这是另一个实现（比其他实现快得多）：
void ConstMult4(uint8_t *V, size_t N, uint8_t digit)
{
uint8_t CARRY = 0;
const uint32_t coef7 = digit * 10000000;
const uint32_t coef6 = digit * 1000000;
const uint32_t coef5 = digit * 100000;
const uint32_t coef4 = digit * 10000;
const uint32_t coef3 = digit * 1000;
const uint32_t coef2 = digit * 100;
const uint32_t coef1 = digit * 10;
const uint32_t coef0 = digit;
static uint8_t table[10000][4];
static int init = 1;
if(init)
{
for(int i=0 ; i<10000 ; ++i)
{
table[i][0] = (i / 1) % 10;
table[i][1] = (i / 10) % 10;
table[i][2] = (i / 100) % 10;
table[i][3] = (i / 1000) % 10;
}
init = 0;
}
for(size_t i=0 ; i<N/8*8 ; i+=8)
{
const uint32_t val = V[i+7]*coef7 + V[i+6]*coef6 + V[i+5]*coef5 + V[i+4]*coef4 + V[i+3]*coef3 + V[i+2]*coef2 + V[i+1]*coef1 + V[i+0]*coef0 + CARRY;
CARRY = val / 100000000;
const uint32_t loVal = val % 10000;
const uint32_t hiVal = val / 10000 - CARRY * 10000;
const uint8_t* loTablePtr = &table[loVal][0];
const uint8_t* hiTablePtr = &table[hiVal][0];
// Assume the compiler optimize the 2 following calls
// (otherwise the performance could be quite bad).
// memcpy is used to prevent performance issue due to pointer aliasing.
memcpy(V+i, loTablePtr, 4);
memcpy(V+i+4, hiTablePtr, 4);
}
for(size_t i=N/8*8 ; i<N ; ++i)
{
V[i] = V[i] * digit + CARRY;
CARRY = V[i] / 10;
V[i] -= CARRY * 10;
}
}
此实现假定V 和digit 中的计算数字实际上是数字。
它明显快于其他方法：
- 按照@phuclv 的建议在内部使用更大的基础（它减少了关键路径并引入了更多并行性）；
- 使用@chqrlieforyellowblockquotes 提出的查找表（它可以非常快速地计算除法/模运算）。
甚至可以通过使用 SSE 4.1 内部函数（SIMD 指令）来改进此代码。但是以较少可移植的代码为代价（尽管它可以在大多数基于 x86_64 的现代处理器上工作）。这是实现：
void ConstMult5(uint8_t *V, size_t N, uint8_t digit)
{
uint8_t CARRY = 0;
static uint8_t table[10000][4];
static int init = 1;
if(init)
{
for(int i=0 ; i<10000 ; ++i)
{
table[i][0] = (i / 1) % 10;
table[i][1] = (i / 10) % 10;
table[i][2] = (i / 100) % 10;
table[i][3] = (i / 1000) % 10;
}
init = 0;
}
__m128i coefs1 = _mm_set_epi16(1000, 100, 10, 1, 1000, 100, 10, 1);
__m128i coefs2 = _mm_set_epi32(10000*digit, 10000*digit, digit, digit);
for(size_t i=0 ; i<N/16*16 ; i+=8)
{
// Require SSE 4.1 (thus smmintrin.h need to be included)
const __m128i vBlock = _mm_loadu_si128((const __m128i*)&V[i]); // load 16 x uint8_t values (only half is used)
const __m128i v = _mm_cvtepu8_epi16(vBlock); // Convert the block to 8 x int16_t values
const __m128i tmp1 = _mm_madd_epi16(v, coefs1); // Compute the sum of adjacent pairs of v * coefs1 and put this in 4 x int32_t values
const __m128i tmp2 = _mm_add_epi32(tmp1, _mm_shuffle_epi32(tmp1, 0b10110001)); // Horizontal partial sum of 4 x int32_t values
const __m128i tmp3 = _mm_mul_epu32(tmp2, coefs2); // Compute tmp2 * coefs2 and put this in 2 x int64_t values
const uint32_t val = _mm_extract_epi64(tmp3, 1) + _mm_extract_epi64(tmp3, 0) + CARRY; // Final horizontal sum with CARRY
CARRY = val / 100000000;
const uint32_t loVal = val % 10000;
const uint32_t hiVal = val / 10000 - CARRY * 10000;
const uint8_t* loTablePtr = &table[loVal][0];
const uint8_t* hiTablePtr = &table[hiVal][0];
// See the memcpy remark in the code above (alternative version).
memcpy(V+i, loTablePtr, 4);
memcpy(V+i+4, hiTablePtr, 4);
}
for(size_t i=N/16*16 ; i<N ; ++i)
{
V[i] = V[i] * digit + CARRY;
CARRY = V[i] / 10;
V[i] -= CARRY * 10;
}
}
以下是我的机器（使用 i7-9700KF 处理器）上的性能结果（使用随机输入重复运行 1000 次并取平均值）：
ConstMult0(10000): 11.702 us
ConstMult3(10000): 6.768 us (last optimized version)
ConstMult4(10000): 3.569 us
ConstMult5(10000): 2.552 us
基于 SSE 的最终版本比您的原始实现快 4.6 倍！