您目前解析 IPv4 位址的程式碼相當高效,但可以進一步最佳化以獲得更快的速度。一種方法是利用專為此任務設計的向量化解決方案。
對於支援SSE4.1 或SSSE3 指令的x86 處理器,這裡有一個可顯著提高效能的向量化解決方案:
__m128i shuffleTable[65536]; //can be reduced 256x times, see @IwillnotexistIdonotexist UINT32 MyGetIP(const char *str) { __m128i input = _mm_lddqu_si128((const __m128i*)str); //"192.167.1.3" input = _mm_sub_epi8(input, _mm_set1_epi8('0')); //1 9 2 254 1 6 7 254 1 254 3 208 245 0 8 40 __m128i cmp = input; //...X...X.X.XX... (signs) UINT32 mask = _mm_movemask_epi8(cmp); //6792 - magic index __m128i shuf = shuffleTable[mask]; //10 -1 -1 -1 8 -1 -1 -1 6 5 4 -1 2 1 0 -1 __m128i arr = _mm_shuffle_epi8(input, shuf); //3 0 0 0 | 1 0 0 0 | 7 6 1 0 | 2 9 1 0 __m128i coeffs = _mm_set_epi8(0, 100, 10, 1, 0, 100, 10, 1, 0, 100, 10, 1, 0, 100, 10, 1); __m128i prod = _mm_maddubs_epi16(coeffs, arr); //3 0 | 1 0 | 67 100 | 92 100 prod = _mm_hadd_epi16(prod, prod); //3 | 1 | 167 | 192 | ? | ? | ? | ? __m128i imm = _mm_set_epi8(-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 2, 0); prod = _mm_shuffle_epi8(prod, imm); //3 1 167 192 0 0 0 0 0 0 0 0 0 0 0 0 return _mm_extract_epi32(prod, 0); // return (UINT32(_mm_extract_epi16(prod, 1)) << 16) + UINT32(_mm_extract_epi16(prod, 0)); //no SSE 4.1 }
要有效地利用此向量化解決方案,需要一個預先計算的隨機表shuffleTable,可以如下產生:
void MyInit() { memset(shuffleTable, -1, sizeof(shuffleTable)); int len[4]; for (len[0] = 1; len[0] <= 3; len[0]++) for (len[1] = 1; len[1] <= 3; len[1]++) for (len[2] = 1; len[2] <= 3; len[2]++) for (len[3] = 1; len[3] <= 3; len[3]++) { int slen = len[0] + len[1] + len[2] + len[3] + 4; int rem = 16 - slen; for (int rmask = 0; rmask < 1<<rem; rmask++) { // { int rmask = (1<<rem)-1; //note: only maximal rmask is possible if strings are zero-padded int mask = 0; char shuf[16] = {-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}; int pos = 0; for (int i = 0; i < 4; i++) { for (int j = 0; j < len[i]; j++) { shuf[(3-i) * 4 + (len[i]-1-j)] = pos; pos++; } mask ^= (1<<pos); pos++; } mask ^= (rmask<<slen); _mm_store_si128(&shuffleTable[mask], _mm_loadu_si128((__m128i*)shuf)); } } }
在Ivy Bridge 處理器上,向量化解決方案展現了令人印象深刻的效能,每秒處理3.36 億個位址。這比原始問題中提供的程式碼大約快 7.8 倍。
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