在ffmpeg中，在进行h264 rbsp流demux的时候，需要进行starting code的搜索，其采用的方法比较简单，就是不断比较字节流中连续的三个字节，是不是 0x00, 0x00, 0x01，ffmpeg采用如下代码用来找到各个NALU的分界点：
static int find_next_start_code(const uint8_t *buf, const uint8_t *next_avc){int i = 0;if (buf + 3 >= next_avc)return next_avc - buf;while (buf + i + 3 < next_avc) {if (buf[i] == 0 && buf[i + 1] == 0 && buf[i + 2] == 1)break;i++;}return i + 3;}
这种方法比较传统，一般来说如果对于性能没有特别的要求，是能够很好满足要求了。
按照ffmpeg的算法，搜索一个内存块中的所有H264 NALU的起始码，并将起始码的位置写入nalu_borders数组中，写出来的代码如下，整个逻辑简单明了：
vector<int> split_simd_ffmpeg(const uint8_t *data, size_t cb){const uint8_t *p = data;const uint8_t *e = data + cb;vector<int> list_index;while (p < e -3){if (p[0] == 0 && p[1] == 0 && p[2] == 1){list_index.emplace_back(p - data);}p++;}return list_index;}
本文提出一种更高效的starting code的搜索算法：
void split_nalu(char *video_data, int size, std::vector<int> &nalu_borders){while(index + 8 <= (size_t)size){uint64_t code = *(uint64_t*)(video_data+index);if ((code & 0xFFFFFFull) == 0x010000ull){nalu_borders.emplace_back(index);index += 3;continue;}if ((code & 0xFFFFFF00ull) == 0x01000000ull){nalu_borders.emplace_back(index+1);index += 4;continue;}if ((code & 0xFFFFFF0000ull) == 0x0100000000ull){nalu_borders.emplace_back(index+2);index += 5;continue;}if (((code & 0xFFFFFF000000ull) == 0x010000000000ull){nalu_borders.emplace_back(index+3);index += 6;continue;}if ((code & 0xFFFFFF00000000ull) == 0x01000000000000ull){nalu_borders.emplace_back(index+4);index += 7;continue;}if ((code & 0xFFFFFF0000000000ull) == 0x0100000000000000ull){nalu_borders.emplace_back(index+5);index += 8;continue;}if ((code & 0xFFFF000000000000ull) == 0x0ull){index += 6;}else if ((code & 0xFF00000000000000ull) == 0x0ull){index += 7;}else{index += 8;}}while(index + 4 < (size_t)size){uint32_t code = *(uint64_t*)(video_data+index);if ((code & 0x00FFFFFFu) == 0x00010000u){nalu_borders.emplace_back(index);index += 3;continue;}index++;}}
看上去代码复杂了很多，但是实际测试结果，对比ffmpeg的搜索算法，在x64的CPU的机器上面，性能至少提升了30%。
本算法利用了64位的寄存器预先读取8个字节，然后在循环提内通过掩码与操作进行比对，一个循环中对8个字节进行264起始码的搜索，相比ffmpeg需要不断从内存中load，然后逐字节比对来说，使得搜索性能得到了显著提升。毕竟寄存器的性能远比内存的读写性能要强多了！而且在一个循环里面可以通过编译器和CPU的优化进行指令的多路并发执行。
然后最近几天拼命想利用SIMD优化指令进行搜索的算法，好不容易熟悉了simd的几个指令，调试了老半天，终于写出来如下代码：
const uint8_t mask_1[32] = { 0xFF, 0xFF, 0xFF, 0x00,0xFF, 0xFF, 0xFF, 0x00,0xFF, 0xFF, 0xFF, 0x00,0xFF, 0xFF, 0xFF, 0x00 };const uint8_t dest_1[32] = { 0, 0, 1, 0, 0, 0, 1, 0,0, 0, 1, 0, 0, 0, 1, 0 };const uint8_t mask_2[32] = { 0x00, 0xFF, 0xFF, 0xFF,0x00, 0xFF, 0xFF, 0xFF,0x00, 0xFF, 0xFF, 0xFF,0x00, 0xFF, 0xFF, 0xFF,};const uint8_t dest_2[32] = { 0, 0, 0, 1, 0, 0, 0, 1,0, 0, 0, 1, 0, 0, 0, 1};vector<int> split_nalu_simd(const uint8_t *video_data, size_t size){size_t index = 0;vector<int> list_index;op_data od;size_t next_block = 31;while(index + 16 <= (size_t)size){__m128i src = _mm_load_si128((__m128i*)(video_data+index));__m128i mask = _mm_load_si128((__m128i*)od.mask_1);__m128i dest = _mm_load_si128((__m128i*)od.dest_1);__m128i tmp = _mm_and_si128(src, mask);__m128i tmp2 = _mm_cmpeq_epi32(tmp, dest);if (_mm_extract_epi32(tmp2, 0) == 0xFFFFFFFFu){list_index.emplace_back(index);}if (_mm_extract_epi32(tmp2, 1) == 0xFFFFFFFFu){list_index.emplace_back(index + 4);}if (_mm_extract_epi32(tmp2, 2) == 0xFFFFFFFFu){list_index.emplace_back(index + 8);}if (_mm_extract_epi32(tmp2, 3) == 0xFFFFFFFFu){list_index.emplace_back(index + 12);}mask = _mm_load_si128((__m128i*)od.mask_2);dest = _mm_load_si128((__m128i*)od.dest_2);tmp = _mm_and_si128(src, mask);tmp2 = _mm_cmpeq_epi32(tmp, dest);if (_mm_extract_epi32(tmp2, 0) == 0xFFFFFFFFu){list_index.emplace_back(index + 1);}if (_mm_extract_epi32(tmp2, 1) == 0xFFFFFFFFu){list_index.emplace_back(index + 5);}if (_mm_extract_epi32(tmp2, 2) == 0xFFFFFFFFu){list_index.emplace_back(index + 9);}if (_mm_extract_epi32(tmp2, 3) == 0xFFFFFFFFu){list_index.emplace_back(index + 13);}src = _mm_srli_si128(src, 2);mask = _mm_load_si128((__m128i*)od.mask_1);dest = _mm_load_si128((__m128i*)od.dest_1);tmp = _mm_and_si128(src, mask);tmp2 = _mm_cmpeq_epi32(tmp, dest);if (_mm_extract_epi32(tmp2, 0) == 0xFFFFFFFFu){list_index.emplace_back(index + 2);}if (_mm_extract_epi32(tmp2, 1) == 0xFFFFFFFFu){list_index.emplace_back(index + 6);}if (_mm_extract_epi32(tmp2, 2) == 0xFFFFFFFFu){list_index.emplace_back(index + 10);}mask = _mm_load_si128((__m128i*)od.mask_2);dest = _mm_load_si128((__m128i*)od.dest_2);tmp = _mm_and_si128(src, mask);tmp2 = _mm_cmpeq_epi32(tmp, dest);if (_mm_extract_epi32(tmp2, 0) == 0xFFFFFFFFu){list_index.emplace_back(index + 3);}if (_mm_extract_epi32(tmp2, 1) == 0xFFFFFFFFu){list_index.emplace_back(index + 7);}if (_mm_extract_epi32(tmp2, 2) == 0xFFFFFFFFu){list_index.emplace_back(index + 11);}index += 16;uint32_t tail = _mm_extract_epi32(src, 3);if (tail == 0x0u){if (index + 1 <= size && video_data[index] == 0x01){list_index.emplace_back(index-2);continue;}}if ((tail & 0x0000FF00u) == 0x0u){if (index + 2 <= size && video_data[index] == 0x00 && video_data[index+1] == 0x01){list_index.emplace_back(index-1);}}}while(index + 4 < (size_t)size){uint32_t code = *(uint64_t*)(video_data+index);if ((code & 0x00FFFFFFu) == 0x00010000u){list_index.emplace_back(index);index += 3;continue;}index++;}std::sort(list_index.begin(), list_index.end());return list_index;}
但是测试下来的效果不太理想，大失所望，基本上和上面的split_nalu不相上下，应该是中间用了太多条件判断的分支，抵消了simd的优势，加上插入起始码位置的时候因为并不是按顺序插入的，需要在结尾处进行一次sort操作一定程度上会引起性能的降低，得不偿失。
感觉能够想到的方法还是split_nalu比较靠谱，也不是太复杂。
今天下午，突然来了灵感，修改了上面的simd的代码，减少了if条件判断分支，尽然性能提升1倍，开心！！！！
const uint8_t mask_1[32] = { 0xFF, 0xFF, 0xFF, 0x00,0xFF, 0xFF, 0xFF, 0x00,0xFF, 0xFF, 0xFF, 0x00,0xFF, 0xFF, 0xFF, 0x00 };const uint8_t dest_1[32] = { 0, 0, 1, 0, 0, 0, 1, 0,0, 0, 1, 0, 0, 0, 1, 0 };const uint8_t mask_2[32] = { 0x00, 0xFF, 0xFF, 0xFF,0x00, 0xFF, 0xFF, 0xFF,0x00, 0xFF, 0xFF, 0xFF,0x00, 0xFF, 0xFF, 0xFF,};const uint8_t dest_2[32] = { 0, 0, 0, 1, 0, 0, 0, 1,0, 0, 0, 1, 0, 0, 0, 1};vector<int> split_nalu_simd2(const uint8_t *video_data, size_t size){size_t index = 0;vector<int> list_index;op_data od;size_t next_block = 31;while(index + 16 <= (size_t)size){__m128i src = _mm_load_si128((__m128i*)(video_data+index));__m128i mask = _mm_load_si128((__m128i*)od.mask_1);__m128i dest = _mm_load_si128((__m128i*)od.dest_1);__m128i tmp = _mm_and_si128(src, mask);__m128i tmp2 = _mm_cmpeq_epi32(tmp, dest);int m = _mm_movemask_epi8(tmp2);if (m != 0){if (m & 0x0F){list_index.emplace_back(index);}if (m & 0xF0){list_index.emplace_back(index + 4);}if (m & 0xF00){list_index.emplace_back(index + 8);}if (m & 0xF000){list_index.emplace_back(index + 12);}}mask = _mm_load_si128((__m128i*)od.mask_2);dest = _mm_load_si128((__m128i*)od.dest_2);tmp = _mm_and_si128(src, mask);tmp2 = _mm_cmpeq_epi32(tmp, dest);m = _mm_movemask_epi8(tmp2);if (m != 0){if (m & 0x0F){list_index.emplace_back(index + 1);}if (m & 0xF0){list_index.emplace_back(index + 5);}if (m & 0xF00){list_index.emplace_back(index + 9);}if (m & 0xF000){list_index.emplace_back(index + 13);}}src = _mm_srli_si128(src, 2);mask = _mm_load_si128((__m128i*)od.mask_1);dest = _mm_load_si128((__m128i*)od.dest_1);tmp = _mm_and_si128(src, mask);tmp2 = _mm_cmpeq_epi32(tmp, dest);m = _mm_movemask_epi8(tmp2);if (m != 0){if (m & 0x0F){list_index.emplace_back(index + 2);}if (m & 0xF0){list_index.emplace_back(index + 6);}if (m & 0xF00){list_index.emplace_back(index + 10);}}mask = _mm_load_si128((__m128i*)od.mask_2);dest = _mm_load_si128((__m128i*)od.dest_2);tmp = _mm_and_si128(src, mask);tmp2 = _mm_cmpeq_epi32(tmp, dest);m = _mm_movemask_epi8(tmp2);if (m != 0){if (m & 0x0F){list_index.emplace_back(index + 3);}if (m & 0xF0){list_index.emplace_back(index + 7);}if (m & 0xF00){list_index.emplace_back(index + 11);}}index += 16;uint32_t tail = _mm_extract_epi32(src, 3);if (tail == 0x0u){if (index + 1 <= size && video_data[index] == 0x01){list_index.emplace_back(index-2);continue;}}if ((tail & 0x0000FF00u) == 0x0u){if (index + 2 <= size && video_data[index] == 0x00 && video_data[index+1] == 0x01){list_index.emplace_back(index-1);}}}while(index + 4 < (size_t)size){uint32_t code = *(uint64_t*)(video_data+index);if ((code & 0x00FFFFFFu) == 0x00010000u){list_index.emplace_back(index);index += 3;continue;}index++;}std::sort(list_index.begin(), list_index.end());return list_index;}
关键是是通过_mm_movemask_epi8将前面比较的结果合并到一个int型的字段m中，然后直接判断m是否都为0，如果为0就可以直接掉过里面的分支逻辑了，大大减少了条件分支，从而提升了性能。
ps：
需要提一下，如果没有开启-O3编译器优化，split_nalu_simd2比split_nalu版本性能差了一倍，开启以后则反回过了，虽然split_nalu性能也提升了，但是split_nalu_simd2提升非常明显。
假设待编译的代码为test.cpp，那么用如下编译指令：
g++ test.cpp -mavx2 -o test -g -O3
-mavx2是必须要加的，否则编译器不能支持simd相关指令。
原创文章，作者：网络技术联盟站，如若转载，请注明出处：https://www.sudun.com/ask/49816.html
