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aligner_swsse_loc_i16.cpp
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aligner_swsse_loc_i16.cpp
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/*
* Copyright 2011, Ben Langmead <langmea@cs.jhu.edu>
*
* This file is part of Bowtie 2.
*
* Bowtie 2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Bowtie 2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Bowtie 2. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* aligner_sw_sse.cpp
*
* Versions of key alignment functions that use vector instructions to
* accelerate dynamic programming. Based chiefly on the striped Smith-Waterman
* paper and implementation by Michael Farrar. See:
*
* Farrar M. Striped Smith-Waterman speeds database searches six times over
* other SIMD implementations. Bioinformatics. 2007 Jan 15;23(2):156-61.
* http://sites.google.com/site/farrarmichael/smith-waterman
*
* While the paper describes an implementation of Smith-Waterman, we extend it
* do end-to-end read alignment as well as local alignment. The change
* required for this is minor: we simply let vmax be the maximum element in the
* score domain rather than the minimum.
*
* The vectorized dynamic programming implementation lacks some features that
* make it hard to adapt to solving the entire dynamic-programming alignment
* problem. For instance:
*
* - It doesn't respect gap barriers on either end of the read
* - It just gives a maximum; not enough information to backtrace without
* redoing some alignment
* - It's a little difficult to handle st_ and en_, especially st_.
* - The query profile mechanism makes handling of ambiguous reference bases a
* little tricky (16 cols in query profile lookup table instead of 5)
*
* Given the drawbacks, it is tempting to use SSE dynamic programming as a
* filter rather than as an aligner per se. Here are a few ideas for how it
* can be extended to handle more of the alignment problem:
*
* - Save calculated scores to a big array as we go. We return to this array
* to find and backtrace from good solutions.
*/
#include <limits>
#include "aligner_sw.h"
static const size_t NBYTES_PER_REG = 16;
static const size_t NWORDS_PER_REG = 8;
static const size_t NBITS_PER_WORD = 16;
static const size_t NBYTES_PER_WORD = 2;
// In 16-bit local mode, we have the option of using signed saturated
// arithmetic. Because we have signed arithmetic, there's no need to
// add/subtract bias when building an applying the query profile. The lowest
// value we can use is 0x8000, greatest is 0x7fff.
typedef int16_t TCScore;
/**
* Build query profile look up tables for the read. The query profile look
* up table is organized as a 1D array indexed by [i][j] where i is the
* reference character in the current DP column (0=A, 1=C, etc), and j is
* the segment of the query we're currently working on.
*/
void SwAligner::buildQueryProfileLocalSseI16(bool fw) {
bool& done = fw ? sseI16fwBuilt_ : sseI16rcBuilt_;
if(done) {
return;
}
done = true;
const BTDnaString* rd = fw ? rdfw_ : rdrc_;
const BTString* qu = fw ? qufw_ : qurc_;
const size_t len = rd->length();
const size_t seglen = (len + (NWORDS_PER_REG-1)) / NWORDS_PER_REG;
// How many __m128i's are needed
size_t n128s =
64 + // slack bytes, for alignment?
(seglen * ALPHA_SIZE) // query profile data
* 2; // & gap barrier data
assert_gt(n128s, 0);
SSEData& d = fw ? sseI16fw_ : sseI16rc_;
d.profbuf_.resizeNoCopy(n128s);
assert(!d.profbuf_.empty());
d.maxPen_ = d.maxBonus_ = 0;
d.lastIter_ = d.lastWord_ = 0;
d.qprofStride_ = d.gbarStride_ = 2;
d.bias_ = 0; // no bias when words are signed
// For each reference character A, C, G, T, N ...
for(size_t refc = 0; refc < ALPHA_SIZE; refc++) {
// For each segment ...
for(size_t i = 0; i < seglen; i++) {
size_t j = i;
int16_t *qprofWords =
reinterpret_cast<int16_t*>(d.profbuf_.ptr() + (refc * seglen * 2) + (i * 2));
int16_t *gbarWords =
reinterpret_cast<int16_t*>(d.profbuf_.ptr() + (refc * seglen * 2) + (i * 2) + 1);
// For each sub-word (byte) ...
for(size_t k = 0; k < NWORDS_PER_REG; k++) {
int sc = 0;
*gbarWords = 0;
if(j < len) {
int readc = (*rd)[j];
int readq = (*qu)[j];
sc = sc_->score(readc, (int)(1 << refc), readq - 33);
size_t j_from_end = len - j - 1;
if(j < (size_t)sc_->gapbar ||
j_from_end < (size_t)sc_->gapbar)
{
// Inside the gap barrier
*gbarWords = 0x8000; // add this twice
}
}
if(refc == 0 && j == len-1) {
// Remember which 128-bit word and which smaller word has
// the final row
d.lastIter_ = i;
d.lastWord_ = k;
}
if(sc < 0) {
if((size_t)(-sc) > d.maxPen_) {
d.maxPen_ = (size_t)(-sc);
}
} else {
if((size_t)sc > d.maxBonus_) {
d.maxBonus_ = (size_t)sc;
}
}
*qprofWords = (int16_t)sc;
gbarWords++;
qprofWords++;
j += seglen; // update offset into query
}
}
}
}
#ifndef NDEBUG
/**
* Return true iff the cell has sane E/F/H values w/r/t its predecessors.
*/
static bool cellOkLocalI16(
SSEData& d,
size_t row,
size_t col,
int refc,
int readc,
int readq,
const Scoring& sc) // scoring scheme
{
TCScore floorsc = MIN_I16;
TCScore ceilsc = MIN_I16-1;
TAlScore offsetsc = 0x8000;
TAlScore sc_h_cur = (TAlScore)d.mat_.helt(row, col);
TAlScore sc_e_cur = (TAlScore)d.mat_.eelt(row, col);
TAlScore sc_f_cur = (TAlScore)d.mat_.felt(row, col);
if(sc_h_cur > floorsc) {
sc_h_cur += offsetsc;
}
if(sc_e_cur > floorsc) {
sc_e_cur += offsetsc;
}
if(sc_f_cur > floorsc) {
sc_f_cur += offsetsc;
}
bool gapsAllowed = true;
size_t rowFromEnd = d.mat_.nrow() - row - 1;
if(row < (size_t)sc.gapbar || rowFromEnd < (size_t)sc.gapbar) {
gapsAllowed = false;
}
bool e_left_trans = false, h_left_trans = false;
bool f_up_trans = false, h_up_trans = false;
bool h_diag_trans = false;
if(gapsAllowed) {
TAlScore sc_h_left = floorsc;
TAlScore sc_e_left = floorsc;
TAlScore sc_h_up = floorsc;
TAlScore sc_f_up = floorsc;
if(col > 0 && sc_e_cur > floorsc && sc_e_cur <= ceilsc) {
sc_h_left = d.mat_.helt(row, col-1) + offsetsc;
sc_e_left = d.mat_.eelt(row, col-1) + offsetsc;
e_left_trans = (sc_e_left > floorsc && sc_e_cur == sc_e_left - sc.readGapExtend());
h_left_trans = (sc_h_left > floorsc && sc_e_cur == sc_h_left - sc.readGapOpen());
assert(e_left_trans || h_left_trans);
}
if(row > 0 && sc_f_cur > floorsc && sc_f_cur <= ceilsc) {
sc_h_up = d.mat_.helt(row-1, col) + offsetsc;
sc_f_up = d.mat_.felt(row-1, col) + offsetsc;
f_up_trans = (sc_f_up > floorsc && sc_f_cur == sc_f_up - sc.refGapExtend());
h_up_trans = (sc_h_up > floorsc && sc_f_cur == sc_h_up - sc.refGapOpen());
assert(f_up_trans || h_up_trans);
}
} else {
assert_geq(floorsc, sc_e_cur);
assert_geq(floorsc, sc_f_cur);
}
if(col > 0 && row > 0 && sc_h_cur > floorsc && sc_h_cur <= ceilsc) {
TAlScore sc_h_upleft = d.mat_.helt(row-1, col-1) + offsetsc;
TAlScore sc_diag = sc.score(readc, (int)refc, readq - 33);
h_diag_trans = sc_h_cur == sc_h_upleft + sc_diag;
}
assert(
sc_h_cur <= floorsc ||
e_left_trans ||
h_left_trans ||
f_up_trans ||
h_up_trans ||
h_diag_trans ||
sc_h_cur > ceilsc ||
row == 0 ||
col == 0);
return true;
}
#endif /*ndef NDEBUG*/
#ifdef NDEBUG
#define assert_all_eq0(x)
#define assert_all_gt(x, y)
#define assert_all_gt_lo(x)
#define assert_all_lt(x, y)
#define assert_all_lt_hi(x)
#else
#define assert_all_eq0(x) { \
__m128i z = _mm_setzero_si128(); \
__m128i tmp = _mm_setzero_si128(); \
z = _mm_xor_si128(z, z); \
tmp = _mm_cmpeq_epi16(x, z); \
assert_eq(0xffff, _mm_movemask_epi8(tmp)); \
}
#define assert_all_gt(x, y) { \
__m128i tmp = _mm_cmpgt_epi16(x, y); \
assert_eq(0xffff, _mm_movemask_epi8(tmp)); \
}
#define assert_all_gt_lo(x) { \
__m128i z = _mm_setzero_si128(); \
__m128i tmp = _mm_setzero_si128(); \
z = _mm_xor_si128(z, z); \
tmp = _mm_cmpgt_epi16(x, z); \
assert_eq(0xffff, _mm_movemask_epi8(tmp)); \
}
#define assert_all_lt(x, y) { \
__m128i tmp = _mm_cmplt_epi16(x, y); \
assert_eq(0xffff, _mm_movemask_epi8(tmp)); \
}
#define assert_all_leq(x, y) { \
__m128i tmp = _mm_cmpgt_epi16(x, y); \
assert_eq(0x0000, _mm_movemask_epi8(tmp)); \
}
#define assert_all_lt_hi(x) { \
__m128i z = _mm_setzero_si128(); \
__m128i tmp = _mm_setzero_si128(); \
z = _mm_cmpeq_epi16(z, z); \
z = _mm_srli_epi16(z, 1); \
tmp = _mm_cmplt_epi16(x, z); \
assert_eq(0xffff, _mm_movemask_epi8(tmp)); \
}
#endif
/**
* Aligns by filling a dynamic programming matrix with the SSE-accelerated,
* banded DP approach of Farrar. As it goes, it determines which cells we
* might backtrace from and tallies the best (highest-scoring) N backtrace
* candidate cells per diagonal. Also returns the alignment score of the best
* alignment in the matrix.
*
* This routine does *not* maintain a matrix holding the entire matrix worth of
* scores, nor does it maintain any other dense O(mn) data structure, as this
* would quickly exhaust memory for queries longer than about 10,000 kb.
* Instead, in the fill stage it maintains two columns worth of scores at a
* time (current/previous, or right/left) - these take O(m) space. When
* finished with the current column, it determines which cells from the
* previous column, if any, are candidates we might backtrace from to find a
* full alignment. A candidate cell has a score that rises above the threshold
* and isn't improved upon by a match in the next column. The best N
* candidates per diagonal are stored in a O(m + n) data structure.
*/
TAlScore SwAligner::alignGatherLoc16(int& flag, bool debug) {
assert_leq(rdf_, rd_->length());
assert_leq(rdf_, qu_->length());
assert_lt(rfi_, rff_);
assert_lt(rdi_, rdf_);
assert_eq(rd_->length(), qu_->length());
assert_geq(sc_->gapbar, 1);
assert_gt(minsc_, 0);
assert_leq(minsc_, MAX_I16);
assert(repOk());
#ifndef NDEBUG
for(size_t i = (size_t)rfi_; i < (size_t)rff_; i++) {
assert_range(0, 16, (int)rf_[i]);
}
#endif
SSEData& d = fw_ ? sseI16fw_ : sseI16rc_;
SSEMetrics& met = extend_ ? sseI16ExtendMet_ : sseI16MateMet_;
if(!debug) met.dp++;
buildQueryProfileLocalSseI16(fw_);
assert(!d.profbuf_.empty());
assert_gt(d.maxBonus_, 0);
size_t iter =
(dpRows() + (NWORDS_PER_REG-1)) / NWORDS_PER_REG; // iter = segLen
// Now set up the score vectors. We just need two columns worth, which
// we'll call "left" and "right".
d.vecbuf_.resize(ROWSTRIDE_2COL * iter * 2);
d.vecbuf_.zero();
__m128i *vbuf_l = d.vecbuf_.ptr();
__m128i *vbuf_r = d.vecbuf_.ptr() + (ROWSTRIDE_2COL * iter);
// This is the data structure that holds candidate cells per diagonal.
const size_t ndiags = rff_ - rfi_ + dpRows() - 1;
if(!debug) {
btdiag_.init(ndiags, 2);
}
// Data structure that holds checkpointed anti-diagonals
TAlScore perfectScore = sc_->perfectScore(dpRows());
bool checkpoint = true;
bool cpdebug = false;
#ifndef NDEBUG
cpdebug = dpRows() < 1000;
#endif
cper_.init(
dpRows(), // # rows
rff_ - rfi_, // # columns
cperPerPow2_, // checkpoint every 1 << perpow2 diags (& next)
perfectScore, // perfect score (for sanity checks)
false, // matrix cells have 8-bit scores?
cperTri_, // triangular mini-fills?
true, // alignment is local?
cpdebug); // save all cells for debugging?
// Many thanks to Michael Farrar for releasing his striped Smith-Waterman
// implementation:
//
// http://sites.google.com/site/farrarmichael/smith-waterman
//
// Much of the implmentation below is adapted from Michael's code.
// Set all elts to reference gap open penalty
__m128i rfgapo = _mm_setzero_si128();
__m128i rfgape = _mm_setzero_si128();
__m128i rdgapo = _mm_setzero_si128();
__m128i rdgape = _mm_setzero_si128();
__m128i vlo = _mm_setzero_si128();
__m128i vhi = _mm_setzero_si128();
__m128i vlolsw = _mm_setzero_si128();
__m128i vmax = _mm_setzero_si128();
__m128i vcolmax = _mm_setzero_si128();
__m128i vmaxtmp = _mm_setzero_si128();
__m128i ve = _mm_setzero_si128();
__m128i vf = _mm_setzero_si128();
__m128i vh = _mm_setzero_si128();
__m128i vhd = _mm_setzero_si128();
__m128i vhdtmp = _mm_setzero_si128();
__m128i vtmp = _mm_setzero_si128();
__m128i vzero = _mm_setzero_si128();
__m128i vminsc = _mm_setzero_si128();
assert_gt(sc_->refGapOpen(), 0);
assert_leq(sc_->refGapOpen(), MAX_I16);
rfgapo = _mm_insert_epi16(rfgapo, sc_->refGapOpen(), 0);
rfgapo = _mm_shufflelo_epi16(rfgapo, 0);
rfgapo = _mm_shuffle_epi32(rfgapo, 0);
// Set all elts to reference gap extension penalty
assert_gt(sc_->refGapExtend(), 0);
assert_leq(sc_->refGapExtend(), MAX_I16);
assert_leq(sc_->refGapExtend(), sc_->refGapOpen());
rfgape = _mm_insert_epi16(rfgape, sc_->refGapExtend(), 0);
rfgape = _mm_shufflelo_epi16(rfgape, 0);
rfgape = _mm_shuffle_epi32(rfgape, 0);
// Set all elts to read gap open penalty
assert_gt(sc_->readGapOpen(), 0);
assert_leq(sc_->readGapOpen(), MAX_I16);
rdgapo = _mm_insert_epi16(rdgapo, sc_->readGapOpen(), 0);
rdgapo = _mm_shufflelo_epi16(rdgapo, 0);
rdgapo = _mm_shuffle_epi32(rdgapo, 0);
// Set all elts to read gap extension penalty
assert_gt(sc_->readGapExtend(), 0);
assert_leq(sc_->readGapExtend(), MAX_I16);
assert_leq(sc_->readGapExtend(), sc_->readGapOpen());
rdgape = _mm_insert_epi16(rdgape, sc_->readGapExtend(), 0);
rdgape = _mm_shufflelo_epi16(rdgape, 0);
rdgape = _mm_shuffle_epi32(rdgape, 0);
// Set all elts to minimum score threshold. Actually, to 1 less than the
// threshold so we can use gt instead of geq.
vminsc = _mm_insert_epi16(vminsc, (int)minsc_-1, 0);
vminsc = _mm_shufflelo_epi16(vminsc, 0);
vminsc = _mm_shuffle_epi32(vminsc, 0);
// Set all elts to 0x8000 (min value for signed 16-bit)
vlo = _mm_cmpeq_epi16(vlo, vlo); // all elts = 0xffff
vlo = _mm_slli_epi16(vlo, NBITS_PER_WORD-1); // all elts = 0x8000
// Set all elts to 0x7fff (max value for signed 16-bit)
vhi = _mm_cmpeq_epi16(vhi, vhi); // all elts = 0xffff
vhi = _mm_srli_epi16(vhi, 1); // all elts = 0x7fff
// Set all elts to 0x8000 (min value for signed 16-bit)
vmax = vlo;
// vlolsw: topmost (least sig) word set to 0x8000, all other words=0
vlolsw = _mm_shuffle_epi32(vlo, 0);
vlolsw = _mm_srli_si128(vlolsw, NBYTES_PER_REG - NBYTES_PER_WORD);
// Points to a long vector of __m128i where each element is a block of
// contiguous cells in the E, F or H matrix. If the index % 3 == 0, then
// the block of cells is from the E matrix. If index % 3 == 1, they're
// from the F matrix. If index % 3 == 2, then they're from the H matrix.
// Blocks of cells are organized in the same interleaved manner as they are
// calculated by the Farrar algorithm.
const __m128i *pvScore; // points into the query profile
const size_t colstride = ROWSTRIDE_2COL * iter;
// Initialize the H and E vectors in the first matrix column
__m128i *pvELeft = vbuf_l + 0; __m128i *pvERight = vbuf_r + 0;
//__m128i *pvFLeft = vbuf_l + 1;
__m128i *pvFRight = vbuf_r + 1;
__m128i *pvHLeft = vbuf_l + 2; __m128i *pvHRight = vbuf_r + 2;
for(size_t i = 0; i < iter; i++) {
// start low in local mode
_mm_store_si128(pvERight, vlo); pvERight += ROWSTRIDE_2COL;
_mm_store_si128(pvHRight, vlo); pvHRight += ROWSTRIDE_2COL;
// Note: right and left are going to be swapped as soon as we enter
// the outer loop below
}
assert_gt(sc_->gapbar, 0);
size_t nfixup = 0;
TAlScore matchsc = sc_->match(30);
TAlScore leftmax = MIN_I64;
// Fill in the table as usual but instead of using the same gap-penalty
// vector for each iteration of the inner loop, load words out of a
// pre-calculated gap vector parallel to the query profile. The pre-
// calculated gap vectors enforce the gap barrier constraint by making it
// infinitely costly to introduce a gap in barrier rows.
//
// AND use a separate loop to fill in the first row of the table, enforcing
// the st_ constraints in the process. This is awkward because it
// separates the processing of the first row from the others and might make
// it difficult to use the first-row results in the next row, but it might
// be the simplest and least disruptive way to deal with the st_ constraint.
size_t off = MAX_SIZE_T, lastoff;
bool bailed = false;
for(size_t i = (size_t)rfi_; i < (size_t)rff_; i++) {
// Swap left and right; vbuf_l is the vector on the left, which we
// generally load from, and vbuf_r is the vector on the right, which we
// generally store to.
swap(vbuf_l, vbuf_r);
pvELeft = vbuf_l + 0; pvERight = vbuf_r + 0;
/* pvFLeft = vbuf_l + 1; */ pvFRight = vbuf_r + 1;
pvHLeft = vbuf_l + 2; pvHRight = vbuf_r + 2;
// Fetch this column's reference mask
const int refm = (int)rf_[i];
// Fetch the appropriate query profile
lastoff = off;
off = (size_t)firsts5[refm] * iter * 2;
pvScore = d.profbuf_.ptr() + off; // even elts = query profile, odd = gap barrier
// Load H vector from the final row of the previous column.
// ??? perhaps we should calculate the next iter's F instead of the
// current iter's? The way we currently do it, seems like it will
// almost always require at least one fixup loop iter (to recalculate
// this topmost F).
vh = _mm_load_si128(pvHLeft + colstride - ROWSTRIDE_2COL);
// Set all F cells to low value
vf = _mm_cmpeq_epi16(vf, vf);
vf = _mm_slli_epi16(vf, NBITS_PER_WORD-1);
vf = _mm_or_si128(vf, vlolsw);
// vf now contains the vertical contribution
// Store cells in F, calculated previously
// No need to veto ref gap extensions, they're all 0x8000s
_mm_store_si128(pvFRight, vf);
pvFRight += ROWSTRIDE_2COL;
// Shift down so that topmost (least sig) cell gets 0
vh = _mm_slli_si128(vh, NBYTES_PER_WORD);
// Fill topmost (least sig) cell with low value
vh = _mm_or_si128(vh, vlolsw);
// We pull out one loop iteration to make it easier to veto values in the top row
// Load cells from E, calculated previously
ve = _mm_load_si128(pvELeft);
vhd = _mm_load_si128(pvHLeft);
assert_all_lt(ve, vhi);
pvELeft += ROWSTRIDE_2COL;
// ve now contains the horizontal contribution
// Factor in query profile (matches and mismatches)
vh = _mm_adds_epi16(vh, pvScore[0]);
// vh now contains the diagonal contribution
// Update vE value
vhdtmp = vhd;
vhd = _mm_subs_epi16(vhd, rdgapo);
vhd = _mm_adds_epi16(vhd, pvScore[1]); // veto some read gap opens
vhd = _mm_adds_epi16(vhd, pvScore[1]); // veto some read gap opens
ve = _mm_subs_epi16(ve, rdgape);
ve = _mm_max_epi16(ve, vhd);
// Update H, factoring in E and F
vh = _mm_max_epi16(vh, ve);
// F won't change anything!
vf = vh;
// Update highest score so far
vcolmax = vh;
// Save the new vH values
_mm_store_si128(pvHRight, vh);
assert_all_lt(ve, vhi);
vh = vhdtmp;
assert_all_lt(ve, vhi);
pvHRight += ROWSTRIDE_2COL;
pvHLeft += ROWSTRIDE_2COL;
// Save E values
_mm_store_si128(pvERight, ve);
pvERight += ROWSTRIDE_2COL;
// Update vf value
vf = _mm_subs_epi16(vf, rfgapo);
assert_all_lt(vf, vhi);
pvScore += 2; // move on to next query profile
// For each character in the reference text:
size_t j;
for(j = 1; j < iter; j++) {
// Load cells from E, calculated previously
ve = _mm_load_si128(pvELeft);
vhd = _mm_load_si128(pvHLeft);
assert_all_lt(ve, vhi);
pvELeft += ROWSTRIDE_2COL;
// Store cells in F, calculated previously
vf = _mm_adds_epi16(vf, pvScore[1]); // veto some ref gap extensions
vf = _mm_adds_epi16(vf, pvScore[1]); // veto some ref gap extensions
_mm_store_si128(pvFRight, vf);
pvFRight += ROWSTRIDE_2COL;
// Factor in query profile (matches and mismatches)
vh = _mm_adds_epi16(vh, pvScore[0]);
vh = _mm_max_epi16(vh, vf);
// Update vE value
vhdtmp = vhd;
vhd = _mm_subs_epi16(vhd, rdgapo);
vhd = _mm_adds_epi16(vhd, pvScore[1]); // veto some read gap opens
vhd = _mm_adds_epi16(vhd, pvScore[1]); // veto some read gap opens
ve = _mm_subs_epi16(ve, rdgape);
ve = _mm_max_epi16(ve, vhd);
vh = _mm_max_epi16(vh, ve);
vtmp = vh;
// Update highest score encountered this far
vcolmax = _mm_max_epi16(vcolmax, vh);
// Save the new vH values
_mm_store_si128(pvHRight, vh);
vh = vhdtmp;
assert_all_lt(ve, vhi);
pvHRight += ROWSTRIDE_2COL;
pvHLeft += ROWSTRIDE_2COL;
// Save E values
_mm_store_si128(pvERight, ve);
pvERight += ROWSTRIDE_2COL;
// Update vf value
vtmp = _mm_subs_epi16(vtmp, rfgapo);
vf = _mm_subs_epi16(vf, rfgape);
assert_all_lt(vf, vhi);
vf = _mm_max_epi16(vf, vtmp);
pvScore += 2; // move on to next query profile / gap veto
}
// pvHStore, pvELoad, pvEStore have all rolled over to the next column
pvFRight -= colstride; // reset to start of column
vtmp = _mm_load_si128(pvFRight);
pvHRight -= colstride; // reset to start of column
vh = _mm_load_si128(pvHRight);
pvScore = d.profbuf_.ptr() + off + 1; // reset veto vector
// vf from last row gets shifted down by one to overlay the first row
// rfgape has already been subtracted from it.
vf = _mm_slli_si128(vf, NBYTES_PER_WORD);
vf = _mm_or_si128(vf, vlolsw);
vf = _mm_adds_epi16(vf, *pvScore); // veto some ref gap extensions
vf = _mm_adds_epi16(vf, *pvScore); // veto some ref gap extensions
vf = _mm_max_epi16(vtmp, vf);
vtmp = _mm_cmpgt_epi16(vf, vtmp);
int cmp = _mm_movemask_epi8(vtmp);
// If any element of vtmp is greater than H - gap-open...
j = 0;
while(cmp != 0x0000) {
// Store this vf
_mm_store_si128(pvFRight, vf);
pvFRight += ROWSTRIDE_2COL;
// Update vh w/r/t new vf
vh = _mm_max_epi16(vh, vf);
// Save vH values
_mm_store_si128(pvHRight, vh);
pvHRight += ROWSTRIDE_2COL;
// Update highest score encountered so far.
vcolmax = _mm_max_epi16(vcolmax, vh);
pvScore += 2;
assert_lt(j, iter);
if(++j == iter) {
pvFRight -= colstride;
vtmp = _mm_load_si128(pvFRight); // load next vf ASAP
pvHRight -= colstride;
vh = _mm_load_si128(pvHRight); // load next vh ASAP
pvScore = d.profbuf_.ptr() + off + 1;
j = 0;
vf = _mm_slli_si128(vf, NBYTES_PER_WORD);
vf = _mm_or_si128(vf, vlolsw);
} else {
vtmp = _mm_load_si128(pvFRight); // load next vf ASAP
vh = _mm_load_si128(pvHRight); // load next vh ASAP
}
// Update F with another gap extension
vf = _mm_subs_epi16(vf, rfgape);
vf = _mm_adds_epi16(vf, *pvScore); // veto some ref gap extensions
vf = _mm_adds_epi16(vf, *pvScore); // veto some ref gap extensions
vf = _mm_max_epi16(vtmp, vf);
vtmp = _mm_cmpgt_epi16(vf, vtmp);
cmp = _mm_movemask_epi8(vtmp);
nfixup++;
}
// Now we'd like to know exactly which cells in the left column are
// candidates we might backtrace from. First question is: did *any*
// elements in the column exceed the minimum score threshold?
if(!debug && leftmax >= minsc_) {
// Yes. Next question is: which cells are candidates? We have to
// allow matches in the right column to override matches above and
// to the left in the left column.
assert_gt(i - rfi_, 0);
pvHLeft = vbuf_l + 2;
assert_lt(lastoff, MAX_SIZE_T);
pvScore = d.profbuf_.ptr() + lastoff; // even elts = query profile, odd = gap barrier
for(size_t k = 0; k < iter; k++) {
vh = _mm_load_si128(pvHLeft);
vtmp = _mm_cmpgt_epi16(pvScore[0], vzero);
int cmp = _mm_movemask_epi8(vtmp);
if(cmp != 0) {
// At least one candidate in this mask. Now iterate
// through vm/vh to evaluate individual cells.
for(size_t m = 0; m < NWORDS_PER_REG; m++) {
size_t row = k + m * iter;
if(row >= dpRows()) {
break;
}
TAlScore sc = (TAlScore)(((TCScore *)&vh)[m] + 0x8000);
if(sc >= minsc_) {
if(((TCScore *)&vtmp)[m] != 0) {
// Add to data structure holding all candidates
size_t col = i - rfi_ - 1; // -1 b/c prev col
size_t frombot = dpRows() - row - 1;
DpBtCandidate cand(row, col, sc);
btdiag_.add(frombot + col, cand);
}
}
}
}
pvHLeft += ROWSTRIDE_2COL;
pvScore += 2;
}
}
// Save some elements to checkpoints
if(checkpoint) {
__m128i *pvE = vbuf_r + 0;
__m128i *pvF = vbuf_r + 1;
__m128i *pvH = vbuf_r + 2;
size_t coli = i - rfi_;
if(coli < cper_.locol_) cper_.locol_ = coli;
if(coli > cper_.hicol_) cper_.hicol_ = coli;
if(cperTri_) {
size_t rc_mod = coli & cper_.lomask_;
assert_lt(rc_mod, cper_.per_);
int64_t row = -rc_mod-1;
int64_t row_mod = row;
int64_t row_div = 0;
size_t idx = coli >> cper_.perpow2_;
size_t idxrow = idx * cper_.nrow_;
assert_eq(4, ROWSTRIDE_2COL);
bool done = false;
while(true) {
row += (cper_.per_ - 2);
row_mod += (cper_.per_ - 2);
for(size_t j = 0; j < 2; j++) {
row++;
row_mod++;
if(row >= 0 && (size_t)row < cper_.nrow_) {
// Update row divided by iter_ and mod iter_
while(row_mod >= (int64_t)iter) {
row_mod -= (int64_t)iter;
row_div++;
}
size_t delt = idxrow + row;
size_t vecoff = (row_mod << 5) + row_div;
assert_lt(row_div, 8);
int16_t h_sc = ((int16_t*)pvH)[vecoff];
int16_t e_sc = ((int16_t*)pvE)[vecoff];
int16_t f_sc = ((int16_t*)pvF)[vecoff];
h_sc += 0x8000; assert_geq(h_sc, 0);
e_sc += 0x8000; assert_geq(e_sc, 0);
f_sc += 0x8000; assert_geq(f_sc, 0);
assert_leq(h_sc, cper_.perf_);
assert_leq(e_sc, cper_.perf_);
assert_leq(f_sc, cper_.perf_);
CpQuad *qdiags = ((j == 0) ? cper_.qdiag1s_.ptr() : cper_.qdiag2s_.ptr());
qdiags[delt].sc[0] = h_sc;
qdiags[delt].sc[1] = e_sc;
qdiags[delt].sc[2] = f_sc;
} // if(row >= 0 && row < nrow_)
else if(row >= 0 && (size_t)row >= cper_.nrow_) {
done = true;
break;
}
} // end of loop over anti-diags
if(done) {
break;
}
idx++;
idxrow += cper_.nrow_;
}
} else {
// If this is the first column, take this opportunity to
// pre-calculate the coordinates of the elements we're going to
// checkpoint.
if(coli == 0) {
size_t cpi = cper_.per_-1;
size_t cpimod = cper_.per_-1;
size_t cpidiv = 0;
cper_.commitMap_.clear();
while(cpi < cper_.nrow_) {
while(cpimod >= iter) {
cpimod -= iter;
cpidiv++;
}
size_t vecoff = (cpimod << 5) + cpidiv;
cper_.commitMap_.push_back(vecoff);
cpi += cper_.per_;
cpimod += cper_.per_;
}
}
// Save all the rows
size_t rowoff = 0;
size_t sz = cper_.commitMap_.size();
for(size_t i = 0; i < sz; i++, rowoff += cper_.ncol_) {
size_t vecoff = cper_.commitMap_[i];
int16_t h_sc = ((int16_t*)pvH)[vecoff];
//int16_t e_sc = ((int16_t*)pvE)[vecoff];
int16_t f_sc = ((int16_t*)pvF)[vecoff];
h_sc += 0x8000; assert_geq(h_sc, 0);
//e_sc += 0x8000; assert_geq(e_sc, 0);
f_sc += 0x8000; assert_geq(f_sc, 0);
assert_leq(h_sc, cper_.perf_);
//assert_leq(e_sc, cper_.perf_);
assert_leq(f_sc, cper_.perf_);
CpQuad& dst = cper_.qrows_[rowoff + coli];
dst.sc[0] = h_sc;
//dst.sc[1] = e_sc;
dst.sc[2] = f_sc;
}
// Is this a column we'd like to checkpoint?
if((coli & cper_.lomask_) == cper_.lomask_) {
// Save the column using memcpys
assert_gt(coli, 0);
size_t wordspercol = cper_.niter_ * ROWSTRIDE_2COL;
size_t coloff = (coli >> cper_.perpow2_) * wordspercol;
__m128i *dst = cper_.qcols_.ptr() + coloff;
memcpy(dst, vbuf_r, sizeof(__m128i) * wordspercol);
}
}
if(cper_.debug_) {
// Save the column using memcpys
size_t wordspercol = cper_.niter_ * ROWSTRIDE_2COL;
size_t coloff = coli * wordspercol;
__m128i *dst = cper_.qcolsD_.ptr() + coloff;
memcpy(dst, vbuf_r, sizeof(__m128i) * wordspercol);
}
}
vmax = _mm_max_epi16(vmax, vcolmax);
{
// Get single largest score in this column
vmaxtmp = vcolmax;
vtmp = _mm_srli_si128(vmaxtmp, 8);
vmaxtmp = _mm_max_epi16(vmaxtmp, vtmp);
vtmp = _mm_srli_si128(vmaxtmp, 4);
vmaxtmp = _mm_max_epi16(vmaxtmp, vtmp);
vtmp = _mm_srli_si128(vmaxtmp, 2);
vmaxtmp = _mm_max_epi16(vmaxtmp, vtmp);
int16_t ret = _mm_extract_epi16(vmaxtmp, 0);
TAlScore score = (TAlScore)(ret + 0x8000);
if(ret == MIN_I16) {
score = MIN_I64;
}
if(score < minsc_) {
size_t ncolleft = rff_ - i - 1;
if(max<TAlScore>(score, 0) + (TAlScore)ncolleft * matchsc < minsc_) {
// Bail! There can't possibly be a valid alignment that
// passes through this column.
bailed = true;
break;
}
}
leftmax = score;
}
}
lastoff = off;
// Now we'd like to know exactly which cells in the *rightmost* column are
// candidates we might backtrace from. Did *any* elements exceed the
// minimum score threshold?
if(!debug && !bailed && leftmax >= minsc_) {
// Yes. Next question is: which cells are candidates? We have to
// allow matches in the right column to override matches above and
// to the left in the left column.
pvHLeft = vbuf_r + 2;
assert_lt(lastoff, MAX_SIZE_T);
pvScore = d.profbuf_.ptr() + lastoff; // even elts = query profile, odd = gap barrier
for(size_t k = 0; k < iter; k++) {
vh = _mm_load_si128(pvHLeft);
vtmp = _mm_cmpgt_epi16(pvScore[0], vzero);
int cmp = _mm_movemask_epi8(vtmp);
if(cmp != 0) {
// At least one candidate in this mask. Now iterate
// through vm/vh to evaluate individual cells.
for(size_t m = 0; m < NWORDS_PER_REG; m++) {
size_t row = k + m * iter;
if(row >= dpRows()) {
break;
}
TAlScore sc = (TAlScore)(((TCScore *)&vh)[m] + 0x8000);
if(sc >= minsc_) {
if(((TCScore *)&vtmp)[m] != 0) {
// Add to data structure holding all candidates
size_t col = rff_ - rfi_ - 1; // -1 b/c prev col
size_t frombot = dpRows() - row - 1;
DpBtCandidate cand(row, col, sc);
btdiag_.add(frombot + col, cand);
}
}
}
}
pvHLeft += ROWSTRIDE_2COL;
pvScore += 2;
}
}
// Find largest score in vmax
vtmp = _mm_srli_si128(vmax, 8);
vmax = _mm_max_epi16(vmax, vtmp);
vtmp = _mm_srli_si128(vmax, 4);
vmax = _mm_max_epi16(vmax, vtmp);
vtmp = _mm_srli_si128(vmax, 2);
vmax = _mm_max_epi16(vmax, vtmp);
int16_t ret = _mm_extract_epi16(vmax, 0);
// Update metrics
if(!debug) {
size_t ninner = (rff_ - rfi_) * iter;
met.col += (rff_ - rfi_); // DP columns
met.cell += (ninner * NWORDS_PER_REG); // DP cells
met.inner += ninner; // DP inner loop iters
met.fixup += nfixup; // DP fixup loop iters
}
flag = 0;
// Did we find a solution?
TAlScore score = MIN_I64;
if(ret == MIN_I16) {
flag = -1; // no
if(!debug) met.dpfail++;
return MIN_I64;
} else {
score = (TAlScore)(ret + 0x8000);
if(score < minsc_) {
flag = -1; // no
if(!debug) met.dpfail++;
return score;
}
}
// Could we have saturated?
if(ret == MAX_I16) {
flag = -2; // yes
if(!debug) met.dpsat++;
return MIN_I64;
}
// Now take all the backtrace candidates in the btdaig_ structure and
// dump them into the btncand_ array. They'll be sorted later.
if(!debug) {
btdiag_.dump(btncand_);
assert(!btncand_.empty());
}
// Return largest score
if(!debug) met.dpsucc++;
return score;
}
/**
* Solve the current alignment problem using SSE instructions that operate on 8
* signed 16-bit values packed into a single 128-bit register.
*/
TAlScore SwAligner::alignNucleotidesLocalSseI16(int& flag, bool debug) {
assert_leq(rdf_, rd_->length());
assert_leq(rdf_, qu_->length());
assert_lt(rfi_, rff_);
assert_lt(rdi_, rdf_);
assert_eq(rd_->length(), qu_->length());
assert_geq(sc_->gapbar, 1);
assert(repOk());
#ifndef NDEBUG
for(size_t i = (size_t)rfi_; i < (size_t)rff_; i++) {
assert_range(0, 16, (int)rf_[i]);
}
#endif
SSEData& d = fw_ ? sseI16fw_ : sseI16rc_;
SSEMetrics& met = extend_ ? sseI16ExtendMet_ : sseI16MateMet_;
if(!debug) met.dp++;
buildQueryProfileLocalSseI16(fw_);
assert(!d.profbuf_.empty());
assert_gt(d.maxBonus_, 0);
size_t iter =
(dpRows() + (NWORDS_PER_REG-1)) / NWORDS_PER_REG; // iter = segLen
// Many thanks to Michael Farrar for releasing his striped Smith-Waterman
// implementation:
//
// http://sites.google.com/site/farrarmichael/smith-waterman
//
// Much of the implmentation below is adapted from Michael's code.
// Set all elts to reference gap open penalty
__m128i rfgapo = _mm_setzero_si128();
__m128i rfgape = _mm_setzero_si128();