我从tutorial知道未对齐加载和存储它看起来像:
-lX
看起来很糟糕。如此大量的工作,以存储一个向量! 它有适当的性能损失。
//Load a vector from an unaligned location in memory
__vector unsigned char LoadUnaligned(const unsigned char * src )
{
__vector unsigned char permuteVector = vec_lvsl(0, src);
__vector unsigned char low = vec_ld( 0, src);
__vector unsigned char high = vec_ld( 16, src);
return vec_perm( low, high, permuteVector);
}
//Store a vector to an unaligned location in memory
void StoreUnaligned(__vector unsigned char v, __vector unsigned char * dst)
{
//Load the surrounding area
__vector unsigned char low = vec_ld( 0, dst);
__vector unsigned char high = vec_ld( 16, dst);
//Prepare the constants that we need
__vector unsigned char permuteVector = vec_lvsr( 0, (int*) dst);
__vector signed char oxFF = vec_splat_s8( -1 );
__vector signed char ox00 = vec_splat_s8( 0 );
//Make a mask for which parts of the vectors to swap out
__vector unsigned char mask = vec_perm( ox00, oxFF, permuteVector );
//Right rotate our input data
v = vec_perm( v, v, permuteVector );
//Insert our data into the low and high vectors
low = vec_sel( v, low, mask );
high = vec_sel( high, v, mask );
//Store the two aligned result vectors
vec_st( low, 0, dst);
vec_st( high, 16, dst);
}
第二个功能比第一个功能慢3-4倍。 由于我无法控制输入和输出内存的对齐,因此我必须实现两个版本。 如何最大限度地减少未对齐案例的性能损失?
答案 0 :(得分:4)
首先,我想提一下,如果将Altivec向量多次保存到未对齐的内存中,则不需要仅在开头和结尾处保存数组中间的先前内存状态。 因此Simd Library中有一个有用的函数和类,它实现了这个功能:
typedef __vector uint8_t v128_u8;
const v128_u8 K8_00 = vec_splat_u8(0x00);
const v128_u8 K8_FF = vec_splat_u8(0xFF);
template <bool align> inline v128_u8 Load(const uint8_t * p);
template <> inline v128_u8 Load<false>(const uint8_t * p)
{
v128_u8 lo = vec_ld(0, p);
v128_u8 hi = vec_ld(16, p);
return vec_perm(lo, hi, vec_lvsl(0, p));
}
template <> inline v128_u8 Load<true>(const uint8_t * p)
{
return vec_ld(0, p);
}
template <bool align> struct Storer;
template <> struct Storer<true>
{
template <class T> Storer(T * ptr)
:_ptr((uint8_t*)ptr)
{
}
template <class T> inline void First(T value)
{
vec_st((v128_u8)value, 0, _ptr);
}
template <class T> inline void Next(T value)
{
_ptr += 16;
vec_st((v128_u8)value, 0, _ptr);
}
inline void Flush()
{
}
private:
uint8_t * _ptr;
};
template <> struct Storer<false>
{
template <class T> inline Storer(T * ptr)
:_ptr((uint8_t*)ptr)
{
_perm = vec_lvsr(0, _ptr);
_mask = vec_perm(K8_00, K8_FF, _perm);
}
template <class T> inline void First(T value)
{
_last = (v128_u8)value;
v128_u8 background = vec_ld(0, _ptr);
v128_u8 foreground = vec_perm(_last, _last, _perm);
vec_st(vec_sel(background, foreground, _mask), 0, _ptr);
}
template <class T> inline void Next(T value)
{
_ptr += 16;
vec_st(vec_perm(_last, (v128_u8)value, _perm), 0, _ptr);
_last = (v128_u8)value;
}
inline void Flush()
{
v128_u8 background = vec_ld(16, _ptr);
v128_u8 foreground = vec_perm(_last, _last, _perm);
vec_st(vec_sel(foreground, background, _mask), 16, _ptr);
}
private:
uint8_t * _ptr;
v128_u8 _perm;
v128_u8 _mask;
v128_u8 _last;
};
它的使用将如下所示:
template<bool align> void SomeFunc(const unsigned char * src, size_t size, unsigned char * dst)
{
Storer<align> _dst(dst);
__vector unsigned char a = Load<align>(src);
//simple work
_dst.First(a);// save first block
for(size_t i = 16; i < size; i += 16)
{
__vector unsigned char a = Load<align>(src + i);
//simple work
_dst.Next(a);// save body
}
_dst.Flush(); // save tail
}
与对齐版本相比,性能损失将为30-40%。 这当然是令人不快的,但却是宽容的。
其他优点是减少代码 - 所有函数(对齐和未对齐)具有相同的实现。