SpECTRE
v2022.05.05

In SpECTRE, sets of contiguous or related data are stored in specializations of vector data types. The canonical implementation of this is the DataVector
, which is used for storage of a contiguous sequence of doubles which support a wide variety of mathematical operations and represent data on a grid used during an evolution or elliptic solve. However, we support the ability to easily generate similar vector types which can hold data of a different type (e.g. std::complex<double>
), or support a different set of mathematical operations. SpECTRE vector classes are derived from the class template VectorImpl
. The remainder of this brief guide gives a description of the tools for defining additional vector types.
For reference, all functions described here can also be found in brief in the Doxygen documentation for VectorImpl.hpp, and a simple reference implementation can be found in DataVector.hpp and DataVector.cpp.
SpECTRE vector types inherit from vector types implemented in the highperformance arithmetic library Blaze. Using inheritance, SpECTRE vectors gracefully make use of the math functions defined for the Blaze types, but can be customized for the specific needs in SpECTRE computations.
The pair of template parameters for VectorImpl
are the type of the stored data (e.g. double
for DataVector
), and the result type for mathematical operations. The result type is used by Blaze to ensure that only compatible vector types are used together in mathematical expressions. For example, a vector representing double
data on a grid (DataVector
) cannot be added to a vector representing spectral coefficients (ModalVector
). This avoids subtle bugs that arise when vector types are unintentionally mixed. In nearly all cases the result type will be the vector type that is being defined, so, for instance, DataVector
is a derived class of VectorImpl<double, DataVector>
. This template pattern is known as the ["Curiously Recurring Template Pattern"] (https://en.wikipedia.org/wiki/Curiously_recurring_template_pattern) (CRTP).
For the Blaze system to use the CRTP inheritance appropriately, it requires the specification of separate type traits in the blaze
namespace. These traits can usually be declared in a standard form, so are abstracted in a macro. For any new vector MyNewVector
, the pattern that must appear at the beginning of the file (i.e. before the class definition) is:
The class template VectorImpl
defines various constructors, assignment operators, and iterator generation members. Most of these are inherited from Blaze types, but in addition, the methods set_data_ref
, and pup
are defined for use in SpECTRE. All except for the assignment operators and constructors will be implicitly inherited from VectorImpl
. The assignment and constructors may be inherited calling the following alias code in the vector class definition:
Only the mathematical operations supported on the base Blaze types are supported by default. Those operations are determined by the storage type T
and by the Blaze library. See [blazewiki/Vector_Operations] (https://bitbucket.org/blazelib/blaze/wiki/Vector%20Operations).
Blaze keeps track of the return type of unary and binary operations using "type
trait" structs. These specializations for vector types should be placed in the header file associated with the VectorImpl
specialization. For DataVector
, the specializations are defined in DataStructures/DataVector.hpp
. The presence or absence of template specializations of these structs also determines the set of allowed operations between the vector type and other types. For example, if adding a double
to a DataVector
should be allowed and the result should be treated as a DataVector
for subsequent operations, then the struct blaze::AddTrait<DataVector, double>
needs to be defined as follows:
Note that this only adds support for DataVector + double
, not double + DataVector
. To get the latter the following AddTrait specialization must be defined
Four helper macros are defined to assist with generating the many specializations that binary operations may require. Both of these macros must be put inside the blaze namespace for them to work correctly.
The first of these helper macros is BLAZE_TRAIT_SPECIALIZE_BINARY_TRAIT(VECTOR_TYPE, BLAZE_MATH_TRAIT)
, which will define all of the pairwise operations (BLAZE_MATH_TRAIT
) for the vector type (VECTOR_TYPE
) with itself and for the vector type with its value_type
. This reduces the three specializations similar to the above code blocks to a single line call,
The second helper macro is provided to easily define all of the arithmetic operations that will typically be supported for a vector type with its value type. The macro is VECTOR_BLAZE_TRAIT_SPECIALIZE_ARITHMETIC_TRAITS(VECTOR_TYPE)
, and defines all of:
IsVector<VECTOR_TYPE>
to std::true_type
TransposeFlag<VECTOR_TYPE>
, which informs Blaze of the interpretation of the data as a "column" or "row" vectorAddTrait
for the VECTOR_TYPE
and its value type (3 Blaze struct specializations)SubTrait
for the VECTOR_TYPE
and its value type (3 Blaze struct specializations)MultTrait
for the VECTOR_TYPE
and its value type (3 Blaze struct specializations)DivTrait
for the VECTOR_TYPE
and its value type (3 Blaze struct specializations)This macro is similarly intended to be used in the blaze
namespace and can substantially simplify these specializations for new vector types. For instance, the call for DataVector
is:
The third helper macro is provided to define a combination of Blaze traits for symmetric operations of a vector type with a second type (which may or may not be a vector type). The macro is BLAZE_TRAIT_SPECIALIZE_COMPATIBLE_BINARY_TRAIT(VECTOR, COMPATIBLE, TRAIT)
, and defines the appropriate trait for the two combinations <VECTOR, COMPATIBLE>
and <COMPATIBLE, VECTOR>
, and defines the result type to be VECTOR
. For instance, to support the multiplication of a ComplexDataVector
with a DataVector
and have the result be a ComplexDataVector
, the following macro call should be included in the blaze
namespace:
Finally, the fourth helper macro is provided to define all of the blaze traits which are considered either unary or binary maps. This comprises most named unary functions (like sin()
or sqrt()
) and named binary functions (like hypot()
and atan2()
). The macro VECTOR_BLAZE_TRAIT_SPECIALIZE_ALL_MAP_TRAITS(VECTOR_TYPE)
broadly specializes all blazedefined maps in which the given VECTOR_TYPE
as the sole argument (for unary maps) or both arguments (for binary maps). This macro is also intended to be used in the blaze namespace. The call for DataVector
is:
In addition to operations between SpECTRE vectors, it is useful to gracefully handle operations between std::arrays
of vectors elementwise. There are general macros defined for handling operations between array specializations: DEFINE_STD_ARRAY_BINOP
and DEFINE_STD_ARRAY_INPLACE_BINOP
from Utilities/StdArrayHelpers.hpp
.
In addition, there is a macro for rapidly generating addition and subtraction between arrays of vectors and arrays of their data types. The macro MAKE_STD_ARRAY_VECTOR_BINOPS(VECTOR_TYPE)
will define:
+
and 
with std::array<VECTOR_TYPE, N>
and std::array<VECTOR_TYPE, N>
+
and 
of either ordering of std::array<VECTOR_TYPE, N>
with std::array<VECTOR_TYPE::value_type, N>
+=
and =
of std::array<VECTOR_TYPE, N>
with a std::array<VECTOR_TYPE, N>
+=
and =
of std::array<VECTOR_TYPE, N>
with a std::array<VECTOR_TYPE::value_type, N>
.Equivalence operators are supported by the Blaze type inheritance. The equivalence operator ==
evaluates to true on a pair of vectors if they are the same size and contain the same values, regardless of ownership.
SpECTRE offers the convenience function make_with_value
for various types. The typical behavior for a SpECTRE vector type is to create a new vector type of the same type and length initialized with the value provided as the second argument in all entries. This behavior may be created by placing the macro MAKE_WITH_VALUE_IMPL_DEFINITION_FOR(VECTOR_TYPE)
in the .hpp file. Any other specializations of MakeWithValueImpl
will need to be written manually.
When additional vector types are added, small changes are necessary if they are to be used as the base container type either for Tensor
s or for Variables
(a Variables
contains Tensor
s), which contain some vector type.
In Tensor.hpp
, there is a static_assert
which whitelists the possible types that can be used as the storage type in Tensor
s. Any new vectors must be added to that whitelist if they are to be used within Tensor
s.
Variables
is templated on the storage type of the stored Tensor
s. However, any new data type should be appropriately tested. New vector types should be tested by invoking new versions of existing testing functions templated on the new vector type, rather than DataVector
.
In addition to the utilities for generating new vector types, there are a number of convenience functions and utilities for easily generating the tests necessary to verify that the vectors function appropriately. These utilities are in VectorImplTestHelper.hpp
, and documented individually in the TestingFrameworkGroup. Presented here are the salient details for rapidly assembling basic tests for vectors.
Each of these functions is intended to encapsulate a single frequently used unit test and is templated (in order) on the vector type and the value type to be generated. The default behavior is to uniformly sample values between 100 and 100, but alternative bounds may be passed in via the function arguments.
This function tests a battery of construction and assignment operators for the vector type.
This function tests that vector types can be serialized and deserialized, retaining their data.
This function tests the set_data_ref
method of sharing data between vectors, and that the appropriate owning flags and move operations are handled correctly.
Tests several combinations of math operations and ownership before and after use of std::move
.
This function intentionally generates an error when assigning values from one vector to a differently sized, nonowning vector (made nonowning by use of set_data_ref
). The assertion test which calls this function should search for the string "Must copy into same size". Three forms of the test are provided, which are switched between using a value from the enum RefSizeErrorTestKind
in the first function argument:
RefSizeErrorTestKind::Copy
: tests that the size error is appropriately generated when copying to a nonowning vector of the wrong size.RefSizeErrorTestKind::ExpressionAssign
: tests that the size error is appropriately generated when assigning the result of a mathematical expression to a nonowning vector of the wrong size.RefSizeErrorTestKind::Move
: tests that the size error is appropriately generated when a vector is std::move
d into a nonowning vector of the wrong sizeThis is a general function for testing the mathematical operation of vector types with other vector types and/or their base types, with or without various reference wrappers. This may be used to efficiently test the full set of permitted math operations on a vector. See the documentation of test_functions_with_vector_arguments()
for full usage details.
An example simple use case for the math test utility:
More use cases of this functionality can be found in Test_DataVector.cpp
.
Internally, all vector classes inherit from the templated VectorImpl
, which inherits from a blaze::CustomVector
. Most of the mathematical operations are supported through the Blaze inheritance, which ensures that the math operations execute the optimized forms in Blaze.
Blaze also offers the possibility of restricting operations via groups
in the blaze::CustomVector
template arguments. Currently, we do not use the blaze::GroupTag
functionality to determine available operations for vectors, but in principle this feature could allow us to further simplify our operator choice logic in the SpECTRE vector code.
SpECTRE vectors can be either "owning" or "nonowning". If a vector is owning, it allocates and controls the data it has access to, and is responsible for eventually freeing that data when the vector goes out of scope. If the vector is nonowning, it acts as a (possibly complete) "view" of otherwise allocated memory. Nonowning vectors do not manage memory, nor can they change size. The two cases of data ownership cause the underlying data to be handled fairly differently, so we will discuss each in turn.
When a SpECTRE vector is constructed as owning, or becomes owning, it allocates its own block of memory of appropriate size, and stores a pointer to that memory in a std::unique_ptr
named owned_data_
. The std::unique_ptr
ensures that the SpECTRE vector needs to perform no further direct memory management, and that the memory will be appropriately managed whenever the std::unique_ptr owned_data_
member is deleted or moved. The base blaze::CustomVector
must also be told about the pointer, which is always accomplished by calling the protected function VectorImpl.reset_pointer_vector(const size_t set_size)
, which sets the blaze::CustomVector
internal pointer to the pointer obtained by std::unique_pointer.get()
.
When a SpECTRE vector is constructed as nonowning by the VectorImpl(ValueType*
start, size_t set_size)
constructor, or becomes nonowning by the set_data_ref
function, the internal std::unique_ptr
named owned_data_
no longer points to the data represented by the vector and can be thought of as "inactive" for the purposes of computation and memory management. This behavior is desirable, because otherwise the std::unique_ptr
would attempt to free memory that is presumed to be also used elsewhere, causing difficult to diagnose memory errors. The nonowning SpECTRE vector updates the base blaze::CustomVector
pointer directly by calling blaze::CustomVector.reset
from the derived class (on itself).