#include <tv2fastiter.h>
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class | SliceInfo |
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struct | tvresult |
| For an iterator, the vectorized result for width N is always a TinyVector<T_numtype, N>. More...
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| FastTV2IteratorBase (const T_iterator &x) |
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void | operator= (const T_iterator &x) |
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| FastTV2IteratorBase (const T_vector &array) |
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| ~FastTV2IteratorBase () |
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T_numtype | operator() (int i) const |
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T_result | operator() (TinyVector< int, 1 > i) const |
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int | ascending (const int r) const |
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int | ordering (const int r) const |
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int | lbound (const int r) const |
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int | ubound (const int r) const |
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T_result | first_value () const |
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T_result | operator* () const |
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T_result | operator[] (int i) const |
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T_result | fastRead (diffType i) const |
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template<int N> |
tvresult< N >::Type | fastRead_tv (diffType i) const |
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bool | isVectorAligned (diffType offset) const |
| Since data_ is simd aligned by construction, we just have to check the offest.
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int | suggestStride (int r) const |
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bool | isStride (int r, diffType stride) const |
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void | push (int position) |
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void | pop (int position) |
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void | advance () |
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void | advance (int n) |
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void | loadStride (int r) |
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const T_numtype *restrict | data () const |
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const T_vector & | array () const |
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void | _bz_setData (const T_numtype *ptr) |
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void | _bz_offsetData (sizeType i) |
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void | _bz_offsetData (sizeType offset, int dim) |
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void | _bz_offsetData (sizeType offset1, int dim1, sizeType offset2, int dim2) |
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int | stride () const |
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bool | isUnitStride (int r) const |
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bool | isUnitStride () const |
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void | advanceUnitStride () |
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bool | canCollapse (int outerLoopRank, int innerLoopRank) const |
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template<typename T_shape> |
bool | shapeCheck (const T_shape &s) const |
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T_result | shift (int offset, int dim) const |
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T_result | shift (int offset1, int dim1, int offset2, int dim2) const |
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void | prettyPrint (std::string &str, prettyPrintFormat &format) const |
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◆ T_ctorArg1
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_ctorArg2
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_iterator
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_numtype
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_optype
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_range_result
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_result
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_tvresult
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_tvtypeprop
template<typename P_numtype, int N_length, typename P_arraytype>
Result type for fastRead_tv is a FastTVIterator.
This is only used for mixed TV/Array expressions.
◆ T_typeprop
template<typename P_numtype, int N_length, typename P_arraytype>
◆ T_vector
template<typename P_numtype, int N_length, typename P_arraytype>
◆ FastTV2IteratorBase() [1/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ FastTV2IteratorBase() [2/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ ~FastTV2IteratorBase()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ _bz_offsetData() [1/3]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ _bz_offsetData() [2/3]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ _bz_offsetData() [3/3]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ _bz_setData()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ advance() [1/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ advance() [2/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ advanceUnitStride()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ array()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ ascending()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ canCollapse()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ data()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ fastRead()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ fastRead_tv()
template<typename P_numtype, int N_length, typename P_arraytype>
template<int N>
◆ first_value()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ isStride()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ isUnitStride() [1/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ isUnitStride() [2/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ isVectorAligned()
template<typename P_numtype, int N_length, typename P_arraytype>
Since data_ is simd aligned by construction, we just have to check the offest.
◆ lbound()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ loadStride()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ operator()() [1/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ operator()() [2/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ operator*()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ operator=()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ operator[]()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ ordering()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ pop()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ prettyPrint()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ push()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ shapeCheck()
template<typename P_numtype, int N_length, typename P_arraytype>
template<typename T_shape>
◆ shift() [1/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ shift() [2/2]
template<typename P_numtype, int N_length, typename P_arraytype>
◆ stride()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ suggestStride()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ ubound()
template<typename P_numtype, int N_length, typename P_arraytype>
◆ array_
template<typename P_numtype, int N_length, typename P_arraytype>
◆ data_
template<typename P_numtype, int N_length, typename P_arraytype>
◆ maxWidth
template<typename P_numtype, int N_length, typename P_arraytype>
◆ minWidth
template<typename P_numtype, int N_length, typename P_arraytype>
◆ numArrayOperands
template<typename P_numtype, int N_length, typename P_arraytype>
◆ numIndexPlaceholders
template<typename P_numtype, int N_length, typename P_arraytype>
◆ numTMOperands
template<typename P_numtype, int N_length, typename P_arraytype>
◆ numTVOperands
template<typename P_numtype, int N_length, typename P_arraytype>
◆ rank_
template<typename P_numtype, int N_length, typename P_arraytype>
◆ stack_
template<typename P_numtype, int N_length, typename P_arraytype>
◆ stride_
template<typename P_numtype, int N_length, typename P_arraytype>
The documentation for this class was generated from the following file: