiLocal()
and
N_VDotProdMultiAllReduce()
, have been added to support
lowsynchronization methods for Anderson acceleration.
The CUDA, HIP, and SYCL execution policies have been moved from the sundials
namespace to the sundials::cuda
, sundials::hip
, and sundials::sycl
namespaces respectively. Accordingly, the prefixes “Cuda”, “Hip”, and “Sycl”
have been removed from the execution policy classes and methods.
The Sundials
namespace used by the Trilinos Tpetra NVector has been replaced
with the sundials::trilinos::nvector_tpetra
namespace.
The serial, PThreads, PETSc, hypre, Parallel, OpenMP_DEV, and OpenMP vector
functions N_VCloneVectorArray_*
and N_VDestroyVectorArray_*
have been
deprecated. The generic N_VCloneVectorArray()
and
N_VDestroyVectorArray()
functions should be used instead.
The previously deprecated constructor N_VMakeWithManagedAllocator_Cuda
and
the function N_VSetCudaStream_Cuda
have been removed and replaced with
N_VNewWithMemHelp_Cuda()
and N_VSetKerrnelExecPolicy_Cuda()
respectively.
The previously deprecated macros PVEC_REAL_MPI_TYPE
and
PVEC_INTEGER_MPI_TYPE
have been removed and replaced with
MPI_SUNREALTYPE
and MPI_SUNINDEXTYPE
respectively.
SUNLinearSolver
The following previously deprecated functions have been removed:
Removed 
Replacement 




















































ARKODE
The MRIStep module has been extended to support implicitexplicit (ImEx)
multirate infinitesimal generalized additive Runge–Kutta (MRIGARK) methods. As
such, MRIStepCreate()
has been updated to include arguments for the slow
explicit and slow implicit ODE righthand side functions.
MRIStepCreate()
has also been updated to require attaching an
MRIStepInnerStepper for evolving the fast time scale. MRIStepReInit()
has been similarly updated to take explicit and implicit righthand side
functions as input. Codes using explicit or implicit MRI methods will need to
update MRIStepCreate()
and MRIStepReInit()
calls to pass
NULL
for either the explicit or implicit righthand side function as
appropriate. If ARKStep is used as the fast time scale integrator, codes will
need to call ARKStepCreateMRIStepInnerStepper()
to wrap the ARKStep
memory as an MRIStepInnerStepper object. Additionally,
MRIStepGetNumRhsEvals()
has been updated to return the number of slow
implicit and explicit function evaluations. The coupling table structure
MRIStepCouplingMem
and the functions MRIStepCoupling_Alloc()
and MRIStepCoupling_Create()
have also been updated to support
IMEXMRIGARK methods.
The deprecated functions MRIStepGetCurrentButcherTables
and
MRIStepWriteButcher
and the utility functions MRIStepSetTable
and
MRIStepSetTableNum
have been removed. Users wishing to create an MRIGARK
method from a Butcher table should use MRIStepCoupling_MIStoMRI()
to
create the corresponding MRI coupling table and attach it with
MRIStepSetCoupling()
.
The implementation of solvedecoupled implicit MRIGARK methods has been updated to remove extraneous slow implicit function calls and reduce the memory requirements.
The previously deprecated functions ARKStepSetMaxStepsBetweenLSet
and
ARKStepSetMaxStepsBetweenJac
have been removed and replaced with
ARKStepSetLSetupFrequency()
and ARKStepSetMaxStepsBetweenJac()
respectively.
The ARKODE Fortran 77 interface has been removed. See §2.5 and the F2003 example programs for more details using the SUNDIALS Fortran 2003 module interfaces.
Deprecations
In addition to the deprecations noted elsewhere, many constants, types, and functions have been renamed so that they are properly namespaced. The old names have been deprecated and will be removed in SUNDIALS v7.0.0.
The following constants, macros, and typedefs are now deprecated:
Deprecated Name 
New Name 































































































































































































In addition, the following functions are now deprecated (compiletime warnings will be thrown if supported by the compiler):
Deprecated Name 
New Name 







































































































In addition, the entire sundials_lapack.h
header file is now deprecated for
removal in SUNDIALS v7.0.0. Note, this header file is not needed to use the
SUNDIALS LAPACK linear solvers.
3.1.1.9. Changes in v4.8.0
The RAJA NVECTOR implementation has been updated to support the SYCL backend
in addition to the CUDA and HIP backend. Users can choose the backend when
configuring SUNDIALS by using the SUNDIALS_RAJA_BACKENDS
CMake variable.
This module remains experimental and is subject to change from version to
version.
A new SUNMatrix and SUNLinearSolver implementation were added to interface with the Intel oneAPI Math Kernel Library (oneMKL). Both the matrix and the linear solver support general dense linear systems as well as block diagonal linear systems. See §11.14 for more details. This module is experimental and is subject to change from version to version.
Added a new optional function to the SUNLinearSolver API,
SUNLinSolSetZeroGuess()
, to indicate that the next call to
SUNLinSolSolve()
will be made with a zero initial guess. SUNLinearSolver
implementations that do not use the SUNLinSolNewEmpty()
constructor
will, at a minimum, need set the setzeroguess
function pointer in the linear
solver ops
structure to NULL
. The SUNDIALS iterative linear solver
implementations have been updated to leverage this new set function to remove
one dot product per solve.
ARKODE now supports a new “matrixembedded” SUNLinearSolver type. This type supports usersupplied SUNLinearSolver implementations that set up and solve the specified linear system at each linear solve call. Any matrixrelated data structures are held internally to the linear solver itself, and are not provided by the SUNDIALS package.
Support for userdefined inner (fast) integrators has been to the MRIStep module. See §3.4.4.4 for more information on providing a userdefined integration method.
Added the functions ARKStepSetNlsRhsFn()
and
MRIStepSetNlsRhsFn()
to supply an alternative implicit righthand side
function for use within nonlinear system function evaluations.
The installed SUNDIALSConfig.cmake file now supports the COMPONENTS
option
to find_package
. The exported targets no longer have IMPORTED_GLOBAL set.
A bug was fixed in SUNMatCopyOps()
where the matrixvector product setup
function pointer was not copied.
A bug was fixed in the SPBCGS and SPTFQMR solvers for the case where a nonzero initial guess and a solution scaling vector are provided. This fix only impacts codes using SPBCGS or SPTFQMR as standalone solvers as all SUNDIALS packages utilize a zero initial guess.
A bug was fixed in the ARKODE stepper modules where the stop time may be passed after resetting the integrator.
3.1.1.10. Changes in v4.7.0
A new NVECTOR implementation based on the SYCL abstraction layer has been added targeting Intel GPUs. At present the only SYCL compiler supported is the DPC++ (Intel oneAPI) compiler. See §9.17 for more details. This module is considered experimental and is subject to major changes even in minor releases.
A new SUNMatrix and SUNLinearSolver implementation were added to interface with the MAGMA linear algebra library. Both the matrix and the linear solver support general dense linear systems as well as block diagonal linear systems, and both are targeted at GPUs (AMD or NVIDIA). See §11.13 for more details.
3.1.1.11. Changes in v4.6.1
Fixed a bug in the SUNDIALS CMake which caused an error if the CMAKE_CXX_STANDARD and SUNDIALS_RAJA_BACKENDS options were not provided.
Fixed some compiler warnings when using the IBM XL compilers.
3.1.1.12. Changes in v4.6.0
A new NVECTOR implementation based on the AMD ROCm HIP platform has been added. This vector can target NVIDIA or AMD GPUs. See §9.16 for more details. This module is considered experimental and is subject to change from version to version.
The RAJA NVECTOR implementation has been updated to support the HIP backend
in addition to the CUDA backend. Users can choose the backend when configuring
SUNDIALS by using the SUNDIALS_RAJA_BACKENDS
CMake variable. This module
remains experimental and is subject to change from version to version.
A new optional operation, N_VGetDeviceArrayPointer()
, was added to the
N_Vector API. This operation is useful for N_Vectors that utilize dual memory
spaces, e.g. the native SUNDIALS CUDA N_Vector.
The SUNMATRIX_CUSPARSE and SUNLINEARSOLVER_CUSOLVERSP_BATCHQR implementations
no longer require the SUNDIALS CUDA N_Vector. Instead, they require that the
vector utilized provides the N_VGetDeviceArrayPointer()
operation, and
that the pointer returned by N_VGetDeviceArrayPointer()
is a valid CUDA
device pointer.
3.1.1.13. Changes in v4.5.0
Refactored the SUNDIALS build system. CMake 3.12.0 or newer is now required. Users will likely see deprecation warnings, but otherwise the changes should be fully backwards compatible for almost all users. SUNDIALS now exports CMake targets and installs a SUNDIALSConfig.cmake file.
Added support for SuperLU DIST 6.3.0 or newer.
3.1.1.14. Changes in v4.4.0
Added full support for timedependent mass matrices in ARKStep, and expanded existing nonidentity mass matrix infrastructure to support use of the fixed point nonlinear solver. Fixed bug for ERK method integration with static mass matrices.
An interface between ARKStep and the XBraid multigrid reduction in time (MGRIT)
library [1] has been added to enable parallelintime integration. See the
§3.4.2.4 section for more information and the example
codes in examples/arkode/CXX_xbraid
. This interface required the addition of
three new N_Vector operations to exchange vector data between computational
nodes, see N_VBufSize()
, N_VBufPack()
, and
N_VBufUnpack()
. These N_Vector operations are only used within the
XBraid interface and need not be implemented for any other context.
Updated the MRIStep timestepping module in ARKODE to support higherorder MRIGARK methods [91], including methods that involve solvedecoupled, diagonallyimplicit treatment of the slow time scale.
Added the functions ARKStepSetLSNormFactor()
,
ARKStepSetMassLSNormFactor()
, and MRIStepSetLSNormFactor()
to specify the factor for converting between integrator tolerances (WRMS norm)
and linear solver tolerances (L2 norm) i.e.,
tol_L2 = nrmfac * tol_WRMS
.
Added new reset functions ARKStepReset()
, ERKStepReset()
,
and MRIStepReset()
to reset the stepper time and state vector to
userprovided values for continuing the integration from that point while
retaining the integration history. These function complement the
reinitialization functions ARKStepReInit()
, ERKStepReInit()
,
and MRIStepReInit()
which reinitialize the stepper so that the problem
integration should resume as if started from scratch.
Added new functions ARKStepComputeState()
,
ARKStepGetNonlinearSystemData()
, MRIStepComputeState()
, and
MRIStepGetNonlinearSystemData()
which advanced users might find useful
if providing a custom SUNNonlinSolSysFn()
.
The expected behavior of SUNNonlinSolGetNumIters()
and
SUNNonlinSolGetNumConvFails()
in the SUNNonlinearSolver API have been
updated to specify that they should return the number of nonlinear solver
iterations and convergence failures in the most recent solve respectively rather
than the cumulative number of iterations and failures across all solves
respectively. The API documentation and SUNDIALS provided SUNNonlinearSolver
implementations have been updated accordingly. As before, the cumulative number
of nonlinear iterations may be retrieved by calling
ARKStepGetNumNonlinSolvIters()
, the cumulative number of failures with
ARKStepGetNumNonlinSolvConvFails()
, or both with
ARKStepGetNonlinSolvStats()
.
A minor bug in checking the Jacobian evaluation frequency has been fixed. As a
result codes using using a nondefault Jacobian update frequency through a call
to ARKStepSetMaxStepsBetweenJac()
will need to increase the provided
value by 1 to achieve the same behavior as before. Additionally, for greater
clarity the functions ARKStepSetMaxStepsBetweenLSet()
and
ARKStepSetMaxStepsBetweenJac()
have been deprecated and replaced with
ARKStepSetLSetupFrequency()
and ARKStepSetJacEvalFrequency()
respectively.
The NVECTOR_RAJA
module has been updated to mirror the NVECTOR_CUDA
module.
Notably, the update adds managed memory support to the NVECTOR_RAJA
module.
Users of the module will need to update any calls to the N_VMake_Raja
function
because that signature was changed. This module remains experimental and is
subject to change from version to version.
The NVECTOR_TRILINOS
module has been updated to work with Trilinos 12.18+.
This update changes the local ordinal type to always be an int
.
Added support for CUDA v11.
3.1.1.15. Changes in v4.3.0
Fixed a bug in ARKODE where the prototypes for ERKStepSetMinReduction()
and ARKStepSetMinReduction()
were not included in arkode_erkstep.h
and arkode_arkstep.h
respectively.
Fixed a bug where inequality constraint checking would need to be disabled and
then reenabled to update the inequality constraint values after resizing a
problem. Resizing a problem will now disable constraints and a call to
ARKStepSetConstraints()
or ERKStepSetConstraints()
is
required to reenable constraint checking for the new problem size.
Fixed a bug in the iterative linear solver modules where an error is not
returned if the Atimes function is NULL
or, if preconditioning is enabled,
the PSolve function is NULL
.
Added the ability to control the CUDA kernel launch parameters for the
NVECTOR_CUDA
and SUNMATRIX_CUSPARSE
modules. These modules remain
experimental and are subject to change from version to version.
In addition, the NVECTOR_CUDA
kernels were rewritten to be more flexible.
Most users should see equivalent performance or some improvement, but a select
few may observe minor performance degradation with the default settings. Users
are encouraged to contact the SUNDIALS team about any perfomance changes
that they notice.
Added the optional function ARKStepSetJacTimesRhsFn()
to specify an
alternative implicit righthand side function for computing Jacobianvector
products with the internal difference quotient approximation.
Added new capabilities for monitoring the solve phase in the SUNNONLINSOL_NEWTON
and SUNNONLINSOL_FIXEDPOINT
modules, and the SUNDIALS iterative linear solver
modules. SUNDIALS must be built with the CMake option
SUNDIALS_BUILD_WITH_MONITORING
to use these capabilties.
3.1.1.16. Changes in v4.2.0
Fixed a build system bug related to the Fortran 2003 interfaces when using the
IBM XL compiler. When building the Fortran 2003 interfaces with an XL compiler
it is recommended to set CMAKE_Fortran_COMPILER
to f2003
, xlf2003
,
or xlf2003_r
.
Fixed a bug in how ARKODE interfaces with a usersupplied, iterative, unscaled linear solver.
In this case, ARKODE adjusts the linear solver tolerance in an attempt to account for the
lack of support for left/right scaling matrices. Previously, ARKODE computed this scaling
factor using the error weight vector, ewt
; this fix changes that to the residual weight vector,
rwt
, that can differ from ewt
when solving problems with nonidentity mass matrix.
Fixed a similar bug in how ARKODE interfaces with scaled linear solvers when solving problems
with nonidentity mass matrices. Here, the left scaling matrix should correspond with rwt
and the right scaling matrix with ewt
; these were reversed but are now correct.
Fixed a bug where a nondefault value for the maximum allowed growth factor after the first step would be ignored.
The function ARKStepSetLinearSolutionScaling()
was added to
enable or disable the scaling applied to linear system solutions with
matrixbased linear solvers to account for a lagged value of \(\gamma\) in
the linear system matrix e.g., \(M  \gamma J\) or \(I  \gamma J\).
Scaling is enabled by default when using a matrixbased linear solver.
Added two new functions, ARKStepSetMinReduction()
and
ERKStepSetMinReduction()
, to change the minimum allowed step size
reduction factor after an error test failure.
Added a new SUNMatrix
implementation, §10.13, that interfaces
to the sparse matrix implementation from the NVIDIA cuSPARSE library. In addition,
the §11.22 SUNLinearSolver
has been updated to
use this matrix, as such, users of this module will need to update their code.
These modules are still considered to be experimental, thus they are subject to
breaking changes even in minor releases.
Added a new “stiff” interpolation module, based on Lagrange polynomial interpolation,
that is accessible to each of the ARKStep, ERKStep and MRIStep timestepping modules.
This module is designed to provide increased interpolation accuracy when integrating
stiff problems, as opposed to the ARKODEstandard Hermite interpolation module that
can suffer when the IVP righthand side has large Lipschitz constant. While the
Hermite module remains the default, the new Lagrange module may be enabled using one
of the routines ARKStepSetInterpolantType()
, ERKStepSetInterpolantType()
,
or MRIStepSetInterpolantType()
. The serial example problem ark_brusselator.c
has been converted to use this Lagrange interpolation module. Created accompanying routines
ARKStepSetInterpolantDegree()
, ARKStepSetInterpolantDegree()
and
ARKStepSetInterpolantDegree()
to provide user control over these
interpolating polynomials. While the routines ARKStepSetDenseOrder()
,
ARKStepSetDenseOrder()
and ARKStepSetDenseOrder()
still exist,
these have been deprecated and will be removed in a future release.
3.1.1.17. Changes in v4.1.0
Fixed a build system bug related to finding LAPACK/BLAS.
Fixed a build system bug related to checking if the KLU library works.
Fixed a build system bug related to finding PETSc when using the CMake
variables PETSC_INCLUDES
and PETSC_LIBRARIES
instead of
PETSC_DIR
.
Added a new build system option, CUDA_ARCH
, that can be used to specify
the CUDA architecture to compile for.
Fixed a bug in the Fortran 2003 interfaces to the ARKODE Butcher table routines and structure.
This includes changing the ARKodeButcherTable
type to be a type(c_ptr)
in Fortran.
Added two utility functions, SUNDIALSFileOpen
and SUNDIALSFileClose
for creating/destroying file pointers that are useful when using the Fortran
2003 interfaces.
Added support for a usersupplied function to update the prediction for each
implicit stage solution in ARKStep. If supplied, this routine will be called
after any existing ARKStep predictor algorithm completes, so that the
predictor may be modified by the user as desired. The new usersupplied routine
has type ARKStepStagePredictFn
, and may be set by calling
ARKStepSetStagePredictFn()
.
The MRIStep module has been updated to support attaching different user data
pointers to the inner and outer integrators. If applicable, user codes will
need to add a call to ARKStepSetUserData()
to attach their user data
pointer to the inner integrator memory as MRIStepSetUserData()
will
not set the pointer for both the inner and outer integrators. The MRIStep
examples have been updated to reflect this change.
Added support for constant damping to the SUNNonlinearSolver_FixedPoint
module when using Anderson acceleration. See §12.8.1
and the SUNNonlinSolSetDamping_FixedPoint()
for more details.
3.1.1.18. Changes in v4.0.0
Build system changes
Increased the minimum required CMake version to 3.5 for most SUNDIALS configurations, and 3.10 when CUDA or OpenMP with device offloading are enabled.
The CMake option BLAS_ENABLE
and the variable BLAS_LIBRARIES
have been
removed to simplify builds as SUNDIALS packages do not use BLAS directly. For
third party libraries that require linking to BLAS, the path to the BLAS
library should be included in the _LIBRARIES
variable for the third party
library e.g., SUPERLUDIST_LIBRARIES
when enabling SuperLU_DIST.
Fixed a bug in the build system that prevented the PThreads NVECTOR module from being built.
NVECTOR module changes
Two new functions were added to aid in creating custom NVECTOR objects. The
constructor N_VNewEmpty()
allocates an “empty” generic NVECTOR with
the object’s content pointer and the function pointers in the operations
structure initialized to NULL
. When used in the constructor for custom
objects this function will ease the introduction of any new optional operations
to the NVECTOR API by ensuring only required operations need to be set.
Additionally, the function N_VCopyOps()
has been added to copy the
operation function pointers between vector objects. When used in clone routines
for custom vector objects these functions also will ease the introduction of
any new optional operations to the NVECTOR API by ensuring all operations
are copied when cloning objects.
Two new NVECTOR implementations, NVECTOR_MANYVECTOR and NVECTOR_MPIMANYVECTOR, have been created to support flexible partitioning of solution data among different processing elements (e.g., CPU + GPU) or for multiphysics problems that couple distinct MPIbased simulations together. This implementation is accompanied by additions to user documentation and SUNDIALS examples.
One new required vector operation and ten new optional vector operations have
been added to the NVECTOR API. The new required operation, N_VGetLength()
,
returns the global length of an N_Vector
. The optional operations have
been added to support the new NVECTOR_MPIMANYVECTOR implementation. The
operation N_VGetCommunicator()
must be implemented by subvectors that are
combined to create an NVECTOR_MPIMANYVECTOR, but is not used outside of
this context. The remaining nine operations are optional local reduction
operations intended to eliminate unnecessary latency when performing vector
reduction operations (norms, etc.) on distributed memory systems. The optional
local reduction vector operations are
N_VDotProdLocal()
,
N_VMaxNormLocal()
,
N_VMinLocal()
,
N_VL1NormLocal()
,
N_VWSqrSumLocal()
,
N_VWSqrSumMaskLocal()
,
N_VInvTestLocal()
,
N_VConstrMaskLocal()
, and
N_VMinQuotientLocal()
.
If an NVECTOR implementation defines any of the local operations as
NULL
, then the NVECTOR_MPIMANYVECTOR will call standard NVECTOR
operations to complete the computation.
An additional NVECTOR implementation, NVECTOR_MPIPLUSX, has been created to support the MPI+X paradigm where X is a type of onnode parallelism (e.g., OpenMP, CUDA). The implementation is accompanied by additions to user documentation and SUNDIALS examples.
The *_MPICuda
and *_MPIRaja
functions have been removed from the
NVECTOR_CUDA and NVECTOR_RAJA implementations respectively. Accordingly, the
nvector_mpicuda.h
, nvector_mpiraja.h
, libsundials_nvecmpicuda.lib
,
and libsundials_nvecmpicudaraja.lib
files have been removed. Users should
use the NVECTOR_MPIPLUSX module coupled in conjunction with the NVECTOR_CUDA
or NVECTOR_RAJA modules to replace the functionality. The necessary changes are
minimal and should require few code modifications. See the programs in
examples/ida/mpicuda
and examples/ida/mpiraja
for examples of how to
use the NVECTOR_MPIPLUSX module with the NVECTOR_CUDA and NVECTOR_RAJA modules
respectively.
Fixed a memory leak in the NVECTOR_PETSC module clone function.
Made performance improvements to the NVECTOR_CUDA module. Users who utilize a nondefault stream should no longer see default stream synchronizations after memory transfers.
Added a new constructor to the NVECTOR_CUDA module that allows a user to provide custom allocate and free functions for the vector data array and internal reduction buffer.
Added new Fortran 2003 interfaces for most NVECTOR modules. See the §2.5 section for more details.
Added three new NVECTOR utility functions,
N_VGetVecAtIndexVectorArray()
N_VSetVecAtIndexVectorArray()
, and
N_VNewVectorArray()
,
for working with N_Vector
arrays when using the Fortran 2003 interfaces.
SUNMatrix module changes
Two new functions were added to aid in creating custom SUNMATRIX objects. The
constructor SUNMatNewEmpty()
allocates an “empty” generic SUNMATRIX with
the object’s content pointer and the function pointers in the operations
structure initialized to NULL
. When used in the constructor for custom
objects this function will ease the introduction of any new optional operations
to the SUNMATRIX API by ensuring only required operations need to be set.
Additionally, the function SUNMatCopyOps()
has been added to copy the
operation function pointers between matrix objects. When used in clone routines
for custom matrix objects these functions also will ease the introduction of any
new optional operations to the SUNMATRIX API by ensuring all operations are
copied when cloning objects.
A new operation, SUNMatMatvecSetup()
, was added to the SUNMATRIX API.
Users who have implemented custom SUNMATRIX modules will need to at least
update their code to set the corresponding ops
structure member,
matvecsetup
, to NULL
.
A new operation, SUNMatMatvecSetup()
, was added to the SUNMATRIX API
to perform any setup necessary for computing a matrixvector product. This
operation is useful for SUNMATRIX implementations which need to prepare the
matrix itself, or communication structures before performing the matrixvector
product. Users who have implemented custom SUNMATRIX modules will need to at
least update their code to set the corresponding ops
structure member,
matvecsetup
, to NULL
.
The generic SUNMATRIX API now defines error codes to be returned by SUNMATRIX operations. Operations which return an integer flag indiciating success/failure may return different values than previously.
A new SUNMATRIX (and SUNLINEARSOLVER) implementation was added to facilitate the use of the SuperLU_DIST library with SUNDIALS.
Added new Fortran 2003 interfaces for most SUNMATRIX modules. See the §2.5 section for more details.
SUNLinearSolver module changes
A new function was added to aid in creating custom SUNLINEARSOLVER objects.
The constructor SUNLinSolNewEmpty()
allocates an “empty” generic
SUNLINEARSOLVER with the object’s content pointer and the function pointers
in the operations structure initialized to NULL
. When used in the
constructor for custom objects this function will ease the introduction of any
new optional operations to the SUNLINEARSOLVER API by ensuring only required
operations need to be set.
The return type of the SUNLINEARSOLVER API function SUNLinSolLastFlag()
has changed from long int
to sunindextype
to be consistent with the
type used to store row indices in dense and banded linear solver modules.
Added a new optional operation to the SUNLINEARSOLVER API,
SUNLinSolGetID()
, that returns a SUNLinearSolver_ID
for identifying
the linear solver module.
The SUNLINEARSOLVER API has been updated to make the initialize and setup functions optional.
A new SUNLINEARSOLVER (and SUNMATRIX) implementation was added to facilitate the use of the SuperLU_DIST library with SUNDIALS.
Added a new SUNLinearSolver implementation, SUNLinearSolver_cuSolverSp_batchQR
,
which leverages the NVIDIA cuSOLVER sparse batched QR method for efficiently
solving block diagonal linear systems on NVIDIA GPUs.
Added three new accessor functions to the SUNLinSol_KLU module,
SUNLinSol_KLUGetSymbolic()
, SUNLinSol_KLUGetNumeric()
, and
SUNLinSol_KLUGetCommon()
, to provide user access to the underlying
KLU solver structures.
Added new Fortran 2003 interfaces for most SUNLINEARSOLVER modules. See the §2.5 section for more details.
SUNNonlinearSolver module changes
A new function was added to aid in creating custom SUNNONLINEARSOLVER
objects. The constructor SUNNonlinSolNewEmpty()
allocates an “empty”
generic SUNNONLINEARSOLVER with the object’s content pointer and the function
pointers in the operations structure initialized to NULL
. When used in the
constructor for custom objects this function will ease the introduction of any
new optional operations to the SUNNONLINEARSOLVER API by ensuring only
required operations need to be set.
To facilitate the use of user supplied nonlinear solver convergence test
functions the SUNNonlinSolSetConvTestFn()
function in the
SUNNONLINEARSOLVER API has been updated to take a void*
data pointer as
input. The supplied data pointer will be passed to the nonlinear solver
convergence test function on each call.
The inputs values passed to the first two inputs of the SUNNonlinSolSolve()
function in the SUNNONLINEARSOLVER have been changed to be the predicted
state and the initial guess for the correction to that state. Additionally,
the definitions of SUNNonlinSolLSetupFn
and SUNNonlinSolLSolveFn
in the SUNNONLINEARSOLVER API have been updated to remove unused input
parameters.
Added a new SUNNonlinearSolver
implementation, SUNNonlinsol_PetscSNES
,
which interfaces to the PETSc SNES nonlinear solver API.
Added new Fortran 2003 interfaces for most SUNNONLINEARSOLVER modules. See the §2.5 section for more details.
ARKODE changes
The MRIStep module has been updated to support explicit, implicit, or ImEx
methods as the fast integrator using the ARKStep module. As a result some
function signatures have been changed including MRIStepCreate()
which
now takes an ARKStep memory structure for the fast integration as an input.
Fixed a bug in the ARKStep timestepping module that would result in an infinite loop if the nonlinear solver failed to converge more than the maximum allowed times during a single step.
Fixed a bug that would result in a “too much accuracy requested” error when using fixed time step sizes with explicit methods in some cases.
Fixed a bug in ARKStep where the mass matrix linear solver setup function was not called in the Matrixfree case.
Fixed a minor bug in ARKStep where an incorrect flag is reported when an error occurs in the mass matrix setup or Jacobianvector product setup functions.
Fixed a memeory leak in FARKODE when not using the default nonlinear solver.
The reinitialization functions ERKStepReInit()
,
ARKStepReInit()
, and MRIStepReInit()
have been updated to
retain the minimum and maxiumum step size values from before reinitialization
rather than resetting them to the default values.
Removed extraneous calls to N_VMin()
for simulations where
the scalar valued absolute tolerance, or all entries of the
vectorvalued absolute tolerance array, are strictly positive. In
this scenario, ARKODE will remove at least one global reduction per
time step.
The ARKLS interface has been updated to only zero the Jacobian matrix before
calling a usersupplied Jacobian evaluation function when the attached linear
solver has type SUNLINEARSOLVER_DIRECT
.
A new linear solver interface function ARKLsLinSysFn()
was added as an
alternative method for evaluating the linear system \(A = M  \gamma J\).
Added two new embedded ARK methods of orders 4 and 5 to ARKODE (from [76]).
Support for optional inequality constraints on individual components of the
solution vector has been added the ARKODE ERKStep and ARKStep modules. See
the descriptions of ERKStepSetConstraints()
and
ARKStepSetConstraints()
for more details. Note that enabling
constraint handling requires the NVECTOR operations N_VMinQuotient()
,
N_VConstrMask()
, and N_VCompare()
that were not previously
required by ARKODE.
Added two new ‘Get’ functions to ARKStep, ARKStepGetCurrentGamma()
,
and ARKStepGetCurrentState()
, that may be useful to users who choose
to provide their own nonlinear solver implementation.
Add two new ‘Set’ functions to MRIStep, MRIStepSetPreInnerFn()
and
MRIStepSetPostInnerFn()
for performing communication or memory
transfers needed before or after the inner integration.
A new Fortran 2003 interface to ARKODE was added. This includes Fortran 2003 interfaces to the ARKStep, ERKStep, and MRIStep timestepping modules. See the §2.5 section for more details.
3.1.1.19. Changes in v3.1.0
An additional NVECTOR implementation was added for the Tpetra vector from the Trilinos library to facilitate interoperability between SUNDIALS and Trilinos. This implementation is accompanied by additions to user documentation and SUNDIALS examples.
A bug was fixed where a nonlinear solver object could be freed twice in some use cases.
The EXAMPLES_ENABLE_RAJA
CMake option has been removed. The option EXAMPLES_ENABLE_CUDA
enables all examples that use CUDA including the RAJA examples with a CUDA back end
(if the RAJA NVECTOR is enabled).
The implementation header file arkode_impl.h is no longer installed. This means users
who are directly manipulating the ARKodeMem
structure will need to update their code
to use ARKODE’s public API.
Python is no longer required to run make test
and make test_install
.
Fixed a bug in ARKodeButcherTable_Write
when printing a Butcher table
without an embedding.
3.1.1.20. Changes in v3.0.2
Added information on how to contribute to SUNDIALS and a contributing agreement.
3.1.1.21. Changes in v3.0.1
A bug in ARKODE where single precision builds would fail to compile has been fixed.
3.1.1.22. Changes in v3.0.0
The ARKODE library has been entirely rewritten to support a modular
approach to onestep methods, which should allow rapid research and
development of novel integration methods without affecting existing
solver functionality. To support this, the existing ARKbased methods
have been encapsulated inside the new ARKStep
timestepping
module. Two new timestepping modules have been added:
The
ERKStep
module provides an optimized implementation for explicit Runge–Kutta methods with reduced storage and number of calls to the ODE righthand side function.The
MRIStep
module implements tworate explicitexplicit multirate infinitesimal step methods utilizing different step sizes for slow and fast processes in an additive splitting.
This restructure has resulted in numerous small changes to the user
interface, particularly the suite of “Set” routines for userprovided
solver parameters and “Get” routines to access solver statistics,
that are now prefixed with the name of timestepping module (e.g., ARKStep
or ERKStep
) instead of ARKODE
. Aside from affecting the names of these
routines, userlevel changes have been kept to a minimum. However, we recommend
that users consult both this documentation and the ARKODE example programs for
further details on the updated infrastructure.
As part of the ARKODE restructuring an ARKodeButcherTable
structure
has been added for storing Butcher tables. Functions for creating new Butcher
tables and checking their analytic order are provided along with other utility
routines. For more details see §3.5.
Two changes were made in the initial step size algorithm:
Fixed an efficiency bug where an extra call to the right hand side function was made.
Changed the behavior of the algorithm if the maxiterations case is hit. Before the algorithm would exit with the step size calculated on the penultimate iteration. Now it will exit with the step size calculated on the final iteration.
ARKODE’s dense output infrastructure has been improved to support higherdegree Hermite polynomial interpolants (up to degree 5) over the last successful time step.
ARKODE’s previous direct and iterative linear solver interfaces, ARKDLS and
ARKSPILS, have been merged into a single unified linear solver interface, ARKLS,
to support any valid SUNLINSOL module. This includes DIRECT
and
ITERATIVE
types as well as the new MATRIX_ITERATIVE
type. Details
regarding how ARKLS utilizes linear solvers of each type as well as discussion
regarding intended use cases for usersupplied SUNLinSol implementations are
included in the chapter §11. All ARKODE examples programs and the
standalone linear solver examples have been updated to use the unified linear
solver interface.
The user interface for the new ARKLS module is very similar to the previous
ARKDLS and ARKSPILS interfaces. Additionally, we note that Fortran users will
need to enlarge their iout
array of optional integer outputs, and update the
indices that they query for certain linearsolverrelated statistics.
The names of all constructor routines for SUNDIALSprovided SUNLinSol
implementations have been updated to follow the naming convention
SUNLinSol_*
where *
is the name of the linear solver. The new names are
SUNLinSol_Band
, SUNLinSol_Dense
, SUNLinSol_KLU
,
SUNLinSol_LapackBand
, SUNLinSol_LapackDense
, SUNLinSol_PCG
,
SUNLinSol_SPBCGS
, SUNLinSol_SPFGMR
, SUNLinSol_SPGMR
,
SUNLinSol_SPTFQMR
, and SUNLinSol_SuperLUMT
. Solverspecific “set”
routine names have been similarly standardized. To minimize challenges in user
migration to the new names, the previous routine names may still be used; these
will be deprecated in future releases, so we recommend that users migrate to the
new names soon. All ARKODE example programs and the standalone linear solver
examples have been updated to use the new naming convention.
The SUNBandMatrix
constructor has been simplified to remove the
storage upper bandwidth argument.
SUNDIALS integrators have been updated to utilize generic nonlinear solver modules defined through the SUNNONLINSOL API. This API will ease the addition of new nonlinear solver options and allow for external or usersupplied nonlinear solvers. The SUNNONLINSOL API and SUNDIALS provided modules are described in §12 and follow the same object oriented design and implementation used by the NVector, SUNMatrix, and SUNLinSol modules. Currently two SUNNONLINSOL implementations are provided, SUNNonlinSol_Newton and SUNNonlinSol_FixedPoint. These replicate the previous integrator specific implementations of a Newton iteration and an accelerated fixedpoint iteration, respectively. Example programs using each of these nonlinear solver modules in a standalone manner have been added and all ARKODE example programs have been updated to use generic SUNNonlinSol modules.
As with previous versions, ARKODE will use the Newton solver (now
provided by SUNNonlinSol_Newton) by default. Use of the
ARKStepSetLinear()
routine (previously named
ARKodeSetLinear
) will indicate that the problem is
linearlyimplicit, using only a single Newton iteration per implicit
stage. Users wishing to switch to the accelerated fixedpoint solver
are now required to create a SUNNonlinSol_FixedPoint object and attach
that to ARKODE, instead of calling the previous
ARKodeSetFixedPoint
routine. See the documentation sections
§3.4.2.1,
§3.4.2.2.5, and
§12.8 for further details, or the serial C
example program ark_brusselator_fp.c
for an example.
Three fused vector operations and seven vector array operations have been added
to the NVECTOR API. These optional operations are disabled by default and may
be activated by calling vector specific routines after creating an NVector (see
§9.1 for more details). The new operations are intended
to increase data reuse in vector operations, reduce parallel communication on
distributed memory systems, and lower the number of kernel launches on systems
with accelerators. The fused operations are N_VLinearCombination
,
N_VScaleAddMulti
, and N_VDotProdMulti
, and the vector array operations
are N_VLinearCombinationVectorArray
, N_VScaleVectorArray
,
N_VConstVectorArray
, N_VWrmsNormVectorArray
,
N_VWrmsNormMaskVectorArray
, N_VScaleAddMultiVectorArray
, and
N_VLinearCombinationVectorArray
. If an NVector implementation defines any of
these operations as NULL
, then standard NVector operations will
automatically be called as necessary to complete the computation.
Multiple changes to the CUDA NVECTOR were made:
Changed the
N_VMake_Cuda
function to take a host data pointer and a device data pointer instead of anN_VectorContent_Cuda
object.Changed
N_VGetLength_Cuda
to return the global vector length instead of the local vector length.Added
N_VGetLocalLength_Cuda
to return the local vector length.Added
N_VGetMPIComm_Cuda
to return the MPI communicator used.Removed the accessor functions in the namespace
suncudavec
.Added the ability to set the
cudaStream_t
used for execution of the CUDA NVECTOR kernels. See the functionN_VSetCudaStreams_Cuda
.Added
N_VNewManaged_Cuda
,N_VMakeManaged_Cuda
, andN_VIsManagedMemory_Cuda
functions to accommodate using managed memory with the CUDA NVECTOR.
Multiple changes to the RAJA NVECTOR were made:
Changed
N_VGetLength_Raja
to return the global vector length instead of the local vector length.Added
N_VGetLocalLength_Raja
to return the local vector length.Added
N_VGetMPIComm_Raja
to return the MPI communicator used.Removed the accessor functions in the namespace
sunrajavec
.
A new NVECTOR implementation for leveraging OpenMP 4.5+ device offloading has been added, NVECTOR_OpenMPDEV. See §9.20 for more details.
3.1.1.23. Changes in v2.2.1
Fixed a bug in the CUDA NVECTOR where the N_VInvTest
operation could
write beyond the allocated vector data.
Fixed library installation path for multiarch systems. This fix changes the default
library installation path to CMAKE_INSTALL_PREFIX/CMAKE_INSTALL_LIBDIR
from CMAKE_INSTALL_PREFIX/lib
. CMAKE_INSTALL_LIBDIR
is automatically
set, but is available as a CMAKE option that can modified.
3.1.1.24. Changes in v2.2.0
Fixed a problem with setting sunindextype
which would occur with
some compilers (e.g. armclang) that did not define __STDC_VERSION__
.
Added hybrid MPI/CUDA and MPI/RAJA vectors to allow use of more than one MPI rank when using a GPU system. The vectors assume one GPU device per MPI rank.
Changed the name of the RAJA NVECTOR library to
libsundials_nveccudaraja.lib
from
libsundials_nvecraja.lib
to better reflect that we only support CUDA
as a backend for RAJA currently.
Several changes were made to the build system:
CMake 3.1.3 is now the minimum required CMake version.
Deprecate the behavior of the
SUNDIALS_INDEX_TYPE
CMake option and added theSUNDIALS_INDEX_SIZE
CMake option to select thesunindextype
integer size.The native CMake FindMPI module is now used to locate an MPI installation.
If MPI is enabled and MPI compiler wrappers are not set, the build system will check if
CMAKE_<language>_COMPILER
can compile MPI programs before trying to locate and use an MPI installation.The previous options for setting MPI compiler wrappers and the executable for running MPI programs have been have been depreated. The new options that align with those used in native CMake FindMPI module are
MPI_C_COMPILER
,MPI_CXX_COMPILER
,MPI_Fortran_COMPILER
, andMPIEXEC_EXECUTABLE
.When a Fortran namemangling scheme is needed (e.g.,
ENABLE_LAPACK
isON
) the build system will infer the scheme from the Fortran compiler. If a Fortran compiler is not available or the inferred or default scheme needs to be overridden, the advanced optionsSUNDIALS_F77_FUNC_CASE
andSUNDIALS_F77_FUNC_UNDERSCORES
can be used to manually set the namemangling scheme and bypass trying to infer the scheme.Parts of the main CMakeLists.txt file were moved to new files in the
src
andexample
directories to make the CMake configuration file structure more modular.
3.1.1.25. Changes in v2.1.2
Updated the minimum required version of CMake to 2.8.12 and enabled using rpath by default to locate shared libraries on OSX.
Fixed Windows specific problem where sunindextype was not correctly
defined when using 64bit integers for the SUNDIALS index type. On Windows
sunindextype is now defined as the MSVC basic type __int64
.
Added sparse SUNMatrix “Reallocate” routine to allow specification of the nonzero storage.
Updated the KLU SUNLinearSolver module to set constants for the two reinitialization types, and fixed a bug in the full reinitialization approach where the sparse SUNMatrix pointer would go out of scope on some architectures.
Updated the “ScaleAdd” and “ScaleAddI” implementations in the sparse SUNMatrix module to more optimally handle the case where the target matrix contained sufficient storage for the sum, but had the wrong sparsity pattern. The sum now occurs inplace, by performing the sum backwards in the existing storage. However, it is still more efficient if the usersupplied Jacobian routine allocates storage for the sum \(I+\gamma J\) or \(M+\gamma J\) manually (with zero entries if needed).
Changed LICENSE install path to instdir/include/sundials
.
3.1.1.26. Changes in v2.1.1
Fixed a potential memory leak in the SPGMR and SPFGMR linear solvers: if “Initialize” was called multiple times then the solver memory was reallocated (without being freed).
Fixed a minor bug in the ARKReInit routine, where a flag was incorrectly set to indicate that the problem had been resized (instead of just reinitialized).
Fixed C++11 compiler errors/warnings about incompatible use of string literals.
Updated KLU SUNLinearSolver module to use a typedef
for the
precisionspecific solve function to be used (to avoid compiler
warnings).
Added missing typecasts for some (void*)
pointers (again, to avoid
compiler warnings).
Bugfix in sunmatrix_sparse.c
where we had used int
instead of
sunindextype
in one location.
Added missing #include <stdio.h>
in NVECTOR and SUNMATRIX header files.
Added missing prototype for ARKSpilsGetNumMTSetups
.
Fixed an indexing bug in the CUDA NVECTOR implementation of
N_VWrmsNormMask
and revised the RAJA NVECTOR implementation of
N_VWrmsNormMask
to work with mask arrays using values other than
zero or one. Replaced double
with realtype
in the RAJA vector
test functions.
Fixed compilation issue with GCC 7.3.0 and Fortran programs that do not require a SUNMatrix or SUNLinearSolver module (e.g. iterative linear solvers, explicit methods, fixed point solver, etc.).
3.1.1.27. Changes in v2.1.0
Added NVECTOR print functions that write vector data to a specified
file (e.g. N_VPrintFile_Serial
).
Added make test
and make test_install
options to the build
system for testing SUNDIALS after building with make
and
installing with make install
respectively.
3.1.1.28. Changes in v2.0.0
All interfaces to matrix structures and linear solvers have been reworked, and all example programs have been updated. The goal of the redesign of these interfaces was to provide more encapsulation and ease in interfacing custom linear solvers and interoperability with linear solver libraries.
Specific changes include:
Added generic SUNMATRIX module with three provided implementations: dense, banded and sparse. These replicate previous SUNDIALS Dls and Sls matrix structures in a single objectoriented API.
Added example problems demonstrating use of generic SUNMATRIX modules.
Added generic SUNLINEARSOLVER module with eleven provided implementations: dense, banded, LAPACK dense, LAPACK band, KLU, SuperLU_MT, SPGMR, SPBCGS, SPTFQMR, SPFGMR, PCG. These replicate previous SUNDIALS generic linear solvers in a single objectoriented API.
Added example problems demonstrating use of generic SUNLINEARSOLVER modules.
Expanded packageprovided direct linear solver (Dls) interfaces and scaled, preconditioned, iterative linear solver (Spils) interfaces to utilize generic SUNMATRIX and SUNLINEARSOLVER objects.
Removed packagespecific, linear solverspecific, solver modules (e.g. CVDENSE, KINBAND, IDAKLU, ARKSPGMR) since their functionality is entirely replicated by the generic Dls/Spils interfaces and SUNLINEARSOLVER/SUNMATRIX modules. The exception is CVDIAG, a diagonal approximate Jacobian solver available to CVODE and CVODES.
Converted all SUNDIALS example problems to utilize new generic SUNMATRIX and SUNLINEARSOLVER objects, along with updated Dls and Spils linear solver interfaces.
Added Spils interface routines to ARKODE, CVODE, CVODES, IDA and IDAS to allow specification of a userprovided “JTSetup” routine. This change supports users who wish to set up data structures for the userprovided Jacobiantimesvector (“JTimes”) routine, and where the cost of one JTSetup setup per Newton iteration can be amortized between multiple JTimes calls.
Two additional NVECTOR implementations were added – one for CUDA and one for RAJA vectors. These vectors are supplied to provide very basic support for running on GPU architectures. Users are advised that these vectors both move all data to the GPU device upon construction, and speedup will only be realized if the user also conducts the righthandside function evaluation on the device. In addition, these vectors assume the problem fits on one GPU. Further information about RAJA, users are referred to the web site, https://software.llnl.gov/RAJA/. These additions are accompanied by additions to various interface functions and to user documentation.
All indices for data structures were updated to a new sunindextype
that can be configured to be a 32 or 64bit integer data index type.
sunindextype
is defined to be int32_t
or int64_t
when
portable types are supported, otherwise it is defined as int
or
long int
. The Fortran interfaces continue to use long int
for
indices, except for their sparse matrix interface that now uses the
new sunindextype
. This new flexible capability for index types
includes interfaces to PETSc, hypre, SuperLU_MT, and KLU with either
32bit or 64bit capabilities depending how the user configures
SUNDIALS.
To avoid potential namespace conflicts, the macros defining
booleantype
values TRUE
and FALSE
have been changed to
SUNTRUE
and SUNFALSE
respectively.
Temporary vectors were removed from preconditioner setup and solve routines for all packages. It is assumed that all necessary data for userprovided preconditioner operations will be allocated and stored in userprovided data structures.
The file include/sundials_fconfig.h
was added. This file contains
SUNDIALS type information for use in Fortran programs.
Added functions SUNDIALSGetVersion and SUNDIALSGetVersionNumber to get SUNDIALS release version information at runtime.
The build system was expanded to support many of the xSDKcompliant keys. The xSDK is a movement in scientific software to provide a foundation for the rapid and efficient production of highquality, sustainable extremescale scientific applications. More information can be found at, https://xsdk.info.
In addition, numerous changes were made to the build system.
These include the addition of separate BLAS_ENABLE
and BLAS_LIBRARIES
CMake variables, additional error checking during CMake configuration,
minor bug fixes, and renaming CMake options to enable/disable examples
for greater clarity and an added option to enable/disable Fortran 77 examples.
These changes included changing ENABLE_EXAMPLES
to ENABLE_EXAMPLES_C
,
changing CXX_ENABLE
to EXAMPLES_ENABLE_CXX
, changing F90_ENABLE
to
EXAMPLES_ENABLE_F90
, and adding an EXAMPLES_ENABLE_F77
option.
Corrections and additions were made to the examples, to installationrelated files, and to the user documentation.
3.1.1.29. Changes in v1.1.0
We have included numerous bugfixes and enhancements since the v1.0.2 release.
The bugfixes include:
For each linear solver, the various solver performance counters are now initialized to 0 in both the solver specification function and in the solver’s
linit
function. This ensures that these solver counters are initialized upon linear solver instantiation as well as at the beginning of the problem solution.The choice of the method vs embedding the Billington and TRBDF2 explicit Runge–Kutta methods were swapped, since in those the lowerorder coefficients result in an Astable method, while the higherorder coefficients do not. This change results in significantly improved robustness when using those methods.
A bug was fixed for the situation where a user supplies a vector of absolute tolerances, and also uses the vector Resize() functionality.
A bug was fixed wherein a usersupplied Butcher table without an embedding is supplied, and the user is running with either fixed time steps (or they do adaptivity manually); previously this had resulted in an error since the embedding order was below 1.
Numerous aspects of the documentation were fixed and/or clarified.
The feature changes/enhancements include:
Two additional NVECTOR implementations were added – one for Hypre (parallel) ParVector vectors, and one for PETSc vectors. These additions are accompanied by additions to various interface functions and to user documentation.
Each NVECTOR module now includes a function,
N_VGetVectorID
, that returns the NVECTOR module name.A memory leak was fixed in the banded preconditioner and bandedblockdiagonal preconditioner interfaces. In addition, updates were done to return integers from linear solver and preconditioner ‘free’ routines.
The Krylov linear solver BiCGstab was enhanced by removing a redundant dot product. Various additions and corrections were made to the interfaces to the sparse solvers KLU and SuperLU_MT, including support for CSR format when using KLU.
The ARKODE implicit predictor algorithms were updated: methods 2 and 3 were improved slightly, a new predictor approach was added, and the default choice was modified.
The underlying sparse matrix structure was enhanced to allow both CSR and CSC matrices, with CSR supported by the KLU linear solver interface. ARKODE interfaces to the KLU solver from both C and Fortran were updated to enable selection of sparse matrix type, and a Fortran90 CSR example program was added.
The missing
ARKSpilsGetNumMtimesEvals()
function was added – this had been included in the previous documentation but had not been implemented.The handling of integer codes for specifying builtin ARKODE Butcher tables was enhanced. While a global numbering system is still used, methods now have #defined names to simplify the user interface and to streamline incorporation of new Butcher tables into ARKODE.
The maximum number of Butcher table stages was increased from 8 to 15 to accommodate very high order methods, and an 8thorder adaptive ERK method was added.
Support was added for the explicit and implicit methods in an additive Runge–Kutta method to utilize different stage times, solution and embedding coefficients, to support new SSPARK methods.
The FARKODE interface was extended to include a routine to set scalar/arrayvalued residual tolerances, to support Fortran applications with nonidentity massmatrices.
3.1.2. Reading this User Guide
This user guide is a combination of general usage instructions and specific example programs. 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.
The structure of this document is as follows:
In the next section we provide a thorough presentation of the underlying mathematical algorithms used within the ARKODE family of solvers.
We follow this with an overview of how the source code for both SUNDIALS and ARKODE are organized.
The largest section follows, providing a full account of how to use ARKODE’s timestepping modules, ARKStep, ERKStep, and MRIStep, within C and C++ applications. This section then includes additional information on how to use ARKODE from applications written in Fortran, as well as information on how to leverage GPU accelerators within ARKODE.
A much smaller section follows, describing ARKODE’s Butcher table structure, that is used by both ARKStep and ERKStep.
Subsequent sections discuss shared SUNDIALS features that are used by ARKODE: vector data structures, matrix data structures, linear solver data structures, nonlinear solver data structures, memory management utilities, and the installation procedure.
The final sections catalog the full set of ARKODE constants, that are used for both input specifications and return codes, and the full set of Butcher tables that are packaged with ARKODE.
3.1.3. SUNDIALS License and Notices
All SUNDIALS packages are released open source, under the BSD 3Clause license for more details see the LICENSE and NOTICE files provided with all SUNDIALS packages.