#include #include #include #include #include #include #include #ifdef __GNUC__ #define LIKELY(x) __builtin_expect((x), 1) #define UNLIKELY(x) __builtin_expect((x), 0) #else #define LIKELY(x) (x) #define UNLIKELY(x) (x) #endif #undef MAX #undef MIN #define MAX(x, y) ((x) > (y) ? (x) : (y)) #define MIN(x, y) ((x) < (y) ? (x) : (y)) #define SIMD_WIDTH 16 int ksw_extend2_origin(int qlen, const uint8_t *query, int tlen, const uint8_t *target, int is_left, int m, const int8_t *mat, int o_del, int e_del, int o_ins, int e_ins, int w, int end_bonus, int zdrop, int h0, int *_qle, int *_tle, int *_gtle, int *_gscore, int *_max_off); static const uint16_t h_vec_int_mask[SIMD_WIDTH][SIMD_WIDTH] = { {0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0}, {0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff}}; // const int permute_mask = _MM_SHUFFLE(0, 1, 2, 3); #define permute_mask _MM_SHUFFLE(0, 1, 2, 3) // 初始化变量 #define SIMD_INIT \ int oe_del = o_del + e_del, oe_ins = o_ins + e_ins; \ __m256i zero_vec; \ __m256i max_vec; \ __m256i oe_del_vec; \ __m256i oe_ins_vec; \ __m256i e_del_vec; \ __m256i e_ins_vec; \ __m256i h_vec_mask[SIMD_WIDTH]; \ zero_vec = _mm256_setzero_si256(); \ oe_del_vec = _mm256_set1_epi16(-oe_del); \ oe_ins_vec = _mm256_set1_epi16(-oe_ins); \ e_del_vec = _mm256_set1_epi16(-e_del); \ e_ins_vec = _mm256_set1_epi16(-e_ins); \ __m256i match_sc_vec = _mm256_set1_epi16(a); \ __m256i mis_sc_vec = _mm256_set1_epi16(-b); \ __m256i amb_sc_vec = _mm256_set1_epi16(-1); \ __m256i amb_vec = _mm256_set1_epi16(4); \ for (i = 0; i < SIMD_WIDTH; ++i) \ h_vec_mask[i] = _mm256_loadu_si256((__m256i *)(&h_vec_int_mask[i])); /* * e 表示当前ref的碱基被删除 * f 表示当前seq的碱基插入 * m 表示当前碱基匹配(可以相等,也可以不想等) * h 表示最大值 */ // load向量化数据 #define SIMD_LOAD \ __m256i m1 = _mm256_loadu_si256((__m256i *)(&mA1[j])); \ __m256i e1 = _mm256_loadu_si256((__m256i *)(&eA1[j])); \ __m256i m1j1 = _mm256_loadu_si256((__m256i *)(&mA1[j - 1])); \ __m256i f1j1 = _mm256_loadu_si256((__m256i *)(&fA1[j - 1])); \ __m256i h0j1 = _mm256_loadu_si256((__m256i *)(&hA0[j - 1])); \ __m256i qs_vec = _mm256_loadu_si256((__m256i *)(&seq[j - 1])); \ __m256i ts_vec = _mm256_loadu_si256((__m256i *)(&ref[i])); // 比对ref和seq的序列,计算罚分 #define SIMD_CMP_SEQ \ ts_vec = _mm256_permute4x64_epi64(ts_vec, permute_mask); \ ts_vec = _mm256_shufflelo_epi16(ts_vec, permute_mask); \ ts_vec = _mm256_shufflehi_epi16(ts_vec, permute_mask); \ __m256i match_mask_vec = _mm256_cmpeq_epi16(qs_vec, ts_vec); \ __m256i mis_score_vec = _mm256_andnot_si256(match_mask_vec, mis_sc_vec); \ __m256i score_vec = _mm256_and_si256(match_sc_vec, match_mask_vec); \ score_vec = _mm256_or_si256(score_vec, mis_score_vec); \ __m256i q_amb_mask_vec = _mm256_cmpeq_epi16(qs_vec, amb_vec); \ __m256i t_amb_mask_vec = _mm256_cmpeq_epi16(ts_vec, amb_vec); \ __m256i amb_mask_vec = _mm256_or_si256(q_amb_mask_vec, t_amb_mask_vec); \ score_vec = _mm256_andnot_si256(amb_mask_vec, score_vec); \ __m256i amb_score_vec = _mm256_and_si256(amb_mask_vec, amb_sc_vec); \ score_vec = _mm256_or_si256(score_vec, amb_score_vec); // 向量化计算h, e, f, m #define SIMD_COMPUTE \ __m256i en_vec0 = _mm256_add_epi16(m1, oe_del_vec); \ __m256i en_vec1 = _mm256_add_epi16(e1, e_del_vec); \ __m256i en_vec = _mm256_max_epi16(en_vec0, en_vec1); \ __m256i fn_vec0 = _mm256_add_epi16(m1j1, oe_ins_vec); \ __m256i fn_vec1 = _mm256_add_epi16(f1j1, e_ins_vec); \ __m256i fn_vec = _mm256_max_epi16(fn_vec0, fn_vec1); \ __m256i mn_vec0 = _mm256_add_epi16(h0j1, score_vec); \ __m256i mn_mask = _mm256_cmpgt_epi16(h0j1, zero_vec); \ __m256i mn_vec = _mm256_and_si256(mn_vec0, mn_mask); \ __m256i hn_vec0 = _mm256_max_epi16(en_vec, fn_vec); \ __m256i hn_vec = _mm256_max_epi16(hn_vec0, mn_vec); \ en_vec = _mm256_max_epi16(en_vec, zero_vec); \ fn_vec = _mm256_max_epi16(fn_vec, zero_vec); \ mn_vec = _mm256_max_epi16(mn_vec, zero_vec); \ hn_vec = _mm256_max_epi16(hn_vec, zero_vec); // 存储向量化结果 #define SIMD_STORE \ max_vec = _mm256_max_epi16(max_vec, hn_vec); \ _mm256_storeu_si256((__m256i *)&eA2[j], en_vec); \ _mm256_storeu_si256((__m256i *)&fA2[j], fn_vec); \ _mm256_storeu_si256((__m256i *)&mA2[j], mn_vec); \ _mm256_storeu_si256((__m256i *)&hA2[j], hn_vec); // 去除多余的部分 #define SIMD_REMOVE_EXTRA \ en_vec = _mm256_and_si256(en_vec, h_vec_mask[end - j]); \ fn_vec = _mm256_and_si256(fn_vec, h_vec_mask[end - j]); \ mn_vec = _mm256_and_si256(mn_vec, h_vec_mask[end - j]); \ hn_vec = _mm256_and_si256(hn_vec, h_vec_mask[end - j]); // 找最大值和位置 #define SIMD_FIND_MAX \ max_vec = _mm256_max_epu16(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 2)); \ max_vec = _mm256_max_epu16(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 4)); \ max_vec = _mm256_max_epu16(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 6)); \ max_vec = _mm256_max_epu16(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 8)); \ max_vec = _mm256_max_epu16(max_vec, _mm256_permute2x128_si256(max_vec, max_vec, 0x01)); \ int16_t *maxVal = (int16_t *)&max_vec; \ m = maxVal[0]; \ if (m > 0) \ { \ for (j = beg, i = iend; j <= end; j += SIMD_WIDTH, i -= SIMD_WIDTH) \ { \ __m256i h2_vec = _mm256_loadu_si256((__m256i *)(&hA2[j])); \ __m256i vcmp = _mm256_cmpeq_epi16(h2_vec, max_vec); \ uint32_t mask = _mm256_movemask_epi8(vcmp); \ if (mask > 0) \ { \ int pos = SIMD_WIDTH - 1 - ((__builtin_clz(mask)) >> 1); \ mj = j - 1 + pos; \ mi = i - 1 - pos; \ } \ } \ } // 每轮迭代后,交换数组 #define SWAP_DATA_POINTER \ int16_t *tmp = hA0; \ hA0 = hA1; \ hA1 = hA2; \ hA2 = tmp; \ tmp = eA1; \ eA1 = eA2; \ eA2 = tmp; \ tmp = fA1; \ fA1 = fA2; \ fA2 = tmp; \ tmp = mA1; \ mA1 = mA2; \ mA2 = tmp; int ksw_avx2(int qlen, // query length 待匹配段碱基的query长度 const uint8_t *query, // read碱基序列 int tlen, // target length reference的长度 const uint8_t *target, // reference序列 int is_left, // 是不是向左扩展 int m, // 碱基种类 (5) const int8_t *mat, // 每个位置的query和target的匹配得分 m*m int o_del, // deletion 错配开始的惩罚系数 int e_del, // deletion extension的惩罚系数 int o_ins, // insertion 错配开始的惩罚系数 int e_ins, // insertion extension的惩罚系数SIMD_BTYES int a, // 碱基match时的分数 int b, // 碱基mismatch时的惩罚分数(正数) int w, // 提前剪枝系数,w =100 匹配位置和beg的最大距离 int end_bonus, int zdrop, int h0, // 该seed的初始得分(完全匹配query的碱基数) int *_qle, // 匹配得到全局最大得分的碱基在query的位置 int *_tle, // 匹配得到全局最大得分的碱基在reference的位置 int *_gtle, // query全部匹配上的target的长度 int *_gscore, // query的端到端匹配得分 int *_max_off) // 取得最大得分时在query和reference上位置差的 最大值 { int16_t *mA, *hA, *eA, *fA, *mA1, *mA2, *hA0, *hA1, *eA1, *fA1, *hA2, *eA2, *fA2; // hA0保存上上个col的H,其他的保存上个H E F M int16_t *seq, *ref; uint8_t *mem; int16_t *qtmem, *vmem; int seq_size = qlen + SIMD_WIDTH, ref_size = tlen + SIMD_WIDTH; int i, iStart, D, j, k, beg, end, max, max_i, max_j, max_ins, max_del, max_ie, gscore, max_off; int Dloop = tlen + qlen; // 循环跳出条件 int span, beg1, end1; // 边界条件计算 int col_size = qlen + 2 + SIMD_WIDTH; int val_mem_size = (col_size * 9 * 2 + 31) >> 5 << 5; // 32字节的整数倍 int mem_size = (seq_size + ref_size) * 2 + val_mem_size; SIMD_INIT; // 初始化simd用的数据 assert(h0 > 0); // allocate memory mem = malloc(mem_size); qtmem = (int16_t *)&mem[0]; seq = &qtmem[0]; ref = &qtmem[seq_size]; if (is_left) { for (i = 0; i < qlen; ++i) seq[i] = query[qlen - 1 - i]; for (i = 0; i < tlen; ++i) ref[i + SIMD_WIDTH] = target[tlen - 1 - i]; } else { for (i = 0; i < qlen; ++i) seq[i] = query[i]; for (i = 0; i < tlen; ++i) ref[i + SIMD_WIDTH] = target[i]; } vmem = &ref[ref_size]; for (i = 0; i < (val_mem_size >> 1); i += SIMD_WIDTH) { _mm256_storeu_si256((__m256i *)&vmem[i], zero_vec); } hA = &vmem[0]; mA = &vmem[col_size * 3]; eA = &vmem[col_size * 5]; fA = &vmem[col_size * 7]; hA0 = &hA[0]; hA1 = &hA[col_size]; hA2 = &hA1[col_size]; mA1 = &mA[0]; mA2 = &mA[col_size]; eA1 = &eA[0]; eA2 = &eA[col_size]; fA1 = &fA[0]; fA2 = &fA[col_size]; // adjust $w if it is too large k = m * m; // get the max score for (i = 0, max = 0; i < k; ++i) max = max > mat[i] ? max : mat[i]; max_ins = (int)((double)(qlen * max + end_bonus - o_ins) / e_ins + 1.); max_ins = max_ins > 1 ? max_ins : 1; w = w < max_ins ? w : max_ins; max_del = (int)((double)(qlen * max + end_bonus - o_del) / e_del + 1.); max_del = max_del > 1 ? max_del : 1; w = w < max_del ? w : max_del; // TODO: is this necessary? if (tlen < qlen) w = MIN(tlen - 1, w); // DP loop max = h0, max_i = max_j = -1; max_ie = -1, gscore = -1; ; max_off = 0; beg = 1; end = qlen; // init h0 hA0[0] = h0; // 左上角 if (qlen == 0 || tlen == 0) Dloop = 0; // 防止意外情况 if (w >= qlen) { max_ie = 0; gscore = 0; } int m_last = 0; int iend; for (D = 1; LIKELY(D < Dloop); ++D) { // 边界条件一定要注意! tlen 大于,等于,小于 qlen时的情况 if (D > tlen) { span = MIN(Dloop - D, w); beg1 = MAX(D - tlen + 1, ((D - w) / 2) + 1); } else { span = MIN(D - 1, w); beg1 = MAX(1, ((D - w) / 2) + 1); } end1 = MIN(qlen, beg1 + span); if (beg < beg1) beg = beg1; if (end > end1) end = end1; if (beg > end) break; // 不用计算了,直接跳出,否则hA2没有被赋值,里边是上一轮hA0的值,会出bug iend = D - (beg - 1); // ref开始计算的位置,倒序 span = end - beg; iStart = iend - span - 1; // 0开始的ref索引位置 // 每一轮需要记录的数据 int m = 0, mj = -1, mi = -1; max_vec = zero_vec; // 要处理边界 // 左边界 处理f (insert) if (iStart == 0) { hA1[end] = MAX(0, h0 - (o_ins + e_ins * end)); } // 上边界 if (beg == 1) { hA1[0] = MAX(0, h0 - (o_del + e_del * iend)); } else { hA1[beg - 1] = 0; eA1[beg - 1] = 0; } for (j = beg, i = iend; j <= end + 1 - SIMD_WIDTH; j += SIMD_WIDTH, i -= SIMD_WIDTH) { // 取数据 SIMD_LOAD; // 比对seq,计算罚分 SIMD_CMP_SEQ; // 计算 SIMD_COMPUTE; // 存储结果 SIMD_STORE; } // 剩下的计算单元 if (j <= end) { // 取数据 SIMD_LOAD; // 比对seq,计算罚分 SIMD_CMP_SEQ; // 计算 SIMD_COMPUTE; // 去除多余计算的部分 SIMD_REMOVE_EXTRA; // 存储结果 SIMD_STORE; } SIMD_FIND_MAX; // 注意最后跳出循环j的值 j = end + 1; if (j == qlen + 1) { max_ie = gscore > hA2[qlen] ? max_ie : iStart; gscore = gscore > hA2[qlen] ? gscore : hA2[qlen]; } // if (m == 0 && m_last == 0) // break; // 一定要注意,斜对角遍历和按列遍历的不同点 if (m > max) { max = m, max_i = mi, max_j = mj; max_off = max_off > abs(mj - mi) ? max_off : abs(mj - mi); } else if (zdrop > 0) { if (mi - max_i > mj - max_j) { if (max - m - ((mi - max_i) - (mj - max_j)) * e_del > zdrop) break; } else { if (max - m - ((mj - max_j) - (mi - max_i)) * e_ins > zdrop) break; } } // 调整计算的边界 for (j = beg; LIKELY(j <= end); ++j) { int has_val = hA1[j - 1] | hA2[j]; if (has_val) break; } beg = j; for (j = end + 1; LIKELY(j >= beg); --j) { int has_val = hA1[j - 1] | hA2[j]; if (has_val) break; else hA0[j - 1] = 0; } end = j + 1 <= qlen ? j + 1 : qlen; // beg = 0; // end = qlen; // uncomment this line for debugging m_last = m; // swap m, h, e, f SWAP_DATA_POINTER; } free(mem); if (_qle) *_qle = max_j + 1; if (_tle) *_tle = max_i + 1; if (_gtle) *_gtle = max_ie + 1; if (_gscore) *_gscore = gscore; if (_max_off) *_max_off = max_off; return max; } typedef struct { int32_t h, e; } eh_t; int ksw_extend2_origin(int qlen, // query length 待匹配段碱基的query长度 const uint8_t *query, // read碱基序列 int tlen, // target length reference的长度 const uint8_t *target, // reference序列 int is_left, // 是不是向左扩展 int m, // 碱基种类 (5) const int8_t *mat, // 每个位置的query和target的匹配得分 m*m int o_del, // deletion 错配开始的惩罚系数 int e_del, // deletion extension的惩罚系数 int o_ins, // insertion 错配开始的惩罚系数 int e_ins, // insertion extension的惩罚系数 int w, // 提前剪枝系数,w =100 匹配位置和beg的最大距离 int end_bonus, int zdrop, int h0, // 该seed的初始得分(完全匹配query的碱基数) int *_qle, // 匹配得到全局最大得分的碱基在query的位置 int *_tle, // 匹配得到全局最大得分的碱基在reference的位置 int *_gtle, // query全部匹配上的target的长度 int *_gscore, // query的端到端匹配得分 int *_max_off) // 取得最大得分时在query和reference上位置差的 最大值 { eh_t *eh; // score array int8_t *qp; // query profile int i, j, k, oe_del = o_del + e_del, oe_ins = o_ins + e_ins, beg, end, max, max_i, max_j, max_ins, max_del, max_ie, gscore, max_off; uint8_t *qmem, *ref, *seq; assert(h0 > 0); // allocate memory qp = malloc(qlen * m); eh = calloc(qlen + 1, 8); qmem = malloc(qlen + tlen); seq = (uint8_t *)&qmem[0]; ref = (uint8_t *)&qmem[qlen]; if (is_left) { for (i = 0; i < qlen; ++i) seq[i] = query[qlen - 1 - i]; for (i = 0; i < tlen; ++i) ref[i] = target[tlen - 1 - i]; } else { for (i = 0; i < qlen; ++i) seq[i] = query[i]; for (i = 0; i < tlen; ++i) ref[i] = target[i]; } // generate the query profile for (k = i = 0; k < m; ++k) { const int8_t *p = &mat[k * m]; for (j = 0; j < qlen; ++j) qp[i++] = p[seq[j]]; } // fill the first row eh[0].h = h0; eh[1].h = h0 > oe_ins ? h0 - oe_ins : 0; for (j = 2; j <= qlen && eh[j - 1].h > e_ins; ++j) eh[j].h = eh[j - 1].h - e_ins; // adjust $w if it is too large k = m * m; for (i = 0, max = 0; i < k; ++i) // get the max score max = max > mat[i] ? max : mat[i]; max_ins = (int)((double)(qlen * max + end_bonus - o_ins) / e_ins + 1.); max_ins = max_ins > 1 ? max_ins : 1; w = w < max_ins ? w : max_ins; max_del = (int)((double)(qlen * max + end_bonus - o_del) / e_del + 1.); max_del = max_del > 1 ? max_del : 1; w = w < max_del ? w : max_del; // TODO: is this necessary? // printf("%d\n", w); // DP loop max = h0, max_i = max_j = -1; max_ie = -1, gscore = -1; max_off = 0; beg = 0, end = qlen; for (i = 0; LIKELY(i < tlen); ++i) { int t, f = 0, h1, m = 0, mj = -1; int8_t *q = &qp[ref[i] * qlen]; // apply the band and the constraint (if provided) if (beg < i - w) beg = i - w; if (end > i + w + 1) end = i + w + 1; // if (end > qlen) end = qlen; 没用 // compute the first column if (beg == 0) { h1 = h0 - (o_del + e_del * (i + 1)); if (h1 < 0) h1 = 0; } else h1 = 0; for (j = beg; LIKELY(j < end); ++j) { // At the beginning of the loop: eh[j] = { H(i-1,j-1), E(i,j) }, f = F(i,j) and h1 = H(i,j-1) // Similar to SSE2-SW, cells are computed in the following order: // H(i,j) = max{H(i-1,j-1)+S(i,j), E(i,j), F(i,j)} // E(i+1,j) = max{H(i,j)-gapo, E(i,j)} - gape // F(i,j+1) = max{H(i,j)-gapo, F(i,j)} - gape eh_t *p = &eh[j]; int h, M = p->h, e = p->e; // get H(i-1,j-1) and E(i-1,j) p->h = h1; // set H(i,j-1) for the next row M = M ? M + q[j] : 0; // separating H and M to disallow a cigar like "100M3I3D20M" h = M > e ? M : e; // e and f are guaranteed to be non-negative, so h>=0 even if M<0 h = h > f ? h : f; h1 = h; // save H(i,j) to h1 for the next column mj = m > h ? mj : j; // record the position where max score is achieved m = m > h ? m : h; // m is stored at eh[mj+1] t = M - oe_del; t = t > 0 ? t : 0; e -= e_del; e = e > t ? e : t; // computed E(i+1,j) p->e = e; // save E(i+1,j) for the next row t = M - oe_ins; t = t > 0 ? t : 0; f -= e_ins; f = f > t ? f : t; // computed F(i,j+1) } eh[end].h = h1; eh[end].e = 0; if (j == qlen) { max_ie = gscore > h1 ? max_ie : i; gscore = gscore > h1 ? gscore : h1; } if (m == 0) break; if (m > max) { max = m, max_i = i, max_j = mj; max_off = max_off > abs(mj - i) ? max_off : abs(mj - i); } else if (zdrop > 0) { if (i - max_i > mj - max_j) { if (max - m - ((i - max_i) - (mj - max_j)) * e_del > zdrop) break; } else { if (max - m - ((mj - max_j) - (i - max_i)) * e_ins > zdrop) break; } } // update beg and end for the next round for (j = beg; LIKELY(j < end) && eh[j].h == 0 && eh[j].e == 0; ++j) ; beg = j; for (j = end; LIKELY(j >= beg) && eh[j].h == 0 && eh[j].e == 0; --j) ; end = j + 2 < qlen ? j + 2 : qlen; // beg = 0; end = qlen; // uncomment this line for debugging } free(eh); free(qp); free(qmem); if (_qle) *_qle = max_j + 1; if (_tle) *_tle = max_i + 1; if (_gtle) *_gtle = max_ie + 1; if (_gscore) *_gscore = gscore; if (_max_off) *_max_off = max_off; return max; }