8.1. Introduction

KINSOL is part of a software family called SUNDIALS: SUite of Nonlinear and DIfferential/ALgebraic equation Solvers [69]. This suite consists of CVODE, ARKODE, KINSOL, and IDA, and variants of these with sensitivity analysis capabilities.

KINSOL is a general-purpose nonlinear system solver based on Newton-Krylov solver technology. A fixed point iteration is also included with the release of KINSOL v.2.8.0 and higher.

8.1.1. Historical Background

The first nonlinear solver packages based on Newton-Krylov methods were written in Fortran. In particular, the NKSOL package, written at LLNL, was the first Newton-Krylov solver package written for solution of systems arising in the solution of partial differential equations [25]. This Fortran code made use of Newton’s method to solve the discrete nonlinear systems and applied a preconditioned Krylov linear solver for solution of the Jacobian system at each nonlinear iteration. The key to the Newton-Krylov method was that the matrix-vector multiplies required by the Krylov method could effectively be approximated by a finite difference of the nonlinear system-defining function, avoiding a requirement for the formation of the actual Jacobian matrix. Significantly less memory was required for the solver as a result.

In the late 1990’s, there was a push at LLNL to rewrite the nonlinear solver in C and port it to distributed memory parallel machines. Both Newton and Krylov methods are easily implemented in parallel, and this effort gave rise to the KINSOL package. KINSOL is similar to NKSOL in functionality, except that it provides for more options in the choice of linear system methods and tolerances, and has a more modular design to provide flexibility for future enhancements.

At present, KINSOL may utilize a variety of Krylov methods provided in SUNDIALS. These methods include the GMRES (Generalized Minimal RESidual) [102], FGMRES (Flexible Generalized Minimum RESidual) [101], Bi-CGStab (Bi-Conjugate Gradient Stabilized) [127], TFQMR (Transpose-Free Quasi-Minimal Residual) [55], and PCG (Preconditioned Conjugate Gradient) [64] linear iterative methods. As Krylov methods, these require little matrix storage for solving the Newton equations as compared to direct methods. However, the algorithms allow for a user-supplied preconditioner, and, for most problems, preconditioning is essential for an efficient solution. For very large nonlinear algebraic 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.

For the sake of completeness in functionality, direct linear system solvers are included in KINSOL. These include methods for both dense and banded linear systems, with Jacobians that are either user-supplied or generated internally by difference quotients. KINSOL also includes interfaces to sparse direct solvers, including KLU [4, 40] and the threaded sparse direct solver, SuperLU_MT [9, 42, 88], among others (see Chapter §11 for further details).

In the process of translating NKSOL into C, the overall KINSOL organization has been changed considerably. One key feature of the KINSOL organization is that a separate module devoted to vector operations was created. This module facilitated extension to multiprosessor environments with minimal impact on the rest of the solver. The vector module design is shared across the SUNDIALS suite. This N_Vector module is written in terms of abstract vector operations with the actual routines attached by a particular implementation (such as serial or parallel) of N_Vector. This abstraction 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 KINSOL) is supplied with serial, MPI-parallel, OpenMP and Pthreads thread-parallel N_Vector implementations, as well as multiple N_Vector implementations designed to leverage GPU architectures (see Chapter §9 for further details).

There are several motivations for choosing the C language for KINSOL. First, a general movement away from Fortran and toward C in scientific computing was apparent. Second, the pointer, structure, and dynamic memory allocation features in C are extremely useful in software of this complexity, with the great variety of method options offered. Finally, we prefer C over C++ for KINSOL because of the wider availability of C compilers, the potentially greater efficiency of C, and the greater ease of interfacing the solver to applications written in Fortran.

8.1.2. Changes from previous versions Changes in v7.0.0

Major Features

SUNDIALS now has more robust and uniform error handling. Non-release builds will be built with additional error checking by default. See §2.5 for details.

Breaking Changes

Minimum C Standard

SUNDIALS now requires using a compiler that supports a subset of the C99 standard. Note with the Microsoft C/C++ compiler the subset of C99 features utilized by SUNDIALS are available starting with Visual Studio 2015.

Deprecated Types and Functions Removed

The previously deprecated types realtype and booleantype were removed from sundials_types.h and replaced with sunrealtype and sunbooleantype. The deprecated names for these types can be used by including the header file sundials_types_deprecated.h but will be fully removed in the next major release. Functions, types, and header files that were previously deprecated have also been removed.

Error Handling Changes

With the addition of the new error handling capability, the functions KINSetErrFile and KINSetHandlerErrFn have been removed. Users of these functions can use the functions SUNContext_PushErrHandler(), and SUNLogger_SetErrorFilename() instead. For further details see Sections §2.5 and §2.7.

In addition the following names/symbols were replaced by SUN_ERR_* codes:


Replaced with SUNErrCode




no replacement (value was unused)

































The following functions have had their signature updated to ensure they can leverage the new SUNDIALS error handling capabilities.

SUNComm Type Added

We have replaced the use of a type-erased (i.e., void*) pointer to a communicator in place of MPI_Comm throughout the SUNDIALS API with a SUNComm, which is just a typedef to an int in builds without MPI and a typedef to a MPI_Comm in builds with MPI. As a result:

  • All users will need to update their codes because the call to SUNContext_Create() now takes a SUNComm instead of type-erased pointer to a communicator. For non-MPI codes, pass SUN_COMM_NULL to the comm argument instead of NULL. For MPI codes, pass the MPI_Comm directly.

  • The same change must be made for calls to SUNLogger_Create() or SUNProfiler_Create().

  • Some users will need to update their calls to N_VGetCommunicator, and update any custom N_Vector implementations that provide N_VGetCommunicator, since it now returns a SUNComm.

The change away from type-erased pointers for SUNComm fixes problems like the one described in GitHub Issue #275.

The SUNLogger is now always MPI-aware if MPI is enabled in SUNDIALS and the SUNDIALS_LOGGING_ENABLE_MPI CMake option and macro definition were removed accordingly.

SUNDIALS Core Library

Users now need to link to sundials_core in addition to the libraries already linked to. This will be picked up automatically in projects that use the SUNDIALS CMake target. The library sundials_generic has been superseded by sundials_core and is no longer available. This fixes some duplicate symbol errors on Windows when linking to multiple SUNDIALS libraries.

Deprecation notice

The functions in sundials_math.h will be deprecated in the next release.

sunrealtype SUNRpowerI(sunrealtype base, int exponent);
sunrealtype SUNRpowerR(sunrealtype base, sunrealtype exponent);
sunbooleantype SUNRCompare(sunrealtype a, sunrealtype b);
sunbooleantype SUNRCompareTol(sunrealtype a, sunrealtype b, sunrealtype tol);
sunrealtype SUNStrToReal(const char* str);

Additionally, the following header files (and everything in them) will be deprecated – users who rely on these are recommended to transition to the corresponding SUNMatrix and SUNLinearSolver modules:


Minor changes

Fixed GitHub Issue #329 so that C++20 aggregate initialization can be used.

Fixed integer overflow in the internal SUNDIALS hashmap. This resolves GitHub Issues #409 and #249.

The CMAKE_BUILD_TYPE defaults to RelWithDebInfo mode now i.e., SUNDIALS will be built with optimizations and debugging symbols enabled by default. Previously the build type was unset by default so no optimization or debugging flags were set.

The advanced CMake options to override the inferred LAPACK name-mangling scheme have been updated from SUNDIALS_F77_FUNC_CASE and SUNDIALS_F77_FUNC_UNDERSCORES to SUNDIALS_LAPACK_CASE and SUNDIALS_LAPACK_UNDERSCORES, respectively.

Converted most previous Fortran 77 and 90 examples to use SUNDIALS’ Fortran 2003 interface. Changes in v6.7.0

Added Fortran support for the LAPACK dense SUNLinearSolver implementation.

Improved computational complexity of SUNMatScaleAddI_Sparse from O(M*N) to O(NNZ).

Fixed scaling bug in SUNMatScaleAddI_Sparse for non-square matrices.

Fixed missing soversions in some SUNLinearSolver and SUNNonlinearSolver CMake targets. Changes in v6.6.2

Fixed the build system support for MAGMA when using a NVIDIA HPC SDK installation of CUDA and fixed the targets used for rocBLAS and rocSPARSE. Changes in v6.6.1

Updated the Tpetra NVector interface to support Trilinos 14.

Fixed a memory leak when destroying a CUDA, HIP, SYCL, or system SUNMemoryHelper object.

Changed the SUNProfiler so that it does not rely on MPI_WTime in any case. This fixes GitHub Issue #312. Changes in v6.6.0

Updated the F2003 utility routines SUNDIALSFileOpen() and SUNDIALSFileClose() to support user specification of stdout and stderr strings for the output file names. Changes in v6.5.1

Fixed build errors when using SuperLU_DIST with ROCM enabled to target AMD GPUs.

Fixed compilation errors in some SYCL examples when using the icx compiler. Changes in v6.5.0

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 the functions KINGetJac() and KINGetJacNumIters() to assist in debugging simulations utilizing a matrix-based linear solver.

Added support for the SYCL backend with RAJA 2022.x.y.

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. Changes in v6.4.1

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. Changes in v6.4.0

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.

Fixed a bug in the CUDA and HIP vectors where N_VMaxNorm() would return the minimum positive floating-point value for the zero vector. Changes in v6.3.0

Added the function KINGetUserData() to retrieve the user data pointer provided to KINSetUserData().

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’. Changes in v6.2.0

Added the SUNLogger API which provides a SUNDIALS-wide mechanism for logging of errors, warnings, informational output, and debugging output.

Deprecated KINSetInfoFile(), KINSetDebugFile(), 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 function KINPrintAllStats() to output all of the nonlinear solver, linear solver, and other statistics in one call. The file scripts/sundials_csv.py contains functions for parsing the comma-separated value output files.

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. Changes in v6.1.1

Fixed exported SUNDIALSConfig.cmake. Changes in v6.1.0

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. Changes in v6.0.0


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 thread-safety. See the documentation section on the SUNContext for more details.

A script upgrade-to-sundials-6-from-5.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:

> ./upgrade-to-sundials-6-from-5.sh <files to update>


A capability to profile/instrument SUNDIALS library code has been added. This can be enabled with the CMake option SUNDIALS_BUILD_WITH_PROFILING. A built-in 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.


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, user-defined 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.


Two new optional vector operations, N_VDotProdMultiLocal() and N_VDotProdMultiAllReduce(), have been added to support low-synchronization 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.


The following previously deprecated functions have been removed:
























































New orthogonalization methods were added for use within the KINSOL Anderson acceleration routine. See §8.2.13 and KINSetOrthAA() for more details.

The KINSOL Fortran 77 interface has been removed. See §2.9 and the F2003 example programs for more details using the SUNDIALS Fortran 2003 module interfaces.


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 (compile-time 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. Changes in v5.8.0

The RAJA N_Vector 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.

New KINSOL options have been added to apply a constant damping in the fixed point and Picard iterations (see KINSetDamping), to delay the start of Anderson acceleration with the fixed point and Picard iterations (see KINSetDelayAA), and to return the newest solution with the fixed point iteration (see KINSetReturnNewest).

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 matrix-vector product setup function pointer was not copied.

A bug was fixed in the SPBCGS and SPTFQMR solvers for the case where a non-zero 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 Picard iteration where the value of KINSetMaxSetupCalls would be ignored. Changes in v5.7.0

A new N_Vector 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. Changes in v5.6.1

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. Changes in v5.6.0

A new N_Vector 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 N_Vector 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. Changes in v5.5.0

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. Changes in v5.4.0

A new API, SUNMemoryHelper, was added to support GPU users who have complex memory management needs such as using memory pools. This is paired with new constructors for the NVECTOR_CUDA and NVECTOR_RAJA modules that accept a SUNMemoryHelper object. Refer to §2.10.1, §9.15, §9.18, and §14 for more information.

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. Changes in v5.3.0

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 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.

Added the optional function KINSetJacTimesVecSysFn to specify an alternative system function for computing Jacobian-vector products with the internal difference quotient approximation. Changes in v5.2.0

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 linkage bug affecting Windows users that stemmed from dllimport/dllexport attributes missing on some SUNDIALS API functions.

Added a new SUNMatrix implementation, SUNMATRIX_CUSPARSE, that interfaces to the sparse matrix implementation from the NVIDIA cuSPARSE library. In addition, the SUNLINSOL_CUSOLVER_BATCHQR linear solver has been updated to use this matrix, therefore, 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. Changes in v5.1.0

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.

Added two utility functions, SUNDIALSFileOpen and SUNDIALSFileClose for creating/destroying file pointers that are useful when using the Fortran 2003 interfaces.

Added support for constant damping when using Anderson acceleration. See §8.2 and the description of the KINSetDampingAA function for more details. Changes in v5.0.0 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 NVECTOR_PTHREADS module from being built. NVECTOR module changes

  • Two new functions were added to aid in creating custom N_Vector objects. The constructor N_VNewEmpty allocates an “empty” generic N_Vector 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 N_Vector API by ensuring only required operations need to be set. Additionally, the function N_VCopyOps(w, v) 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 N_Vector API by ensuring all operations are copied when cloning objects. See §9.1.1 for more details.

  • Two new N_Vector 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 multi-physics problems that couple distinct MPI-based simulations together. This implementation is accompanied by additions to user documentation and SUNDIALS examples. See §9.22 and §9.23 for more details.

  • One new required vector operation and ten new optional vector operations have been added to the N_Vector 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 N_Vector implementation defines any of the local operations as NULL, then the NVECTOR_MPIMANYVECTOR will call standard N_Vector operations to complete the computation. See §9.2.4 for more details.

  • An additional N_Vector implementation, NVECTOR_MPIPLUSX, has been created to support the MPI+X paradigm where X is a type of on-node parallelism (e.g., OpenMP, CUDA). The implementation is accompanied by additions to user documentation and SUNDIALS examples. See §9.24 for more details.

  • 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 non-default 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. See §9.15.1 for more details.

  • Added new Fortran 2003 interfaces for most N_Vector modules. See Chapter §9 for more details on how to use the interfaces.

  • Added three new N_Vector utility functions, FN_VGetVecAtIndexVectorArray, FN_VSetVecAtIndexVectorArray, and FN_VNewVectorArray, for working with N_Vector arrays when using the Fortran 2003 interfaces. See §9.1.1 for more details. 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(A, B) 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. See §10.1 for more details.

  • A new operation, SUNMatMatvecSetup, was added to the SUNMatrix API to perform any setup necessary for computing a matrix-vector product. This operation is useful for SUNMatrix implementations which need to prepare the matrix itself, or communication structures before performing the matrix-vector 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. See §10.2 for more details.

  • 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. See “SUNMatrix Error Codes” for more details.

  • A new SUNMatrix (and SUNLinearSolver) implementation was added to facilitate the use of the SuperLU_DIST library with SUNDIALS. See §10.15 for more details.

  • Added new Fortran 2003 interfaces for most SUNMatrix modules. See Chapter §10 for more details on how to use the interfaces. 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. See §11.1.8 for more details.

  • 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. See §11.20 for more details.

  • 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. See §11.22 for more details.

  • 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. See §11.10.1 for more details.

  • Added new Fortran 2003 interfaces for most SUNLinearSolver modules. See Chapter §11 for more details on how to use the interfaces. KINSOL changes

  • Fixed a bug in the KINSOL linear solver interface where the auxiliary scalar sJpnorm was not computed when necessary with the Picard iteration and the auxiliary scalar sFdotJp was unnecessarily computed in some cases.

  • The KINLS interface has been updated to only zero the Jacobian matrix before calling a user-supplied Jacobian evaluation function when the attached linear solver has type SUNLINEARSOLVER_DIRECT.

  • Added a Fortran 2003 interface to KINSOL. See §2.9 for more details. Changes in v4.1.0

An additional N_Vector 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.

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 N_Vector is enabled).

The implementation header file kin_impl.h is no longer installed. This means users who are directly manipulating the KINMem structure will need to update their code to use KINSOL’s public API.

Python is no longer required to run make test and make test_install. Changes in v4.0.2

Added information on how to contribute to SUNDIALS and a contributing agreement.

Moved definitions of DLS and SPILS backwards compatibility functions to a source file. The symbols are now included in the KINSOL library, libsundials_kinsol. Changes in v4.0.1

No changes were made in this release. Changes in v4.0.0

KINSOL’s previous direct and iterative linear solver interfaces, KINDls and KINSpils, have been merged into a single unified linear solver interface, KINLs, to support any valid SUNLinearSolver module. This includes the “DIRECT” and “ITERATIVE” types as well as the new “MATRIX_ITERATIVE” type. Details regarding how KINLs utilizes linear solvers of each type as well as discussion regarding intended use cases for user-supplied SUNLinearSolver implementations are included in Chapter §11. All KINSOL example programs and the standalone linear solver examples have been updated to use the unified linear solver interface.

The unified interface for the new KINLs module is very similar to the previous KINDls and KINSpils interfaces. To minimize challenges in user migration to the new names, the previous C and Fortran routine names may still be used; these will be deprecated in future releases, so we recommend that users migrate to the new names soon. Additionally, we note that Fortran users, however, may need to enlarge their iout array of optional integer outputs, and update the indices that they query for certain linear-solver-related statistics.

The names of all constructor routines for SUNDIALS-provided SUNLinearSolver 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. Solver-specific “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 KINSOL 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.

Three fused vector operations and seven vector array operations have been added to the N_Vector API. These optional operations are disabled by default and may be activated by calling vector specific routines after creating an N_Vector (see Chapter §9 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 N_Vector implementation defines any of these operations as NULL, then standard N_Vector operations will automatically be called as necessary to complete the computation. Multiple updates to NVECTOR_CUDA were made:

  • 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.

  • Changed the N_VMake_Cuda function to take a host data pointer and a device data pointer instead of an N_VectorContent_Cuda object.

  • Added the ability to set the cudaStream_t used for execution of the NVECTOR_CUDA kernels. See the function N_VSetCudaStreams_Cuda.

  • Added N_VNewManaged_Cuda, N_VMakeManaged_Cuda, and N_VIsManagedMemory_Cuda functions to accommodate using managed memory with the NVECTOR_CUDA.

Multiple changes to NVECTOR_RAJA 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 suncudavec.

A new N_Vector implementation for leveraging OpenMP 4.5+ device offloading has been added, NVECTOR_OPENMPDEV. See §9.20 for more details. Changes in v3.2.1

The changes in this minor release include the following:

  • Fixed a bug in the CUDA N_Vector 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. Changes in v3.2.0

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 N_Vector 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 the SUNDIALS_INDEX_SIZE CMake option to select the sunindextype 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, and MPIEXEC_EXECUTABLE.

  • When a Fortran name-mangling scheme is needed (e.g., ENABLE_LAPACK is ON) 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 options SUNDIALS_F77_FUNC_CASE and SUNDIALS_F77_FUNC_UNDERSCORES can be used to manually set the name-mangling scheme and bypass trying to infer the scheme.

  • Parts of the main CMakeLists.txt file were moved to new files in the src and example directories to make the CMake configuration file structure more modular. Changes in v3.1.2

The changes in this minor release include the following:

  • 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 64-bit 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 in-place, by performing the sum backwards in the existing storage. However, it is still more efficient if the user-supplied Jacobian routine allocates storage for the sum \(I+\gamma J\) manually (with zero entries if needed).

  • Changed the LICENSE install path to instdir/include/sundials. Changes in v3.1.1

The changes in this minor release include the following:

  • 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).

  • Updated KLU SUNLinearSolver module to use a typedef for the precision-specific 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.

  • Fixed a minor bug in KINPrintInfo where a case was missing for KIN_REPTD_SYSFUNC_ERR leading to an undefined info message.

  • Added missing #include <stdio.h> in N_Vector and SUNMatrix header files.

  • Fixed an indexing bug in the CUDA N_Vector implementation of N_VWrmsNormMask and revised the RAJA N_Vector 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 or fixed pointer solver).

In addition to the changes above, minor corrections were also made to the example programs, build system, and user documentation. Changes in v3.1.0

Added N_Vector 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. Changes in v3.0.0

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 the interfacing of 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 object-oriented API.

  • Added example problems demonstrating use of generic SUNMATRIX modules.

  • Added generic SUNLinearSolver module with eleven provided implementations: SUNDIALS native dense, SUNDIALS native banded, LAPACK dense, LAPACK band, KLU, SuperLU_MT, SPGMR, SPBCGS, SPTFQMR, SPFGMR, and PCG. These replicate previous SUNDIALS generic linear solvers in a single object-oriented API.

  • Added example problems demonstrating use of generic SUNLINEARSOLVER modules.

  • Expanded package-provided direct linear solver (Dls) interfaces and scaled, preconditioned, iterative linear solver (Spils) interfaces to utilize generic SUNMATRIX and SUNLINEARSOLVER objects.

  • Removed package-specific, linear solver-specific, 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 user-provided “JTSetup” routine. This change supports users who wish to set up data structures for the user-provided Jacobian-times-vector (“JTimes”) routine, and where the cost of one JTSetup setup per Newton iteration can be amortized between multiple JTimes calls.

Two additional N_Vector 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 right-hand-side function evaluation on the device. In addition, these vectors assume the problem fits on one GPU. Further information about RAJA, users are referred to th 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 64-bit 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 32-bit or 64-bit 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 user-provided preconditioner operations will be allocated and stored in user-provided data structures.

The file include/sundials_fconfig.h was added. This file contains SUNDIALS type information for use in Fortran programs.

The build system was expanded to support many of the xSDK-compliant keys. The xSDK is a movement in scientific software to provide a foundation for the rapid and efficient production of high-quality, sustainable extreme-scale scientific applications. More information can be found at, https://xsdk.info.

Added functions SUNDIALSGetVersion and SUNDIALSGetVersionNumber to get SUNDIALS release version information at runtime.

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 EXAMPLES_ENABLE to EXAMPLES_ENABLE_C, changing CXX_ENABLE to EXAMPLES_ENABLE_CXX, changing F90_ENABLE to EXAMPLES_ENABLE_F90, and adding an EXAMPLES_ENABLE_F77 option.

A bug fix was done to correct the fcmix name translation for FKIN_SPFGMR.

Corrections and additions were made to the examples, to installation-related files, and to the user documentation. Changes in v2.9.0

Two additional N_Vector implementations were added – one for Hypre (parallel) vectors, and one for PETSc vectors. These additions are accompanied by additions to various interface functions and to user documentation.

Each N_Vector module now includes a function, N_VGetVectorID, that returns the N_Vector module name.

The Picard iteration return was chanegd to always return the newest iterate upon success. A minor bug in the line search was fixed to prevent an infinite loop when the beta condition fails and lamba is below the minimum size.

For each linear solver, the various solver performance counters are now initialized to 0 in both the solver specification function and in solver linit function. This ensures that these solver counters are initialized upon linear solver instantiation as well as at the beginning of the problem solution.

A memory leak was fixed in the banded preconditioner interface. In addition, updates were done to return integers from linear solver and preconditioner ’free’ functions.

Corrections were made to three Fortran interface functions. The Anderson acceleration scheme was enhanced by use of QR updating.

The Krylov linear solver Bi-CGstab 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 functions FKINCREATE and FKININIT were added to split the FKINMALLOC routine into two pieces. FKINMALLOC remains for backward compatibility, but documentation for it has been removed.

A new examples was added for use of the OpenMP vector.

Minor corrections and additions were made to the KINSOL solver, to the Fortran interfaces, to the examples, to installation-related files, and to the user documentation. Changes in v2.8.0

Two major additions were made to the globalization strategy options (KINSol argument strategy). One is fixed-point iteration, and the other is Picard iteration. Both can be accelerated by use of the Anderson acceleration method. See the relevant paragraphs in Chapter §8.2.

Three additions were made to the linear system solvers that are available for use with the KINSOL solver. First, in the serial case, an interface to the sparse direct solver KLU was added. Second, an interface to SuperLU_MT, the multi-threaded version of SuperLU, was added as a thread-parallel sparse direct solver option, to be used with the serial version of the N_Vector module. As part of these additions, a sparse matrix (CSC format) structure was added to KINSOL. Finally, a variation of GMRES called Flexible GMRES was added.

Otherwise, only relatively minor modifications were made to KINSOL:

In function KINStop, two return values were corrected to make the values of uu and fval consistent.

A bug involving initialization of mxnewtstep was fixed. The error affects the case of repeated user calls to KINSol with no intervening call to KINSetMaxNewtonStep.

A bug in the increments for difference quotient Jacobian approximations was fixed in function kinDlsBandDQJac.

In KINLapackBand, the line smu = MIN(N-1,mu+ml) was changed to smu = mu + ml to correct an illegal input error for DGBTRF/DGBTRS.

In order to avoid possible name conflicts, the mathematical macro and function names MIN, MAX, SQR, RAbs, RSqrt, RExp, RPowerI, and RPowerR were changed to SUNMIN, SUNMAX, SUNSQR, SUNRabs, SUNRsqrt, SUNRexp, SRpowerI, and SUNRpowerR, respectively. These names occur in both the solver and in various example programs.

In the FKINSOL module, an incorrect return value ier in FKINfunc was fixed.

In the FKINSOL optional input routines FKINSETIIN, FKINSETRIN, and FKINSETVIN, the optional fourth argument key_length was removed, with hardcoded key string lengths passed to all strncmp tests.

In all FKINSOL examples, integer declarations were revised so that those which must match a C type long int are declared INTEGER*8, and a comment was added about the type match. All other integer declarations are just INTEGER. Corresponding minor corrections were made to the user guide.

Two new N_Vector modules have been added for thread-parallel computing environments — one for OpenMP, denoted NVECTOR_OPENMP, and one for Pthreads, denoted NVECTOR_PTHREADS.

With this version of SUNDIALS, support and documentation of the Autotools mode of installation is being dropped, in favor of the CMake mode, which is considered more widely portable. Changes in v2.7.0

One significant design change was made with this release: The problem size and its relatives, bandwidth parameters, related internal indices, pivot arrays, and the optional output lsflag have all been changed from type int to type long int, except for the problem size and bandwidths in user calls to routines specifying BLAS/LAPACK routines for the dense/band linear solvers. The function NewIntArray is replaced by a pair NewIntArray/NewLintArray, for int and long int arrays, respectively.

A large number of errors have been fixed. Three major logic bugs were fixed – involving updating the solution vector, updating the linesearch parameter, and a missing error return. Three minor errors were fixed – involving setting etachoice in the Matlab/KINSOL interface, a missing error case in KINPrintInfo, and avoiding an exponential overflow in the evaluation of omega. In each linear solver interface function, the linear solver memory is freed on an error return, and the **Free function now includes a line setting to NULL the main memory pointer to the linear solver memory. In the installation files, we modified the treatment of the macro SUNDIALS_USE_GENERIC_MATH, so that the parameter GENERIC_MATH_LIB is either defined (with no value) or not defined. Changes in v2.6.0

This release introduces a new linear solver module, based on BLAS and LAPACK for both dense and banded matrices.

The user interface has been further refined. Some of the API changes involve: (a) a reorganization of all linear solver modules into two families (besides the already present family of scaled preconditioned iterative linear solvers, the direct solvers, including the new LAPACK-based ones, were also organized into a direct family); (b) maintaining a single pointer to user data, optionally specified through a Set-type function; (c) a general streamlining of the band-block-diagonal preconditioner module distributed with the solver. Changes in v2.5.0

The main changes in this release involve a rearrangement of the entire SUNDIALS source tree (see §8.3). At the user interface level, the main impact is in the mechanism of including SUNDIALS header files which must now include the relative path (e.g. #include <cvode/cvode.h>). Additional changes were made to the build system: all exported header files are now installed in separate subdirectories of the installation include directory.

The functions in the generic dense linear solver (sundials_dense and sundials_smalldense) were modified to work for rectangular \(m \times n\) matrices (\(m \le n\)), while the factorization and solution functions were renamed to DenseGETRF/denGETRF and DenseGETRS/denGETRS, respectively. The factorization and solution functions in the generic band linear solver were renamed BandGBTRF and BandGBTRS, respectively. Changes in v2.4.0

KINSPBCG, KINSPTFQMR, KINDENSE, and KINBAND modules have been added to interface with the Scaled Preconditioned Bi-CGStab (SPBCG), Scaled Preconditioned Transpose-Free Quasi-Minimal Residual (SPTFQMR), DENSE, and BAND linear solver modules, respectively. (For details see Chapter :numref:KINSOL.Usage.CC.) Corresponding additions were made to the Fortran interface module FKINSOL. At the same time, function type names for Scaled Preconditioned Iterative Linear Solvers were added for the user-supplied Jacobian-times-vector and preconditioner setup and solve functions.

Regarding the Fortran interface module FKINSOL, optional inputs are now set using FKINSETIIN (integer inputs), FKINSETRIN (real inputs), and FKINSETVIN (vector inputs). Optional outputs are still obtained from the IOUT and ROUT arrays which are owned by the user and passed as arguments to FKINMALLOC.

The KINDENSE and KINBAND linear solver modules include support for nonlinear residual monitoring which can be used to control Jacobian updating.

To reduce the possibility of conflicts, the names of all header files have been changed by adding unique prefixes (kinsol_ and sundials_). When using the default installation procedure, the header files are exported under various subdirectories of the target include directory. For more details see Appendix §2.1. Changes in v2.3.0

The user interface has been further refined. Several functions used for setting optional inputs were combined into a single one. Additionally, to resolve potential variable scope issues, all SUNDIALS solvers release user data right after its use. The build system has been further improved to make it more robust. Changes in v2.2.1

The changes in this minor SUNDIALS release affect only the build system. Changes in v2.2.0

The major changes from the previous version involve a redesign of the user interface across the entire SUNDIALS suite. We have eliminated the mechanism of providing optional inputs and extracting optional statistics from the solver through the iopt and ropt arrays. Instead, KINSOL now provides a set of routines (with prefix KINSet) to change the default values for various quantities controlling the solver and a set of extraction routines (with prefix KINGet) to extract statistics after return from the main solver routine. Similarly, each linear solver module provides its own set of Set- and Get-type routines. For more details see Chapter :numref:KINSOL.Usage.CC.

Additionally, the interfaces to several user-supplied routines (such as those providing Jacobian-vector products and preconditioner information) were simplified by reducing the number of arguments. The same information that was previously accessible through such arguments can now be obtained through Get-type functions.

Installation of KINSOL (and all of SUNDIALS) has been completely redesigned and is now based on configure scripts.

8.1.3. Reading this User Guide

This user guide is a combination of general usage instructions and specific examples. 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 KINSOL. The most casual user, with a small nonlinear system, can get by with reading all of Chapter §8.2, then Chapter :numref:KINSOL.Usage.CC through §8.4 only, and looking at examples in [39]. In a different direction, a more expert user with a nonlinear system may want to (a) use a package preconditioner (§8.4.5), (b) supply his/her own Jacobian or preconditioner routines (§8.4.4), (c) supply a new N_Vector module (Chapter §9), or even (d) supply a different linear solver module (§ and Chapter §11).

The structure of this document is as follows:

  • In Chapter §8.2, we provide short descriptions of the numerical methods implemented by KINSOL for the solution of nonlinear systems.

  • The following chapter describes the structure of the SUNDIALS suite of solvers (§8.3) and the software organization of the KINSOL solver (§8.3.1).

  • Chapter :numref:KINSOL.Usage.CC is the main usage document for KINSOL for C applications. It includes a complete description of the user interface for the solution of nonlinear algebraic systems.

  • Chapter §9 gives a brief overview of the generic N_Vector module shared among the various components of SUNDIALS, and details on the four N_Vector implementations provided with SUNDIALS.

  • Chapter §10 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.

  • Chapter §11 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.

  • Finally, in the appendices, we provide detailed instructions for the installation of KINSOL, within the structure of SUNDIALS (Appendix §2.1), as well as a list of all the constants used for input to and output from KINSOL functions (Appendix §8.5).

Finally, the reader should be aware of the following notational conventions in this user guide: program listings and identifiers (such as KINInit) within textual explanations appear in typewriter type style; fields in C structures (such as content) appear in italics; and packages or modules are written in all capitals. Usage and

8.1.4. 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.

8.1.5. Acknowledgments

We wish to acknowledge the contributions to previous versions of the KINSOL code and user guide by Allan G. Taylor.