colmap/lib/PBA/SparseBundleCPU.cpp

////////////////////////////////////////////////////////////////////////////
//  File:           SparseBundleCPU.cpp
//  Author:         Changchang Wu
//  Description :   implementation of the CPU-based multicore bundle adjustment
//
//  Copyright (c) 2011  Changchang Wu (ccwu@cs.washington.edu)
//    and the University of Washington at Seattle
//
//  This library 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.
//
//  This library 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.
//
////////////////////////////////////////////////////////////////////////////////

#include <stdlib.h>
#include <vector>
#include <iostream>
#include <utility>
#include <algorithm>
#include <fstream>
#include <sstream>
#include <iomanip>
#include <cmath>

using std::vector;
using std::cout;
using std::pair;
using std::ofstream;
using std::max;

#include <math.h>
#include <time.h>
#include <float.h>
#include "pba.h"
#include "SparseBundleCPU.h"

#if defined(WINAPI_FAMILY) && WINAPI_FAMILY == WINAPI_FAMILY_APP
#include <thread>
#endif

//#define POINT_DATA_ALIGN4
#if defined(__arm__) || defined(_M_ARM) || defined(__aarch64__)
#undef CPUPBA_USE_SSE
#undef CPUPBA_USE_AVX
#undef POINT_DATA_ALIGN4
#if defined(_M_ARM) && _M_ARM >= 7 && !defined(DISABLE_CPU_NEON)
#include <arm_neon.h>
#define CPUPBA_USE_NEON
#elif defined(__ARM_NEON) && !defined(DISABLE_CPU_NEON)
#include <arm_neon.h>
#define CPUPBA_USE_NEON
#endif
#elif defined(__AVX__) && !defined(DISABLE_CPU_AVX)
#include <immintrin.h>
#define CPUPBA_USE_AVX
#undef CPUPBA_USE_SSE
#undef POINT_DATA_ALIGN4
#elif (defined(__SSE2__) || defined(_M_X64) || (defined(_M_IX86) && _M_IX86_FP >= 2)) && !defined(DISABLE_CPU_SSE)
#define CPUPBA_USE_SSE
#include <xmmintrin.h>
#include <emmintrin.h>
#endif

#ifdef POINT_DATA_ALIGN4
#define POINT_ALIGN 4
#else
#define POINT_ALIGN 3
#endif

#define POINT_ALIGN2 (POINT_ALIGN * 2)

#ifdef _WIN32
#define NOMINMAX
#include <windows.h>
#define INLINESUFIX
#define finite _finite
#else
#include <pthread.h>
#include <sched.h>
#include <unistd.h>
#endif

// maximum thread count
#define THREAD_NUM_MAX 64
// compute the number of threads for vector operatoins, pure heuristics...
#define AUTO_MT_NUM(sz) \
  int((log((double)sz) / log(2.0) - 18.5) * __num_cpu_cores / 16.0)

namespace pba {

template <class Float>
void avec<Float>::SaveToFile(const char* name) {
  ofstream out(name);
  for (Float* p = _data; p < _last; ++p) out << (*p) << '\n';
}

#ifdef CPUPBA_USE_SSE
#define CPUPBA_USE_SIMD
namespace MYSSE {
template <class Float>
class SSE {};
template <>
class SSE<float> {
 public:
  typedef __m128 sse_type;
  static inline sse_type zero() { return _mm_setzero_ps(); }
};
template <>
class SSE<double> {
 public:
  typedef __m128d sse_type;
  static inline sse_type zero() { return _mm_setzero_pd(); }
};

////////////////////////////////////////////
template <class Float>
inline size_t sse_step() {
  return 16 / sizeof(Float);
};
inline __m128 sse_load1(const float* p) { return _mm_load1_ps(p); }
inline __m128 sse_load(const float* p) { return _mm_load_ps(p); }
inline __m128 sse_add(__m128 s1, __m128 s2) { return _mm_add_ps(s1, s2); }
inline __m128 sse_sub(__m128 s1, __m128 s2) { return _mm_sub_ps(s1, s2); }
inline __m128 sse_mul(__m128 s1, __m128 s2) { return _mm_mul_ps(s1, s2); }
inline __m128 sse_sqrt(__m128 s) { return _mm_sqrt_ps(s); }

inline __m128d sse_load1(const double* p) { return _mm_load1_pd(p); }
inline __m128d sse_load(const double* p) { return _mm_load_pd(p); }
inline __m128d sse_add(__m128d s1, __m128d s2) { return _mm_add_pd(s1, s2); }
inline __m128d sse_sub(__m128d s1, __m128d s2) { return _mm_sub_pd(s1, s2); }
inline __m128d sse_mul(__m128d s1, __m128d s2) { return _mm_mul_pd(s1, s2); }
inline __m128d sse_sqrt(__m128d s) { return _mm_sqrt_pd(s); }

#ifdef _WIN32
inline float sse_sum(__m128 s) {
  return (s.m128_f32[0] + s.m128_f32[2]) + (s.m128_f32[1] + s.m128_f32[3]);
}
inline double sse_sum(__m128d s) { return s.m128d_f64[0] + s.m128d_f64[1]; }
#else
inline float sse_sum(__m128 s) {
  float* f = (float*)(&s);
  return (f[0] + f[2]) + (f[1] + f[3]);
}
inline double sse_sum(__m128d s) {
  double* d = (double*)(&s);
  return d[0] + d[1];
}
#endif
// inline float  sse_dot(__m128 s1, __m128 s2)  {__m128 temp = _mm_dp_ps(s1,
// s2, 0xF1);   float* f = (float*) (&temp); return f[0];   }
// inline double  sse_dot(__m128d s1, __m128d s2) {__m128d temp =
// _mm_dp_pd(s1, s2, 0x31);   double* f = (double*) (&temp); return f[0] ; }
inline void sse_store(float* p, __m128 s) { _mm_store_ps(p, s); }
inline void sse_store(double* p, __m128d s) { _mm_store_pd(p, s); }

inline void data_prefetch(const void* p) {
  _mm_prefetch((const char*)p, _MM_HINT_NTA);
}
};

namespace ProgramCPU {
using namespace MYSSE;
#define SSE_ZERO SSE<Float>::zero()
#define SSE_T typename SSE<Float>::sse_type
/////////////////////////////
inline void ScaleJ4(float* jcx, float* jcy, const float* sj) {
  __m128 ps = _mm_load_ps(sj);
  _mm_store_ps(jcx, _mm_mul_ps(_mm_load_ps(jcx), ps));
  _mm_store_ps(jcy, _mm_mul_ps(_mm_load_ps(jcy), ps));
}
inline void ScaleJ8(float* jcx, float* jcy, const float* sj) {
  ScaleJ4(jcx, jcy, sj);
  ScaleJ4(jcx + 4, jcy + 4, sj + 4);
}
inline void ScaleJ4(double* jcx, double* jcy, const double* sj) {
  __m128d ps1 = _mm_load_pd(sj), ps2 = _mm_load_pd(sj + 2);
  _mm_store_pd(jcx, _mm_mul_pd(_mm_load_pd(jcx), ps1));
  _mm_store_pd(jcy, _mm_mul_pd(_mm_load_pd(jcy), ps1));
  _mm_store_pd(jcx + 2, _mm_mul_pd(_mm_load_pd(jcx + 2), ps2));
  _mm_store_pd(jcy + 2, _mm_mul_pd(_mm_load_pd(jcy + 2), ps2));
}
inline void ScaleJ8(double* jcx, double* jcy, const double* sj) {
  ScaleJ4(jcx, jcy, sj);
  ScaleJ4(jcx + 4, jcy + 4, sj + 4);
}
inline float DotProduct8(const float* v1, const float* v2) {
  __m128 ds = _mm_add_ps(_mm_mul_ps(_mm_load_ps(v1), _mm_load_ps(v2)),
                         _mm_mul_ps(_mm_load_ps(v1 + 4), _mm_load_ps(v2 + 4)));
  return sse_sum(ds);
}
inline double DotProduct8(const double* v1, const double* v2) {
  __m128d d1 = _mm_mul_pd(_mm_load_pd(v1), _mm_load_pd(v2));
  __m128d d2 = _mm_mul_pd(_mm_load_pd(v1 + 2), _mm_load_pd(v2 + 2));
  __m128d d3 = _mm_mul_pd(_mm_load_pd(v1 + 4), _mm_load_pd(v2 + 4));
  __m128d d4 = _mm_mul_pd(_mm_load_pd(v1 + 6), _mm_load_pd(v2 + 6));
  __m128d ds = _mm_add_pd(_mm_add_pd(d1, d2), _mm_add_pd(d3, d4));
  return sse_sum(ds);
}

inline void ComputeTwoJX(const float* jc, const float* jp, const float* xc,
                         const float* xp, float* jx) {
#ifdef POINT_DATA_ALIGN4
  __m128 xc1 = _mm_load_ps(xc), xc2 = _mm_load_ps(xc + 4),
         mxp = _mm_load_ps(xp);
  __m128 ds1 = _mm_add_ps(_mm_mul_ps(_mm_load_ps(jc), xc1),
                          _mm_mul_ps(_mm_load_ps(jc + 4), xc2));
  __m128 dx1 = _mm_add_ps(ds1, _mm_mul_ps(_mm_load_ps(jp), mxp));
  jx[0] = sse_sum(dx1);
  __m128 ds2 = _mm_add_ps(_mm_mul_ps(_mm_load_ps(jc + 8), xc1),
                          _mm_mul_ps(_mm_load_ps(jc + 12), xc2));
  __m128 dx2 = _mm_add_ps(ds2, _mm_mul_ps(_mm_load_ps(jp + 4), mxp));
  jx[1] = sse_sum(dx2);
#else
  __m128 xc1 = _mm_load_ps(xc), xc2 = _mm_load_ps(xc + 4);
  __m128 jc1 = _mm_load_ps(jc), jc2 = _mm_load_ps(jc + 4);
  __m128 jc3 = _mm_load_ps(jc + 8), jc4 = _mm_load_ps(jc + 12);
  __m128 ds1 = _mm_add_ps(_mm_mul_ps(jc1, xc1), _mm_mul_ps(jc2, xc2));
  __m128 ds2 = _mm_add_ps(_mm_mul_ps(jc3, xc1), _mm_mul_ps(jc4, xc2));
  jx[0] = sse_sum(ds1) + (jp[0] * xp[0] + jp[1] * xp[1] + jp[2] * xp[2]);
  jx[1] =
      sse_sum(ds2) + (jp[POINT_ALIGN] * xp[0] + jp[POINT_ALIGN + 1] * xp[1] +
                      jp[POINT_ALIGN + 2] * xp[2]);
/*jx[0] = (sse_dot(jc1, xc1) + sse_dot(jc2, xc2)) + (jp[0] * xp[0] + jp[1] *
xp[1] + jp[2] * xp[2]);
jx[1] = (sse_dot(jc3, xc1) + sse_dot(jc4, xc2)) + (jp[POINT_ALIGN] * xp[0] +
jp[POINT_ALIGN+1] * xp[1] + jp[POINT_ALIGN+2] * xp[2]);*/
#endif
}

inline void ComputeTwoJX(const double* jc, const double* jp, const double* xc,
                         const double* xp, double* jx) {
  __m128d xc1 = _mm_load_pd(xc), xc2 = _mm_load_pd(xc + 2),
          xc3 = _mm_load_pd(xc + 4), xc4 = _mm_load_pd(xc + 6);
  __m128d d1 = _mm_mul_pd(_mm_load_pd(jc), xc1);
  __m128d d2 = _mm_mul_pd(_mm_load_pd(jc + 2), xc2);
  __m128d d3 = _mm_mul_pd(_mm_load_pd(jc + 4), xc3);
  __m128d d4 = _mm_mul_pd(_mm_load_pd(jc + 6), xc4);
  __m128d ds1 = _mm_add_pd(_mm_add_pd(d1, d2), _mm_add_pd(d3, d4));
  jx[0] = sse_sum(ds1) + (jp[0] * xp[0] + jp[1] * xp[1] + jp[2] * xp[2]);

  __m128d d5 = _mm_mul_pd(_mm_load_pd(jc + 8), xc1);
  __m128d d6 = _mm_mul_pd(_mm_load_pd(jc + 10), xc2);
  __m128d d7 = _mm_mul_pd(_mm_load_pd(jc + 12), xc3);
  __m128d d8 = _mm_mul_pd(_mm_load_pd(jc + 14), xc4);
  __m128d ds2 = _mm_add_pd(_mm_add_pd(d5, d6), _mm_add_pd(d7, d8));
  jx[1] =
      sse_sum(ds2) + (jp[POINT_ALIGN] * xp[0] + jp[POINT_ALIGN + 1] * xp[1] +
                      jp[POINT_ALIGN + 2] * xp[2]);
}

// v += ax
inline void AddScaledVec8(float a, const float* x, float* v) {
  __m128 aa = sse_load1(&a);
  _mm_store_ps(v, _mm_add_ps(_mm_mul_ps(_mm_load_ps(x), aa), _mm_load_ps(v)));
  _mm_store_ps(v + 4, _mm_add_ps(_mm_mul_ps(_mm_load_ps(x + 4), aa),
                                 _mm_load_ps(v + 4)));
}
// v += ax
inline void AddScaledVec8(double a, const double* x, double* v) {
  __m128d aa = sse_load1(&a);
  _mm_store_pd(v, _mm_add_pd(_mm_mul_pd(_mm_load_pd(x), aa), _mm_load_pd(v)));
  _mm_store_pd(v + 2, _mm_add_pd(_mm_mul_pd(_mm_load_pd(x + 2), aa),
                                 _mm_load_pd(v + 2)));
  _mm_store_pd(v + 4, _mm_add_pd(_mm_mul_pd(_mm_load_pd(x + 4), aa),
                                 _mm_load_pd(v + 4)));
  _mm_store_pd(v + 6, _mm_add_pd(_mm_mul_pd(_mm_load_pd(x + 6), aa),
                                 _mm_load_pd(v + 6)));
}

inline void AddBlockJtJ(const float* jc, float* block, int vn) {
  __m128 j1 = _mm_load_ps(jc);
  __m128 j2 = _mm_load_ps(jc + 4);
  for (int i = 0; i < vn; ++i, ++jc, block += 8) {
    __m128 a = sse_load1(jc);
    _mm_store_ps(block + 0,
                 _mm_add_ps(_mm_mul_ps(a, j1), _mm_load_ps(block + 0)));
    _mm_store_ps(block + 4,
                 _mm_add_ps(_mm_mul_ps(a, j2), _mm_load_ps(block + 4)));
  }
}

inline void AddBlockJtJ(const double* jc, double* block, int vn) {
  __m128d j1 = _mm_load_pd(jc);
  __m128d j2 = _mm_load_pd(jc + 2);
  __m128d j3 = _mm_load_pd(jc + 4);
  __m128d j4 = _mm_load_pd(jc + 6);
  for (int i = 0; i < vn; ++i, ++jc, block += 8) {
    __m128d a = sse_load1(jc);
    _mm_store_pd(block + 0,
                 _mm_add_pd(_mm_mul_pd(a, j1), _mm_load_pd(block + 0)));
    _mm_store_pd(block + 2,
                 _mm_add_pd(_mm_mul_pd(a, j2), _mm_load_pd(block + 2)));
    _mm_store_pd(block + 4,
                 _mm_add_pd(_mm_mul_pd(a, j3), _mm_load_pd(block + 4)));
    _mm_store_pd(block + 6,
                 _mm_add_pd(_mm_mul_pd(a, j4), _mm_load_pd(block + 6)));
  }
}
};
#endif

#ifdef CPUPBA_USE_AVX
#define CPUPBA_USE_SIMD
namespace MYAVX {
template <class Float>
class SSE {};
template <>
class SSE<float> {
 public:
  typedef __m256 sse_type;  // static size_t   step() {return 4;}
  static inline sse_type zero() { return _mm256_setzero_ps(); }
};
template <>
class SSE<double> {
 public:
  typedef __m256d sse_type;  // static size_t   step() {return 2;}
  static inline sse_type zero() { return _mm256_setzero_pd(); }
};

////////////////////////////////////////////
template <class Float>
inline size_t sse_step() {
  return 32 / sizeof(Float);
};
inline __m256 sse_load1(const float* p) { return _mm256_broadcast_ss(p); }
inline __m256 sse_load(const float* p) { return _mm256_load_ps(p); }
inline __m256 sse_add(__m256 s1, __m256 s2) { return _mm256_add_ps(s1, s2); }
inline __m256 sse_sub(__m256 s1, __m256 s2) { return _mm256_sub_ps(s1, s2); }
inline __m256 sse_mul(__m256 s1, __m256 s2) { return _mm256_mul_ps(s1, s2); }
inline __m256 sse_sqrt(__m256 s) { return _mm256_sqrt_ps(s); }

// inline __m256 sse_fmad(__m256 a, __m256 b, __m256 c) {return
// _mm256_fmadd_ps(a, b, c);}

inline __m256d sse_load1(const double* p) { return _mm256_broadcast_sd(p); }
inline __m256d sse_load(const double* p) { return _mm256_load_pd(p); }
inline __m256d sse_add(__m256d s1, __m256d s2) { return _mm256_add_pd(s1, s2); }
inline __m256d sse_sub(__m256d s1, __m256d s2) { return _mm256_sub_pd(s1, s2); }
inline __m256d sse_mul(__m256d s1, __m256d s2) { return _mm256_mul_pd(s1, s2); }
inline __m256d sse_sqrt(__m256d s) { return _mm256_sqrt_pd(s); }

#ifdef _WIN32
inline float sse_sum(__m256 s) {
  return ((s.m256_f32[0] + s.m256_f32[4]) + (s.m256_f32[2] + s.m256_f32[6])) +
         ((s.m256_f32[1] + s.m256_f32[5]) + (s.m256_f32[3] + s.m256_f32[7]));
}
inline double sse_sum(__m256d s) {
  return (s.m256d_f64[0] + s.m256d_f64[2]) + (s.m256d_f64[1] + s.m256d_f64[3]);
}
#else
inline float sse_sum(__m256 s) {
  float* f = (float*)(&s);
  return ((f[0] + f[4]) + (f[2] + f[6])) + ((f[1] + f[5]) + (f[3] + f[7]));
}
inline double sse_sum(__m256d s) {
  double* d = (double*)(&s);
  return (d[0] + d[2]) + (d[1] + d[3]);
}
#endif
inline float sse_dot(__m256 s1, __m256 s2) {
  __m256 temp = _mm256_dp_ps(s1, s2, 0xf1);
  float* f = (float*)(&temp);
  return f[0] + f[4];
}
inline double sse_dot(__m256d s1, __m256d s2) {
  return sse_sum(sse_mul(s1, s2));
}

inline void sse_store(float* p, __m256 s) { _mm256_store_ps(p, s); }
inline void sse_store(double* p, __m256d s) { _mm256_store_pd(p, s); }

inline void data_prefetch(const void* p) {
  _mm_prefetch((const char*)p, _MM_HINT_NTA);
}
};

namespace ProgramCPU {
using namespace MYAVX;
#define SSE_ZERO SSE<Float>::zero()
#define SSE_T typename SSE<Float>::sse_type

/////////////////////////////
inline void ScaleJ8(float* jcx, float* jcy, const float* sj) {
  __m256 ps = _mm256_load_ps(sj);
  _mm256_store_ps(jcx, _mm256_mul_ps(_mm256_load_ps(jcx), ps));
  _mm256_store_ps(jcy, _mm256_mul_ps(_mm256_load_ps(jcy), ps));
}
inline void ScaleJ4(double* jcx, double* jcy, const double* sj) {
  __m256d ps = _mm256_load_pd(sj);
  _mm256_store_pd(jcx, _mm256_mul_pd(_mm256_load_pd(jcx), ps));
  _mm256_store_pd(jcy, _mm256_mul_pd(_mm256_load_pd(jcy), ps));
}
inline void ScaleJ8(double* jcx, double* jcy, const double* sj) {
  ScaleJ4(jcx, jcy, sj);
  ScaleJ4(jcx + 4, jcy + 4, sj + 4);
}
inline float DotProduct8(const float* v1, const float* v2) {
  return sse_dot(_mm256_load_ps(v1), _mm256_load_ps(v2));
}
inline double DotProduct8(const double* v1, const double* v2) {
  __m256d ds = _mm256_add_pd(
      _mm256_mul_pd(_mm256_load_pd(v1), _mm256_load_pd(v2)),
      _mm256_mul_pd(_mm256_load_pd(v1 + 4), _mm256_load_pd(v2 + 4)));
  return sse_sum(ds);
}

inline void ComputeTwoJX(const float* jc, const float* jp, const float* xc,
                         const float* xp, float* jx) {
  __m256 xcm = _mm256_load_ps(xc), jc1 = _mm256_load_ps(jc),
         jc2 = _mm256_load_ps(jc + 8);
  jx[0] = sse_dot(jc1, xcm) + (jp[0] * xp[0] + jp[1] * xp[1] + jp[2] * xp[2]);
  jx[1] = sse_dot(jc2, xcm) +
          (jp[POINT_ALIGN] * xp[0] + jp[POINT_ALIGN + 1] * xp[1] +
           jp[POINT_ALIGN + 2] * xp[2]);
}

inline void ComputeTwoJX(const double* jc, const double* jp, const double* xc,
                         const double* xp, double* jx) {
  __m256d xc1 = _mm256_load_pd(xc), xc2 = _mm256_load_pd(xc + 4);
  __m256d jc1 = _mm256_load_pd(jc), jc2 = _mm256_load_pd(jc + 4);
  __m256d jc3 = _mm256_load_pd(jc + 8), jc4 = _mm256_load_pd(jc + 12);
  __m256d ds1 = _mm256_add_pd(_mm256_mul_pd(jc1, xc1), _mm256_mul_pd(jc2, xc2));
  __m256d ds2 = _mm256_add_pd(_mm256_mul_pd(jc3, xc1), _mm256_mul_pd(jc4, xc2));
  jx[0] = sse_sum(ds1) + (jp[0] * xp[0] + jp[1] * xp[1] + jp[2] * xp[2]);
  jx[1] =
      sse_sum(ds2) + (jp[POINT_ALIGN] * xp[0] + jp[POINT_ALIGN + 1] * xp[1] +
                      jp[POINT_ALIGN + 2] * xp[2]);
}

// v += ax
inline void AddScaledVec8(float a, const float* x, float* v) {
  __m256 aa = sse_load1(&a);
  _mm256_store_ps(v, _mm256_add_ps(_mm256_mul_ps(_mm256_load_ps(x), aa),
                                   _mm256_load_ps(v)));
  //_mm256_store_ps(v, _mm256_fmadd_ps(_mm256_load_ps(x), aa,
  //_mm256_load_ps(v)));
}
// v += ax
inline void AddScaledVec8(double a, const double* x, double* v) {
  __m256d aa = sse_load1(&a);
  _mm256_store_pd(v, _mm256_add_pd(_mm256_mul_pd(_mm256_load_pd(x), aa),
                                   _mm256_load_pd(v)));
  _mm256_store_pd(v + 4, _mm256_add_pd(_mm256_mul_pd(_mm256_load_pd(x + 4), aa),
                                       _mm256_load_pd(v + 4)));
}

inline void AddBlockJtJ(const float* jc, float* block, int vn) {
  __m256 j = _mm256_load_ps(jc);
  for (int i = 0; i < vn; ++i, ++jc, block += 8) {
    __m256 a = sse_load1(jc);
    _mm256_store_ps(block,
                    _mm256_add_ps(_mm256_mul_ps(a, j), _mm256_load_ps(block)));
  }
}

inline void AddBlockJtJ(const double* jc, double* block, int vn) {
  __m256d j1 = _mm256_load_pd(jc);
  __m256d j2 = _mm256_load_pd(jc + 4);
  for (int i = 0; i < vn; ++i, ++jc, block += 8) {
    __m256d a = sse_load1(jc);
    _mm256_store_pd(block + 0, _mm256_add_pd(_mm256_mul_pd(a, j1),
                                             _mm256_load_pd(block + 0)));
    _mm256_store_pd(block + 4, _mm256_add_pd(_mm256_mul_pd(a, j2),
                                             _mm256_load_pd(block + 4)));
  }
}
};

#endif

#ifdef CPUPBA_USE_NEON
#define CPUPBA_USE_SIMD
#define SIMD_NO_SQRT
#define SIMD_NO_DOUBLE
namespace MYNEON {
template <class Float>
class SSE {};
template <>
class SSE<float> {
 public:
  typedef float32x4_t sse_type;
};

////////////////////////////////////////////
template <class Float>
inline size_t sse_step() {
  return 16 / sizeof(Float);
};
inline float32x4_t sse_load1(const float* p) { return vld1q_dup_f32(p); }
inline float32x4_t sse_load(const float* p) { return vld1q_f32(p); }
inline float32x4_t sse_loadzero() {
  float z = 0;
  return sse_load1(&z);
}
inline float32x4_t sse_add(float32x4_t s1, float32x4_t s2) {
  return vaddq_f32(s1, s2);
}
inline float32x4_t sse_sub(float32x4_t s1, float32x4_t s2) {
  return vsubq_f32(s1, s2);
}
inline float32x4_t sse_mul(float32x4_t s1, float32x4_t s2) {
  return vmulq_f32(s1, s2);
}
// inline float32x4_t sse_sqrt(float32x4_t s)                {return
// _mm_sqrt_ps(s); }
inline float sse_sum(float32x4_t s) {
  float* f = (float*)(&s);
  return (f[0] + f[2]) + (f[1] + f[3]);
}
inline void sse_store(float* p, float32x4_t s) { vst1q_f32(p, s); }
inline void data_prefetch(const void* p) {}
};
namespace ProgramCPU {
using namespace MYNEON;
#define SSE_ZERO sse_loadzero()
#define SSE_T typename SSE<Float>::sse_type
/////////////////////////////
inline void ScaleJ4(float* jcx, float* jcy, const float* sj) {
  float32x4_t ps = sse_load(sj);
  sse_store(jcx, sse_mul(sse_load(jcx), ps));
  sse_store(jcy, sse_mul(sse_load(jcy), ps));
}
inline void ScaleJ8(float* jcx, float* jcy, const float* sj) {
  ScaleJ4(jcx, jcy, sj);
  ScaleJ4(jcx + 4, jcy + 4, sj + 4);
}

inline float DotProduct8(const float* v1, const float* v2) {
  float32x4_t ds = sse_add(sse_mul(sse_load(v1), sse_load(v2)),
                           sse_mul(sse_load(v1 + 4), sse_load(v2 + 4)));
  return sse_sum(ds);
}

inline void ComputeTwoJX(const float* jc, const float* jp, const float* xc,
                         const float* xp, float* jx) {
#ifdef POINT_DATA_ALIGN4
  float32x4_t xc1 = sse_load(xc), xc2 = sse_load(xc + 4), mxp = sse_load(xp);
  float32x4_t ds1 =
      sse_add(sse_mul(sse_load(jc), xc1), sse_mul(sse_load(jc + 4), xc2));
  float32x4_t dx1 = sse_add(ds1, sse_mul(sse_load(jp), mxp));
  jx[0] = sse_sum(dx1);
  float32x4_t ds2 =
      sse_add(sse_mul(sse_load(jc + 8), xc1), sse_mul(sse_load(jc + 12), xc2));
  float32x4_t dx2 = sse_add(ds2, sse_mul(sse_load(jp + 4), mxp));
  jx[1] = sse_sum(dx2);
#else
  float32x4_t xc1 = sse_load(xc), xc2 = sse_load(xc + 4);
  float32x4_t jc1 = sse_load(jc), jc2 = sse_load(jc + 4);
  float32x4_t jc3 = sse_load(jc + 8), jc4 = sse_load(jc + 12);
  float32x4_t ds1 = sse_add(sse_mul(jc1, xc1), sse_mul(jc2, xc2));
  float32x4_t ds2 = sse_add(sse_mul(jc3, xc1), sse_mul(jc4, xc2));
  jx[0] = sse_sum(ds1) + (jp[0] * xp[0] + jp[1] * xp[1] + jp[2] * xp[2]);
  jx[1] =
      sse_sum(ds2) + (jp[POINT_ALIGN] * xp[0] + jp[POINT_ALIGN + 1] * xp[1] +
                      jp[POINT_ALIGN + 2] * xp[2]);
/*jx[0] = (sse_dot(jc1, xc1) + sse_dot(jc2, xc2)) + (jp[0] * xp[0] + jp[1] *
xp[1] + jp[2] * xp[2]);
jx[1] = (sse_dot(jc3, xc1) + sse_dot(jc4, xc2)) + (jp[POINT_ALIGN] * xp[0] +
jp[POINT_ALIGN+1] * xp[1] + jp[POINT_ALIGN+2] * xp[2]);*/
#endif
}

// v += ax
inline void AddScaledVec8(float a, const float* x, float* v) {
  float32x4_t aa = sse_load1(&a);
  sse_store(v, sse_add(sse_mul(sse_load(x), aa), sse_load(v)));
  sse_store(v + 4, sse_add(sse_mul(sse_load(x + 4), aa), sse_load(v + 4)));
}

inline void AddBlockJtJ(const float* jc, float* block, int vn) {
  float32x4_t j1 = sse_load(jc);
  float32x4_t j2 = sse_load(jc + 4);
  for (int i = 0; i < vn; ++i, ++jc, block += 8) {
    float32x4_t a = sse_load1(jc);
    sse_store(block + 0, sse_add(sse_mul(a, j1), sse_load(block + 0)));
    sse_store(block + 4, sse_add(sse_mul(a, j2), sse_load(block + 4)));
  }
}
};
#endif

namespace ProgramCPU {
int __num_cpu_cores = 0;
template <class Float>
double ComputeVectorNorm(const avec<Float>& vec, int mt = 0);

#if defined(CPUPBA_USE_SIMD)
template <class Float>
void ComputeSQRT(avec<Float>& vec) {
#ifndef SIMD_NO_SQRT
  const size_t step = sse_step<Float>();
  Float *p = &vec[0], *pe = p + vec.size(), *pex = pe - step;
  for (; p <= pex; p += step) sse_store(p, sse_sqrt(sse_load(p)));
  for (; p < pe; ++p) p[0] = sqrt(p[0]);
#else
  for (Float* it = vec.begin(); it < vec.end(); ++it) *it = sqrt(*it);
#endif
}

template <class Float>
void ComputeRSQRT(avec<Float>& vec) {
  Float *p = &vec[0], *pe = p + vec.size();
  for (; p < pe; ++p) p[0] = (p[0] == 0 ? 0 : Float(1.0) / p[0]);
  ComputeSQRT(vec);
}

template <class Float>
void SetVectorZero(Float* p, Float* pe) {
  SSE_T sse = SSE_ZERO;
  const size_t step = sse_step<Float>();
  Float* pex = pe - step;
  for (; p <= pex; p += step) sse_store(p, sse);
  for (; p < pe; ++p) *p = 0;
}

template <class Float>
void SetVectorZero(avec<Float>& vec) {
  Float *p = &vec[0], *pe = p + vec.size();
  SetVectorZero(p, pe);
}

// function not used
template <class Float>
inline void MemoryCopyA(const Float* p, const Float* pe, Float* d) {
  const size_t step = sse_step<Float>();
  const Float* pex = pe - step;
  for (; p <= pex; p += step, d += step) sse_store(d, sse_load(p));
  // while(p < pe) *d++ = *p++;
}

template <class Float>
void ComputeVectorNorm(const Float* p, const Float* pe, double* psum) {
  SSE_T sse = SSE_ZERO;
  const size_t step = sse_step<Float>();
  const Float* pex = pe - step;
  for (; p <= pex; p += step) {
    SSE_T ps = sse_load(p);
    sse = sse_add(sse, sse_mul(ps, ps));
  }
  double sum = sse_sum(sse);
  for (; p < pe; ++p) sum += p[0] * p[0];
  *psum = sum;
}

template <class Float>
double ComputeVectorNormW(const avec<Float>& vec, const avec<Float>& weight) {
  if (weight.begin() != NULL) {
    SSE_T sse = SSE_ZERO;
    const size_t step = sse_step<Float>();
    const Float *p = vec, *pe = p + vec.size(), *pex = pe - step;
    const Float* w = weight;
    for (; p <= pex; p += step, w += step) {
      SSE_T pw = sse_load(w), ps = sse_load(p);
      sse = sse_add(sse, sse_mul(sse_mul(ps, pw), ps));
    }
    double sum = sse_sum(sse);
    for (; p < pe; ++p, ++w) sum += p[0] * w[0] * p[0];
    return sum;
  } else {
    return ComputeVectorNorm<Float>(vec, 0);
  }
}

template <class Float>
double ComputeVectorDot(const avec<Float>& vec1, const avec<Float>& vec2) {
  SSE_T sse = SSE_ZERO;
  const size_t step = sse_step<Float>();
  const Float *p1 = vec1, *pe = p1 + vec1.size(), *pex = pe - step;
  const Float* p2 = vec2;
  for (; p1 <= pex; p1 += step, p2 += step) {
    SSE_T ps1 = sse_load(p1), ps2 = sse_load(p2);
    sse = sse_add(sse, sse_mul(ps1, ps2));
  }
  double sum = sse_sum(sse);
  for (; p1 < pe; ++p1, ++p2) sum += p1[0] * p2[0];
  return sum;
}

template <class Float>
void ComputeVXY(const avec<Float>& vec1, const avec<Float>& vec2,
                avec<Float>& result, size_t part = 0, size_t skip = 0) {
  const size_t step = sse_step<Float>();
  const Float *p1 = vec1 + skip, *pe = p1 + (part ? part : vec1.size()),
              *pex = pe - step;
  const Float* p2 = vec2 + skip;
  Float* p3 = result + skip;
  for (; p1 <= pex; p1 += step, p2 += step, p3 += step) {
    SSE_T ps1 = sse_load(p1), ps2 = sse_load(p2);
    sse_store(p3, sse_mul(ps1, ps2));
  }
  for (; p1 < pe; ++p1, ++p2, ++p3) *p3 = p1[0] * p2[0];
}

template <class Float>
void ComputeSAXPY(Float a, const Float* p1, const Float* p2, Float* p3,
                  Float* pe) {
  const size_t step = sse_step<Float>();
  SSE_T aa = sse_load1(&a);
  Float* pex = pe - step;
  if (a == 1.0f) {
    for (; p3 <= pex; p1 += step, p2 += step, p3 += step) {
      SSE_T ps1 = sse_load(p1), ps2 = sse_load(p2);
      sse_store(p3, sse_add(ps2, ps1));
    }
  } else if (a == -1.0f) {
    for (; p3 <= pex; p1 += step, p2 += step, p3 += step) {
      SSE_T ps1 = sse_load(p1), ps2 = sse_load(p2);
      sse_store(p3, sse_sub(ps2, ps1));
    }
  } else {
    for (; p3 <= pex; p1 += step, p2 += step, p3 += step) {
      SSE_T ps1 = sse_load(p1), ps2 = sse_load(p2);
      sse_store(p3, sse_add(ps2, sse_mul(ps1, aa)));
    }
  }
  for (; p3 < pe; ++p1, ++p2, ++p3) p3[0] = a * p1[0] + p2[0];
}

template <class Float>
void ComputeSAX(Float a, const avec<Float>& vec1, avec<Float>& result) {
  const size_t step = sse_step<Float>();
  SSE_T aa = sse_load1(&a);
  const Float *p1 = vec1, *pe = p1 + vec1.size(), *pex = pe - step;
  Float* p3 = result;
  for (; p1 <= pex; p1 += step, p3 += step) {
    sse_store(p3, sse_mul(sse_load(p1), aa));
  }
  for (; p1 < pe; ++p1, ++p3) p3[0] = a * p1[0];
}

template <class Float>
inline void ComputeSXYPZ(Float a, const Float* p1, const Float* p2,
                         const Float* p3, Float* p4, Float* pe) {
  const size_t step = sse_step<Float>();
  SSE_T aa = sse_load1(&a);
  Float* pex = pe - step;
  for (; p4 <= pex; p1 += step, p2 += step, p3 += step, p4 += step) {
    SSE_T ps1 = sse_load(p1), ps2 = sse_load(p2), ps3 = sse_load(p3);
    sse_store(p4, sse_add(ps3, sse_mul(sse_mul(ps1, aa), ps2)));
  }
  for (; p4 < pe; ++p1, ++p2, ++p3, ++p4) p4[0] = a * p1[0] * p2[0] + p3[0];
}

#else
template <class Float>
void ComputeSQRT(avec<Float>& vec) {
  Float* it = vec.begin();
  for (; it < vec.end(); ++it) {
    *it = sqrt(*it);
  }
}
template <class Float>
void ComputeRSQRT(avec<Float>& vec) {
  Float* it = vec.begin();
  for (; it < vec.end(); ++it) {
    *it = (*it == 0 ? 0 : Float(1.0) / sqrt(*it));
  }
}
template <class Float>
inline void SetVectorZero(Float* p, Float* pe) {
  std::fill(p, pe, 0);
}
template <class Float>
inline void SetVectorZero(avec<Float>& vec) {
  std::fill(vec.begin(), vec.end(), 0);
}

template <class Float>
inline void MemoryCopyA(const Float* p, const Float* pe, Float* d) {
  while (p < pe) *d++ = *p++;
}

template <class Float>
double ComputeVectorNormW(const avec<Float>& vec, const avec<Float>& weight) {
  double sum = 0;
  const Float *it1 = vec.begin(), *it2 = weight.begin();
  for (; it1 < vec.end(); ++it1, ++it2) {
    sum += (*it1) * (*it2) * (*it1);
  }
  return sum;
}

template <class Float>
double ComputeVectorDot(const avec<Float>& vec1, const avec<Float>& vec2) {
  double sum = 0;
  const Float *it1 = vec1.begin(), *it2 = vec2.begin();
  for (; it1 < vec1.end(); ++it1, ++it2) {
    sum += (*it1) * (*it2);
  }
  return sum;
}
template <class Float>
void ComputeVectorNorm(const Float* p, const Float* pe, double* psum) {
  double sum = 0;
  for (; p < pe; ++p) sum += (*p) * (*p);
  *psum = sum;
}
template <class Float>
inline void ComputeVXY(const avec<Float>& vec1, const avec<Float>& vec2,
                       avec<Float>& result, size_t part = 0, size_t skip = 0) {
  const Float *it1 = vec1.begin() + skip, *it2 = vec2.begin() + skip;
  const Float* ite = part ? (it1 + part) : vec1.end();
  Float* it3 = result.begin() + skip;
  for (; it1 < ite; ++it1, ++it2, ++it3) {
    (*it3) = (*it1) * (*it2);
  }
}
template <class Float>
void ScaleJ8(Float* jcx, Float* jcy, const Float* sj) {
  for (int i = 0; i < 8; ++i) {
    jcx[i] *= sj[i];
    jcy[i] *= sj[i];
  }
}

template <class Float>
inline void AddScaledVec8(Float a, const Float* x, Float* v) {
  for (int i = 0; i < 8; ++i) v[i] += (a * x[i]);
}

template <class Float>
void ComputeSAX(Float a, const avec<Float>& vec1, avec<Float>& result) {
  const Float* it1 = vec1.begin();
  Float* it3 = result.begin();
  for (; it1 < vec1.end(); ++it1, ++it3) {
    (*it3) = (a * (*it1));
  }
}

template <class Float>
inline void ComputeSXYPZ(Float a, const Float* p1, const Float* p2,
                         const Float* p3, Float* p4, Float* pe) {
  for (; p4 < pe; ++p1, ++p2, ++p3, ++p4) *p4 = (a * (*p1) * (*p2) + (*p3));
}

template <class Float>
void ComputeSAXPY(Float a, const Float* it1, const Float* it2, Float* it3,
                  Float* ite) {
  if (a == (Float)1.0) {
    for (; it3 < ite; ++it1, ++it2, ++it3) {
      (*it3) = ((*it1) + (*it2));
    }
  } else {
    for (; it3 < ite; ++it1, ++it2, ++it3) {
      (*it3) = (a * (*it1) + (*it2));
    }
  }
}
template <class Float>
void AddBlockJtJ(const Float* jc, Float* block, int vn) {
  for (int i = 0; i < vn; ++i) {
    Float *row = block + i * 8, a = jc[i];
    for (int j = 0; j < vn; ++j) row[j] += a * jc[j];
  }
}
#endif

#ifdef _WIN32
#define DEFINE_THREAD_DATA(X) \
  template <class Float>      \
  struct X##_STRUCT {
#define DECLEAR_THREAD_DATA(X, ...)        \
  X##_STRUCT<Float> tdata = {__VA_ARGS__}; \
  X##_STRUCT<Float>* newdata = new X##_STRUCT<Float>(tdata)
#define BEGIN_THREAD_PROC(X) \
  }                          \
  ;                          \
  template <class Float>     \
  DWORD X##_PROC(X##_STRUCT<Float>* q) {
#define END_THREAD_RPOC(X) \
  delete q;                \
  return 0;                \
  }

#if defined(WINAPI_FAMILY) && WINAPI_FAMILY == WINAPI_FAMILY_APP
#define MYTHREAD std::thread
#define RUN_THREAD(X, t, ...)          \
  DECLEAR_THREAD_DATA(X, __VA_ARGS__); \
  t = std::thread(X##_PROC<Float>, newdata)
#define WAIT_THREAD(tv, n)                               \
  {                                                      \
    for (size_t i = 0; i < size_t(n); ++i) tv[i].join(); \
  }
#else
#define MYTHREAD HANDLE
#define RUN_THREAD(X, t, ...)                                                 \
  DECLEAR_THREAD_DATA(X, __VA_ARGS__);                                        \
  t = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)X##_PROC<Float>, newdata, \
                   0, 0)
#define WAIT_THREAD(tv, n)                                     \
  {                                                            \
    WaitForMultipleObjects((DWORD)n, tv, TRUE, INFINITE);      \
    for (size_t i = 0; i < size_t(n); ++i) CloseHandle(tv[i]); \
  }
#endif
#else
#define DEFINE_THREAD_DATA(X) \
  template <class Float>      \
  struct X##_STRUCT {         \
    int tid;
#define DECLEAR_THREAD_DATA(X, ...)           \
  X##_STRUCT<Float> tdata = {i, __VA_ARGS__}; \
  X##_STRUCT<Float>* newdata = new X##_STRUCT<Float>(tdata)
#define BEGIN_THREAD_PROC(X) \
  }                          \
  ;                          \
  template <class Float>     \
  void* X##_PROC(X##_STRUCT<Float>* q) {
//                                 cpu_set_t mask;        CPU_ZERO( &mask );
//                                 CPU_SET( q->tid, &mask );
//                                 if( sched_setaffinity(0, sizeof(mask), &mask
//                                 ) == -1 )
//                                     std::cout <<"WARNING: Could not set CPU
//                                     Affinity, continuing...\n";
#define END_THREAD_RPOC(X)                                \
  delete q;                                               \
  return 0;                                               \
  }                                                       \
  template <class Float>                                  \
  struct X##_FUNCTOR {                                    \
    typedef void* (*func_type)(X##_STRUCT<Float>*);       \
    static func_type get() { return &(X##_PROC<Float>); } \
  };
#define MYTHREAD pthread_t

#define RUN_THREAD(X, t, ...)          \
  DECLEAR_THREAD_DATA(X, __VA_ARGS__); \
  pthread_create(&t, NULL, (void* (*)(void*))X##_FUNCTOR<Float>::get(), newdata)
#define WAIT_THREAD(tv, n)                                            \
  {                                                                   \
    for (size_t i = 0; i < size_t(n); ++i) pthread_join(tv[i], NULL); \
  }
#endif
template <class Float>
inline void MemoryCopyB(const Float* p, const Float* pe, Float* d) {
  while (p < pe) *d++ = *p++;
}

template <class Float>
inline Float DotProduct8(const Float* v1, const Float* v2) {
  return v1[0] * v2[0] + v1[1] * v2[1] + v1[2] * v2[2] + v1[3] * v2[3] +
         v1[4] * v2[4] + v1[5] * v2[5] + v1[6] * v2[6] + v1[7] * v2[7];
}
template <class Float>
inline void ComputeTwoJX(const Float* jc, const Float* jp, const Float* xc,
                         const Float* xp, Float* jx) {
  jx[0] = DotProduct8(jc, xc) + (jp[0] * xp[0] + jp[1] * xp[1] + jp[2] * xp[2]);
  jx[1] =
      DotProduct8(jc + 8, xc) + (jp[3] * xp[0] + jp[4] * xp[1] + jp[5] * xp[2]);
}
template <class Float>
Float ComputeVectorMax(const avec<Float>& vec) {
  Float v = 0;
  const Float* it = vec.begin();
  for (; it < vec.end(); ++it) {
    Float vi = (Float)fabs(*it);
    v = std::max(v, vi);
  }
  return v;
}

template <class Float>
void ComputeSXYPZ(Float a, const avec<Float>& vec1, const avec<Float>& vec2,
                  const avec<Float>& vec3, avec<Float>& result) {
  if (vec1.begin() != NULL) {
    const Float *p1 = &vec1[0], *p2 = &vec2[0], *p3 = &vec3[0];
    Float *p4 = &result[0], *pe = p4 + result.size();
    ComputeSXYPZ(a, p1, p2, p3, p4, pe);

  } else {
    // ComputeSAXPY<Float>(a, vec2, vec3, result, 0);
    ComputeSAXPY<Float>(a, vec2.begin(), vec3.begin(), result.begin(),
                        result.end());
  }
}

DEFINE_THREAD_DATA(ComputeSAXPY)
Float a;
const Float *p1, *p2;
Float *p3, *pe;
BEGIN_THREAD_PROC(ComputeSAXPY)
ComputeSAXPY(q->a, q->p1, q->p2, q->p3, q->pe);
END_THREAD_RPOC(ComputeSAXPY)

template <class Float>
void ComputeSAXPY(Float a, const avec<Float>& vec1, const avec<Float>& vec2,
                  avec<Float>& result, int mt = 0) {
  const bool auto_multi_thread = true;
  if (auto_multi_thread && mt == 0) {
    mt = AUTO_MT_NUM(result.size() * 2);
  }
  if (mt > 1 && result.size() >= mt * 4) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    const Float *p1 = vec1.begin(), *p2 = vec2.begin();
    Float* p3 = result.begin();
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = (result.size() * i / thread_num + FLOAT_ALIGN - 1) /
                     FLOAT_ALIGN * FLOAT_ALIGN;
      size_t last_ = (result.size() * (i + 1) / thread_num + FLOAT_ALIGN - 1) /
                     FLOAT_ALIGN * FLOAT_ALIGN;
      size_t last = std::min(last_, result.size());
      RUN_THREAD(ComputeSAXPY, threads[i], a, p1 + first, p2 + first,
                 p3 + first, p3 + last);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    ComputeSAXPY(a, vec1.begin(), vec2.begin(), result.begin(), result.end());
  }
}

DEFINE_THREAD_DATA(ComputeVectorNorm)
const Float *p, *pe;
double* sum;
BEGIN_THREAD_PROC(ComputeVectorNorm)
ComputeVectorNorm(q->p, q->pe, q->sum);
END_THREAD_RPOC(ComputeVectorNorm)

template <class Float>
double ComputeVectorNorm(const avec<Float>& vec, int mt) {
  const bool auto_multi_thread = true;
  if (auto_multi_thread && mt == 0) {
    mt = AUTO_MT_NUM(vec.size());
  }
  if (mt > 1 && vec.size() >= mt * 4) {
    MYTHREAD threads[THREAD_NUM_MAX];
    double sumv[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    const Float* p = vec;
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = (vec.size() * i / thread_num + FLOAT_ALIGN - 1) /
                     FLOAT_ALIGN * FLOAT_ALIGN;
      size_t last_ = (vec.size() * (i + 1) / thread_num + FLOAT_ALIGN - 1) /
                     FLOAT_ALIGN * FLOAT_ALIGN;
      size_t last = std::min(last_, vec.size());
      RUN_THREAD(ComputeVectorNorm, threads[i], p + first, p + last, sumv + i);
    }
    WAIT_THREAD(threads, thread_num);
    double sum = 0;
    for (size_t i = 0; i < thread_num; ++i) sum += sumv[i];
    return sum;
  } else {
    double sum;
    ComputeVectorNorm(vec.begin(), vec.end(), &sum);
    return sum;
  }
}

template <class Float>
void GetRodriguesRotation(const Float m[3][3], Float r[3]) {
  // http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToAngle/index.htm
  double a = (m[0][0] + m[1][1] + m[2][2] - 1.0) / 2.0;
  const double epsilon = 0.01;
  if (fabs(m[0][1] - m[1][0]) < epsilon && fabs(m[1][2] - m[2][1]) < epsilon &&
      fabs(m[0][2] - m[2][0]) < epsilon) {
    if (fabs(m[0][1] + m[1][0]) < 0.1 && fabs(m[1][2] + m[2][1]) < 0.1 &&
        fabs(m[0][2] + m[2][0]) < 0.1 && a > 0.9) {
      r[0] = 0;
      r[1] = 0;
      r[2] = 0;
    } else {
      const Float ha = Float(sqrt(0.5) * 3.14159265358979323846);
      double xx = (m[0][0] + 1.0) / 2.0;
      double yy = (m[1][1] + 1.0) / 2.0;
      double zz = (m[2][2] + 1.0) / 2.0;
      double xy = (m[0][1] + m[1][0]) / 4.0;
      double xz = (m[0][2] + m[2][0]) / 4.0;
      double yz = (m[1][2] + m[2][1]) / 4.0;

      if ((xx > yy) && (xx > zz)) {
        if (xx < epsilon) {
          r[0] = 0;
          r[1] = r[2] = ha;
        } else {
          double t = sqrt(xx);
          r[0] = Float(t * 3.14159265358979323846);
          r[1] = Float(xy / t * 3.14159265358979323846);
          r[2] = Float(xz / t * 3.14159265358979323846);
        }
      } else if (yy > zz) {
        if (yy < epsilon) {
          r[0] = r[2] = ha;
          r[1] = 0;
        } else {
          double t = sqrt(yy);
          r[0] = Float(xy / t * 3.14159265358979323846);
          r[1] = Float(t * 3.14159265358979323846);
          r[2] = Float(yz / t * 3.14159265358979323846);
        }
      } else {
        if (zz < epsilon) {
          r[0] = r[1] = ha;
          r[2] = 0;
        } else {
          double t = sqrt(zz);
          r[0] = Float(xz / t * 3.14159265358979323846);
          r[1] = Float(yz / t * 3.14159265358979323846);
          r[2] = Float(t * 3.14159265358979323846);
        }
      }
    }
  } else {
    a = acos(a);
    double b = 0.5 * a / sin(a);
    r[0] = Float(b * (m[2][1] - m[1][2]));
    r[1] = Float(b * (m[0][2] - m[2][0]));
    r[2] = Float(b * (m[1][0] - m[0][1]));
  }
}
template <class Float>
void UncompressRodriguesRotation(const Float r[3], Float m[]) {
  double a = sqrt(r[0] * r[0] + r[1] * r[1] + r[2] * r[2]);
  double ct = a == 0.0 ? 0.5f : (1.0f - cos(a)) / a / a;
  double st = a == 0.0 ? 1 : sin(a) / a;
  m[0] = Float(1.0 - (r[1] * r[1] + r[2] * r[2]) * ct);
  m[1] = Float(r[0] * r[1] * ct - r[2] * st);
  m[2] = Float(r[2] * r[0] * ct + r[1] * st);
  m[3] = Float(r[0] * r[1] * ct + r[2] * st);
  m[4] = Float(1.0f - (r[2] * r[2] + r[0] * r[0]) * ct);
  m[5] = Float(r[1] * r[2] * ct - r[0] * st);
  m[6] = Float(r[2] * r[0] * ct - r[1] * st);
  m[7] = Float(r[1] * r[2] * ct + r[0] * st);
  m[8] = Float(1.0 - (r[0] * r[0] + r[1] * r[1]) * ct);
}
template <class Float>
void UpdateCamera(int ncam, const avec<Float>& camera, const avec<Float>& delta,
                  avec<Float>& new_camera) {
  const Float *c = &camera[0], *d = &delta[0];
  Float *nc = &new_camera[0], m[9];
  // f[1], t[3], r[3][3], d[1]
  for (int i = 0; i < ncam; ++i, c += 16, d += 8, nc += 16) {
    nc[0] = max(c[0] + d[0], ((Float)1e-10));
    nc[1] = c[1] + d[1];
    nc[2] = c[2] + d[2];
    nc[3] = c[3] + d[3];
    nc[13] = c[13] + d[7];

    ////////////////////////////////////////////////////
    UncompressRodriguesRotation(d + 4, m);
    nc[4] = m[0] * c[4 + 0] + m[1] * c[4 + 3] + m[2] * c[4 + 6];
    nc[5] = m[0] * c[4 + 1] + m[1] * c[4 + 4] + m[2] * c[4 + 7];
    nc[6] = m[0] * c[4 + 2] + m[1] * c[4 + 5] + m[2] * c[4 + 8];
    nc[7] = m[3] * c[4 + 0] + m[4] * c[4 + 3] + m[5] * c[4 + 6];
    nc[8] = m[3] * c[4 + 1] + m[4] * c[4 + 4] + m[5] * c[4 + 7];
    nc[9] = m[3] * c[4 + 2] + m[4] * c[4 + 5] + m[5] * c[4 + 8];
    nc[10] = m[6] * c[4 + 0] + m[7] * c[4 + 3] + m[8] * c[4 + 6];
    nc[11] = m[6] * c[4 + 1] + m[7] * c[4 + 4] + m[8] * c[4 + 7];
    nc[12] = m[6] * c[4 + 2] + m[7] * c[4 + 5] + m[8] * c[4 + 8];

    // Float temp[3];
    // GetRodriguesRotation((Float (*)[3])  (nc + 4), temp);
    // UncompressRodriguesRotation(temp, nc + 4);
    nc[14] = c[14];
    nc[15] = c[15];
  }
}

template <class Float>
void UpdateCameraPoint(int ncam, const avec<Float>& camera,
                       const avec<Float>& point, avec<Float>& delta,
                       avec<Float>& new_camera, avec<Float>& new_point,
                       int mode, int mt) {
  ////////////////////////////
  if (mode != 2) {
    UpdateCamera(ncam, camera, delta, new_camera);
  }
  /////////////////////////////
  if (mode != 1) {
    avec<Float> dp;
    dp.set(delta.begin() + 8 * ncam, point.size());
    ComputeSAXPY((Float)1.0, dp, point, new_point, mt);
  }
}

template <class Float>
void ComputeProjection(size_t nproj, const Float* camera, const Float* point,
                       const Float* ms, const int* jmap, Float* pj, int radial,
                       int mt);

DEFINE_THREAD_DATA(ComputeProjection)
size_t nproj;
const Float *camera, *point, *ms;
const int* jmap;
Float* pj;
int radial_distortion;
BEGIN_THREAD_PROC(ComputeProjection)
ComputeProjection(q->nproj, q->camera, q->point, q->ms, q->jmap, q->pj,
                  q->radial_distortion, 0);
END_THREAD_RPOC(ComputeProjection)

template <class Float>
void ComputeProjection(size_t nproj, const Float* camera, const Float* point,
                       const Float* ms, const int* jmap, Float* pj, int radial,
                       int mt) {
  if (mt > 1 && nproj >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = nproj * i / thread_num;
      size_t last_ = nproj * (i + 1) / thread_num;
      size_t last = std::min(last_, nproj);
      RUN_THREAD(ComputeProjection, threads[i], last - first, camera, point,
                 ms + 2 * first, jmap + 2 * first, pj + 2 * first, radial);
    }
    WAIT_THREAD(threads, thread_num);

  } else {
    for (size_t i = 0; i < nproj; ++i, jmap += 2, ms += 2, pj += 2) {
      const Float* c = camera + jmap[0] * 16;
      const Float* m = point + jmap[1] * POINT_ALIGN;
      /////////////////////////////////////////////////////
      Float p0 = c[4] * m[0] + c[5] * m[1] + c[6] * m[2] + c[1];
      Float p1 = c[7] * m[0] + c[8] * m[1] + c[9] * m[2] + c[2];
      Float p2 = c[10] * m[0] + c[11] * m[1] + c[12] * m[2] + c[3];

      if (radial == 1) {
        Float rr = Float(1.0) + c[13] * (p0 * p0 + p1 * p1) / (p2 * p2);
        Float f_p2 = c[0] * rr / p2;
        pj[0] = ms[0] - p0 * f_p2;
        pj[1] = ms[1] - p1 * f_p2;
      } else if (radial == -1) {
        Float f_p2 = c[0] / p2;
        Float rd = Float(1.0) + c[13] * (ms[0] * ms[0] + ms[1] * ms[1]);
        pj[0] = ms[0] * rd - p0 * f_p2;
        pj[1] = ms[1] * rd - p1 * f_p2;
      } else {
        pj[0] = ms[0] - p0 * c[0] / p2;
        pj[1] = ms[1] - p1 * c[0] / p2;
      }
    }
  }
}

template <class Float>
void ComputeProjectionX(size_t nproj, const Float* camera, const Float* point,
                        const Float* ms, const int* jmap, Float* pj, int radial,
                        int mt);

DEFINE_THREAD_DATA(ComputeProjectionX)
size_t nproj;
const Float *camera, *point, *ms;
const int* jmap;
Float* pj;
int radial_distortion;
BEGIN_THREAD_PROC(ComputeProjectionX)
ComputeProjectionX(q->nproj, q->camera, q->point, q->ms, q->jmap, q->pj,
                   q->radial_distortion, 0);
END_THREAD_RPOC(ComputeProjectionX)

template <class Float>
void ComputeProjectionX(size_t nproj, const Float* camera, const Float* point,
                        const Float* ms, const int* jmap, Float* pj, int radial,
                        int mt) {
  if (mt > 1 && nproj >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = nproj * i / thread_num;
      size_t last_ = nproj * (i + 1) / thread_num;
      size_t last = std::min(last_, nproj);
      RUN_THREAD(ComputeProjectionX, threads[i], last - first, camera, point,
                 ms + 2 * first, jmap + 2 * first, pj + 2 * first, radial);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    for (size_t i = 0; i < nproj; ++i, jmap += 2, ms += 2, pj += 2) {
      const Float* c = camera + jmap[0] * 16;
      const Float* m = point + jmap[1] * POINT_ALIGN;
      /////////////////////////////////////////////////////
      Float p0 = c[4] * m[0] + c[5] * m[1] + c[6] * m[2] + c[1];
      Float p1 = c[7] * m[0] + c[8] * m[1] + c[9] * m[2] + c[2];
      Float p2 = c[10] * m[0] + c[11] * m[1] + c[12] * m[2] + c[3];
      if (radial == 1) {
        Float rr = Float(1.0) + c[13] * (p0 * p0 + p1 * p1) / (p2 * p2);
        Float f_p2 = c[0] / p2;
        pj[0] = ms[0] / rr - p0 * f_p2;
        pj[1] = ms[1] / rr - p1 * f_p2;
      } else if (radial == -1) {
        Float rd = Float(1.0) + c[13] * (ms[0] * ms[0] + ms[1] * ms[1]);
        Float f_p2 = c[0] / p2 / rd;
        pj[0] = ms[0] - p0 * f_p2;
        pj[1] = ms[1] - p1 * f_p2;
      } else {
        pj[0] = ms[0] - p0 * c[0] / p2;
        pj[1] = ms[1] - p1 * c[0] / p2;
      }
    }
  }
}

template <class Float>
void ComputeProjectionQ(size_t nq, const Float* camera, const int* qmap,
                        const Float* wq, Float* pj) {
  for (size_t i = 0; i < nq; ++i, qmap += 2, pj += 2, wq += 2) {
    const Float* c1 = camera + qmap[0] * 16;
    const Float* c2 = camera + qmap[1] * 16;
    pj[0] = -(c1[0] - c2[0]) * wq[0];
    pj[1] = -(c1[13] - c2[13]) * wq[1];
  }
}

template <class Float>
void ComputeJQX(size_t nq, const Float* x, const int* qmap, const Float* wq,
                const Float* sj, Float* jx) {
  if (sj) {
    for (size_t i = 0; i < nq; ++i, qmap += 2, jx += 2, wq += 2) {
      int idx1 = qmap[0] * 8, idx2 = qmap[1] * 8;
      const Float* x1 = x + idx1;
      const Float* x2 = x + idx2;
      const Float* sj1 = sj + idx1;
      const Float* sj2 = sj + idx2;
      jx[0] = (x1[0] * sj1[0] - x2[0] * sj2[0]) * wq[0];
      jx[1] = (x1[7] * sj1[7] - x2[7] * sj2[7]) * wq[1];
    }
  } else {
    for (size_t i = 0; i < nq; ++i, qmap += 2, jx += 2, wq += 2) {
      const Float* x1 = x + qmap[0] * 8;
      const Float* x2 = x + qmap[1] * 8;
      jx[0] = (x1[0] - x2[0]) * wq[0];
      jx[1] = (x1[7] - x2[7]) * wq[1];
    }
  }
}

template <class Float>
void ComputeJQtEC(size_t ncam, const Float* pe, const int* qlist,
                  const Float* wq, const Float* sj, Float* v) {
  if (sj) {
    for (size_t i = 0; i < ncam; ++i, qlist += 2, wq += 2, v += 8, sj += 8) {
      int ip = qlist[0];
      if (ip == -1) continue;
      int in = qlist[1];
      const Float* e1 = pe + ip * 2;
      const Float* e2 = pe + in * 2;
      v[0] += wq[0] * sj[0] * (e1[0] - e2[0]);
      v[7] += wq[1] * sj[7] * (e1[1] - e2[1]);
    }
  } else {
    for (size_t i = 0; i < ncam; ++i, qlist += 2, wq += 2, v += 8) {
      int ip = qlist[0];
      if (ip == -1) continue;
      int in = qlist[1];
      const Float* e1 = pe + ip * 2;
      const Float* e2 = pe + in * 2;
      v[0] += wq[0] * (e1[0] - e2[0]);
      v[7] += wq[1] * (e1[1] - e2[1]);
    }
  }
}

template <class Float>
inline void JacobianOne(const Float* c, const Float* pt, const Float* ms,
                        Float* jxc, Float* jyc, Float* jxp, Float* jyp,
                        bool intrinsic_fixed, int radial_distortion) {
  const Float* r = c + 4;
  Float x0 = c[4] * pt[0] + c[5] * pt[1] + c[6] * pt[2];
  Float y0 = c[7] * pt[0] + c[8] * pt[1] + c[9] * pt[2];
  Float z0 = c[10] * pt[0] + c[11] * pt[1] + c[12] * pt[2];
  Float p2 = (z0 + c[3]);
  Float f_p2 = c[0] / p2;
  Float p0_p2 = (x0 + c[1]) / p2;
  Float p1_p2 = (y0 + c[2]) / p2;

  if (radial_distortion == 1) {
    Float rr1 = c[13] * p0_p2 * p0_p2;
    Float rr2 = c[13] * p1_p2 * p1_p2;
    Float f_p2_x = Float(f_p2 * (1.0 + 3.0 * rr1 + rr2));
    Float f_p2_y = Float(f_p2 * (1.0 + 3.0 * rr2 + rr1));
    if (jxc) {
#ifndef PBA_DISABLE_CONST_CAMERA
      if (c[15] != 0.0f) {
        jxc[0] = 0;
        jxc[1] = 0;
        jxc[2] = 0;
        jxc[3] = 0;
        jxc[4] = 0;
        jxc[5] = 0;
        jxc[6] = 0;
        jxc[7] = 0;
        jyc[0] = 0;
        jyc[1] = 0;
        jyc[2] = 0;
        jyc[3] = 0;
        jyc[4] = 0;
        jyc[5] = 0;
        jyc[6] = 0;
        jyc[7] = 0;
      } else
#endif
      {
        Float jfc = intrinsic_fixed ? 0 : Float(1.0 + rr1 + rr2);
        Float ft_x_pn =
            intrinsic_fixed ? 0 : c[0] * (p0_p2 * p0_p2 + p1_p2 * p1_p2);
        /////////////////////////////////////////////////////
        jxc[0] = p0_p2 * jfc;
        jxc[1] = f_p2_x;
        jxc[2] = 0;
        jxc[3] = -f_p2_x * p0_p2;
        jxc[4] = -f_p2_x * p0_p2 * y0;
        jxc[5] = f_p2_x * (z0 + x0 * p0_p2);
        jxc[6] = -f_p2_x * y0;
        jxc[7] = ft_x_pn * p0_p2;

        jyc[0] = p1_p2 * jfc;
        jyc[1] = 0;
        jyc[2] = f_p2_y;
        jyc[3] = -f_p2_y * p1_p2;
        jyc[4] = -f_p2_y * (z0 + y0 * p1_p2);
        jyc[5] = f_p2_y * x0 * p1_p2;
        jyc[6] = f_p2_y * x0;
        jyc[7] = ft_x_pn * p1_p2;
      }
    }

    ///////////////////////////////////
    if (jxp) {
      jxp[0] = f_p2_x * (r[0] - r[6] * p0_p2);
      jxp[1] = f_p2_x * (r[1] - r[7] * p0_p2);
      jxp[2] = f_p2_x * (r[2] - r[8] * p0_p2);
      jyp[0] = f_p2_y * (r[3] - r[6] * p1_p2);
      jyp[1] = f_p2_y * (r[4] - r[7] * p1_p2);
      jyp[2] = f_p2_y * (r[5] - r[8] * p1_p2);
#ifdef POINT_DATA_ALIGN4
      jxp[3] = jyp[3] = 0;
#endif
    }
  } else {
    if (jxc) {
#ifndef PBA_DISABLE_CONST_CAMERA
      if (c[15] != 0.0f) {
        jxc[0] = 0;
        jxc[1] = 0;
        jxc[2] = 0;
        jxc[3] = 0;
        jxc[4] = 0;
        jxc[5] = 0;
        jxc[6] = 0;
        jxc[7] = 0;
        jyc[0] = 0;
        jyc[1] = 0;
        jyc[2] = 0;
        jyc[3] = 0;
        jyc[4] = 0;
        jyc[5] = 0;
        jyc[6] = 0;
        jyc[7] = 0;
      } else
#endif
      {
        jxc[0] = intrinsic_fixed ? 0 : p0_p2;
        jxc[1] = f_p2;
        jxc[2] = 0;
        jxc[3] = -f_p2 * p0_p2;
        jxc[4] = -f_p2 * p0_p2 * y0;
        jxc[5] = f_p2 * (z0 + x0 * p0_p2);
        jxc[6] = -f_p2 * y0;

        jyc[0] = intrinsic_fixed ? 0 : p1_p2;
        jyc[1] = 0;
        jyc[2] = f_p2;
        jyc[3] = -f_p2 * p1_p2;
        jyc[4] = -f_p2 * (z0 + y0 * p1_p2);
        jyc[5] = f_p2 * x0 * p1_p2;
        jyc[6] = f_p2 * x0;

        if (radial_distortion == -1 && !intrinsic_fixed) {
          Float msn = ms[0] * ms[0] + ms[1] * ms[1];
          jxc[7] = -ms[0] * msn;
          jyc[7] = -ms[1] * msn;
        } else {
          jxc[7] = 0;
          jyc[7] = 0;
        }
      }
    }
    ///////////////////////////////////
    if (jxp) {
      jxp[0] = f_p2 * (r[0] - r[6] * p0_p2);
      jxp[1] = f_p2 * (r[1] - r[7] * p0_p2);
      jxp[2] = f_p2 * (r[2] - r[8] * p0_p2);
      jyp[0] = f_p2 * (r[3] - r[6] * p1_p2);
      jyp[1] = f_p2 * (r[4] - r[7] * p1_p2);
      jyp[2] = f_p2 * (r[5] - r[8] * p1_p2);
#ifdef POINT_DATA_ALIGN4
      jxp[3] = jyp[3] = 0;
#endif
    }
  }
}

template <class Float>
void ComputeJacobian(size_t nproj, size_t ncam, const Float* camera,
                     const Float* point, Float* jc, Float* jp, const int* jmap,
                     const Float* sj, const Float* ms, const int* cmlist,
                     bool intrinsic_fixed, int radial_distortion, bool shuffle,
                     Float* jct, int mt = 2, int i0 = 0);

DEFINE_THREAD_DATA(ComputeJacobian)
size_t nproj, ncam;
const Float *camera, *point;
Float *jc, *jp;
const int* jmap;
const Float *sj, *ms;
const int* cmlist;
bool intrinsic_fixed;
int radial_distortion;
bool shuffle;
Float* jct;
int i0;
BEGIN_THREAD_PROC(ComputeJacobian)
ComputeJacobian(q->nproj, q->ncam, q->camera, q->point, q->jc, q->jp, q->jmap,
                q->sj, q->ms, q->cmlist, q->intrinsic_fixed,
                q->radial_distortion, q->shuffle, q->jct, 0, q->i0);
END_THREAD_RPOC(ComputeJacobian)

template <class Float>
void ComputeJacobian(size_t nproj, size_t ncam, const Float* camera,
                     const Float* point, Float* jc, Float* jp, const int* jmap,
                     const Float* sj, const Float* ms, const int* cmlist,
                     bool intrinsic_fixed, int radial_distortion, bool shuffle,
                     Float* jct, int mt, int i0) {
  if (mt > 1 && nproj >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = nproj * i / thread_num;
      size_t last_ = nproj * (i + 1) / thread_num;
      size_t last = std::min(last_, nproj);
      RUN_THREAD(ComputeJacobian, threads[i], last, ncam, camera, point, jc, jp,
                 jmap + 2 * first, sj, ms + 2 * first, cmlist + first,
                 intrinsic_fixed, radial_distortion, shuffle, jct, first);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    const Float* sjc0 = sj;
    const Float* sjp0 = sj ? sj + ncam * 8 : NULL;

    for (size_t i = i0; i < nproj; ++i, jmap += 2, ms += 2, ++cmlist) {
      int cidx = jmap[0], pidx = jmap[1];
      const Float *c = camera + cidx * 16, *pt = point + pidx * POINT_ALIGN;
      Float* jci = jc ? (jc + (shuffle ? cmlist[0] : i) * 16) : NULL;
      Float* jpi = jp ? (jp + i * POINT_ALIGN2) : NULL;

      /////////////////////////////////////////////////////
      JacobianOne(c, pt, ms, jci, jci + 8, jpi, jpi + POINT_ALIGN,
                  intrinsic_fixed, radial_distortion);

      ///////////////////////////////////////////////////
      if (sjc0) {
        // jacobian scaling
        if (jci) {
          ScaleJ8(jci, jci + 8, sjc0 + cidx * 8);
        }
        if (jpi) {
          const Float* sjp = sjp0 + pidx * POINT_ALIGN;
          for (int j = 0; j < 3; ++j) {
            jpi[j] *= sjp[j];
            jpi[POINT_ALIGN + j] *= sjp[j];
          }
        }
      }

      if (jct && jc) MemoryCopyB(jci, jci + 16, jct + cmlist[0] * 16);
    }
  }
}

template <class Float>
void ComputeDiagonalAddQ(size_t ncam, const Float* qw, Float* d,
                         const Float* sj = NULL) {
  if (sj) {
    for (size_t i = 0; i < ncam; ++i, qw += 2, d += 8, sj += 8) {
      if (qw[0] == 0) continue;
      Float j1 = qw[0] * sj[0];
      Float j2 = qw[1] * sj[7];
      d[0] += (j1 * j1 * 2.0f);
      d[7] += (j2 * j2 * 2.0f);
    }
  } else {
    for (size_t i = 0; i < ncam; ++i, qw += 2, d += 8) {
      if (qw[0] == 0) continue;
      d[0] += (qw[0] * qw[0] * 2.0f);
      d[7] += (qw[1] * qw[1] * 2.0f);
    }
  }
}

///////////////////////////////////////
template <class Float>
void ComputeDiagonal(const avec<Float>& jcv, const vector<int>& cmapv,
                     const avec<Float>& jpv, const vector<int>& pmapv,
                     const vector<int>& cmlistv, const Float* qw0,
                     avec<Float>& jtjdi, bool jc_transpose, int radial) {
  // first camera part
  if (jcv.size() == 0 || jpv.size() == 0) return;  // not gonna happen

  size_t ncam = cmapv.size() - 1, npts = pmapv.size() - 1;
  const int vn = radial ? 8 : 7;
  SetVectorZero(jtjdi);

  const int* cmap = &cmapv[0];
  const int* pmap = &pmapv[0];
  const int* cmlist = &cmlistv[0];
  const Float* jc = &jcv[0];
  const Float* jp = &jpv[0];
  const Float* qw = qw0;
  Float* jji = &jtjdi[0];

  ///////compute jc part
  for (size_t i = 0; i < ncam; ++i, jji += 8, ++cmap, qw += 2) {
    int idx1 = cmap[0], idx2 = cmap[1];
    //////////////////////////////////////
    for (int j = idx1; j < idx2; ++j) {
      int idx = jc_transpose ? j : cmlist[j];
      const Float* pj = jc + idx * 16;
      ///////////////////////////////////////////
      for (int k = 0; k < vn; ++k)
        jji[k] += (pj[k] * pj[k] + pj[k + 8] * pj[k + 8]);
    }
    if (qw0 && qw[0] > 0) {
      jji[0] += (qw[0] * qw[0] * 2.0f);
      jji[7] += (qw[1] * qw[1] * 2.0f);
    }
  }

  for (size_t i = 0; i < npts; ++i, jji += POINT_ALIGN, ++pmap) {
    int idx1 = pmap[0], idx2 = pmap[1];
    const Float* pj = jp + idx1 * POINT_ALIGN2;
    for (int j = idx1; j < idx2; ++j, pj += POINT_ALIGN2) {
      for (int k = 0; k < 3; ++k)
        jji[k] += (pj[k] * pj[k] + pj[k + POINT_ALIGN] * pj[k + POINT_ALIGN]);
    }
  }
  Float* it = jtjdi.begin();
  for (; it < jtjdi.end(); ++it) {
    *it = (*it == 0) ? 0 : Float(1.0 / (*it));
  }
}

template <class T, int n, int m>
void InvertSymmetricMatrix(T a[n][m], T ai[n][m]) {
  for (int i = 0; i < n; ++i) {
    if (a[i][i] > 0) {
      a[i][i] = sqrt(a[i][i]);
      for (int j = i + 1; j < n; ++j) a[j][i] = a[j][i] / a[i][i];
      for (int j = i + 1; j < n; ++j)
        for (int k = j; k < n; ++k) a[k][j] -= a[k][i] * a[j][i];
    }
  }
  /////////////////////////////
  // inv(L)
  for (int i = 0; i < n; ++i) {
    if (a[i][i] == 0) continue;
    a[i][i] = 1.0f / a[i][i];
  }
  for (int i = 1; i < n; ++i) {
    if (a[i][i] == 0) continue;
    for (int j = 0; j < i; ++j) {
      T sum = 0;
      for (int k = j; k < i; ++k) sum += (a[i][k] * a[k][j]);
      a[i][j] = -sum * a[i][i];
    }
  }
  /////////////////////////////
  // inv(L)'  * inv(L)
  for (int i = 0; i < n; ++i) {
    for (int j = i; j < n; ++j) {
      ai[i][j] = 0;
      for (int k = j; k < n; ++k) ai[i][j] += a[k][i] * a[k][j];
      ai[j][i] = ai[i][j];
    }
  }
}
template <class T, int n, int m>
void InvertSymmetricMatrix(T* a, T* ai) {
  InvertSymmetricMatrix<T, n, m>((T(*)[m])a, (T(*)[m])ai);
}

template <class Float>
void ComputeDiagonalBlockC(size_t ncam, float lambda1, float lambda2,
                           const Float* jc, const int* cmap, const int* cmlist,
                           Float* di, Float* bi, int vn, bool jc_transpose,
                           bool use_jq, int mt);

DEFINE_THREAD_DATA(ComputeDiagonalBlockC)
size_t ncam;
float lambda1, lambda2;
const Float* jc;
const int *cmap, *cmlist;
Float *di, *bi;
int vn;
bool jc_transpose, use_jq;
BEGIN_THREAD_PROC(ComputeDiagonalBlockC)
ComputeDiagonalBlockC(q->ncam, q->lambda1, q->lambda2, q->jc, q->cmap,
                      q->cmlist, q->di, q->bi, q->vn, q->jc_transpose,
                      q->use_jq, 0);
END_THREAD_RPOC(ComputeDiagonalBlockC)

template <class Float>
void ComputeDiagonalBlockC(size_t ncam, float lambda1, float lambda2,
                           const Float* jc, const int* cmap, const int* cmlist,
                           Float* di, Float* bi, int vn, bool jc_transpose,
                           bool use_jq, int mt) {
  const size_t bc = vn * 8;

  if (mt > 1 && ncam >= (size_t)mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = ncam * i / thread_num;
      size_t last_ = ncam * (i + 1) / thread_num;
      size_t last = std::min(last_, ncam);
      RUN_THREAD(ComputeDiagonalBlockC, threads[i], (last - first), lambda1,
                 lambda2, jc, cmap + first, cmlist, di + 8 * first,
                 bi + bc * first, vn, jc_transpose, use_jq);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    Float bufv[64 + 8];  // size_t offset = ((size_t)bufv) & 0xf;
    // Float* pbuf = bufv + ((16 - offset) / sizeof(Float));
    Float* pbuf = (Float*)ALIGN_PTR(bufv);

    ///////compute jc part
    for (size_t i = 0; i < ncam; ++i, ++cmap, bi += bc) {
      int idx1 = cmap[0], idx2 = cmap[1];
      //////////////////////////////////////
      if (idx1 == idx2) {
        SetVectorZero(bi, bi + bc);
      } else {
        SetVectorZero(pbuf, pbuf + 64);

        for (int j = idx1; j < idx2; ++j) {
          int idx = jc_transpose ? j : cmlist[j];
          const Float* pj = jc + idx * 16;
          /////////////////////////////////
          AddBlockJtJ(pj, pbuf, vn);
          AddBlockJtJ(pj + 8, pbuf, vn);
        }

        // change and copy the diagonal

        if (use_jq) {
          Float* pb = pbuf;
          for (int j = 0; j < 8; ++j, ++di, pb += 9) {
            Float temp;
            di[0] = temp = (di[0] + pb[0]);
            pb[0] = lambda2 * temp + lambda1;
          }
        } else {
          Float* pb = pbuf;
          for (int j = 0; j < 8; ++j, ++di, pb += 9) {
            *pb = lambda2 * ((*di) = (*pb)) + lambda1;
          }
        }

        // invert the matrix?
        if (vn == 8)
          InvertSymmetricMatrix<Float, 8, 8>(pbuf, bi);
        else
          InvertSymmetricMatrix<Float, 7, 8>(pbuf, bi);
      }
    }
  }
}

template <class Float>
void ComputeDiagonalBlockP(size_t npt, float lambda1, float lambda2,
                           const Float* jp, const int* pmap, Float* di,
                           Float* bi, int mt);

DEFINE_THREAD_DATA(ComputeDiagonalBlockP)
size_t npt;
float lambda1, lambda2;
const Float* jp;
const int* pmap;
Float *di, *bi;
BEGIN_THREAD_PROC(ComputeDiagonalBlockP)
ComputeDiagonalBlockP(q->npt, q->lambda1, q->lambda2, q->jp, q->pmap, q->di,
                      q->bi, 0);
END_THREAD_RPOC(ComputeDiagonalBlockP)

template <class Float>
void ComputeDiagonalBlockP(size_t npt, float lambda1, float lambda2,
                           const Float* jp, const int* pmap, Float* di,
                           Float* bi, int mt) {
  if (mt > 1) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = npt * i / thread_num;
      size_t last_ = npt * (i + 1) / thread_num;
      size_t last = std::min(last_, npt);
      RUN_THREAD(ComputeDiagonalBlockP, threads[i], (last - first), lambda1,
                 lambda2, jp, pmap + first, di + POINT_ALIGN * first,
                 bi + 6 * first);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    for (size_t i = 0; i < npt; ++i, ++pmap, di += POINT_ALIGN, bi += 6) {
      int idx1 = pmap[0], idx2 = pmap[1];

      Float M00 = 0, M01 = 0, M02 = 0, M11 = 0, M12 = 0, M22 = 0;
      const Float *jxp = jp + idx1 * (POINT_ALIGN2), *jyp = jxp + POINT_ALIGN;
      for (int j = idx1; j < idx2;
           ++j, jxp += POINT_ALIGN2, jyp += POINT_ALIGN2) {
        M00 += (jxp[0] * jxp[0] + jyp[0] * jyp[0]);
        M01 += (jxp[0] * jxp[1] + jyp[0] * jyp[1]);
        M02 += (jxp[0] * jxp[2] + jyp[0] * jyp[2]);
        M11 += (jxp[1] * jxp[1] + jyp[1] * jyp[1]);
        M12 += (jxp[1] * jxp[2] + jyp[1] * jyp[2]);
        M22 += (jxp[2] * jxp[2] + jyp[2] * jyp[2]);
      }

      /////////////////////////////////
      di[0] = M00;
      di[1] = M11;
      di[2] = M22;

      /////////////////////////////
      M00 = M00 * lambda2 + lambda1;
      M11 = M11 * lambda2 + lambda1;
      M22 = M22 * lambda2 + lambda1;

      ///////////////////////////////
      Float det = (M00 * M11 - M01 * M01) * M22 + Float(2.0) * M01 * M12 * M02 -
                  M02 * M02 * M11 - M12 * M12 * M00;
      if (det >= FLT_MAX || det <= FLT_MIN * 2.0f) {
        // SetVectorZero(bi, bi + 6);
        for (int j = 0; j < 6; ++j) bi[j] = 0;
      } else {
        bi[0] = (M11 * M22 - M12 * M12) / det;
        bi[1] = -(M01 * M22 - M12 * M02) / det;
        bi[2] = (M01 * M12 - M02 * M11) / det;
        bi[3] = (M00 * M22 - M02 * M02) / det;
        bi[4] = -(M00 * M12 - M01 * M02) / det;
        bi[5] = (M00 * M11 - M01 * M01) / det;
      }
    }
  }
}

template <class Float>
void ComputeDiagonalBlock(size_t ncam, size_t npts, float lambda, bool dampd,
                          const Float* jc, const int* cmap, const Float* jp,
                          const int* pmap, const int* cmlist, const Float* sj,
                          const Float* wq, Float* diag, Float* blocks,
                          int radial_distortion, bool jc_transpose, int mt1 = 2,
                          int mt2 = 2, int mode = 0) {
  const int vn = radial_distortion ? 8 : 7;
  const size_t bc = vn * 8;
  float lambda1 = dampd ? 0.0f : lambda;
  float lambda2 = dampd ? (1.0f + lambda) : 1.0f;

  if (mode == 0) {
    const size_t bsz = bc * ncam + npts * 6;
    const size_t dsz = 8 * ncam + npts * POINT_ALIGN;
    bool use_jq = wq != NULL;
    ///////////////////////////////////////////
    SetVectorZero(blocks, blocks + bsz);
    SetVectorZero(diag, diag + dsz);

    ////////////////////////////////
    if (use_jq) ComputeDiagonalAddQ(ncam, wq, diag, sj);
    ComputeDiagonalBlockC(ncam, lambda1, lambda2, jc, cmap, cmlist, diag,
                          blocks, vn, jc_transpose, use_jq, mt1);
    ComputeDiagonalBlockP(npts, lambda1, lambda2, jp, pmap, diag + 8 * ncam,
                          blocks + bc * ncam, mt2);
  } else if (mode == 1) {
    const size_t bsz = bc * ncam;
    const size_t dsz = 8 * ncam;
    bool use_jq = wq != NULL;
    ///////////////////////////////////////////
    SetVectorZero(blocks, blocks + bsz);
    SetVectorZero(diag, diag + dsz);

    ////////////////////////////////
    if (use_jq) ComputeDiagonalAddQ(ncam, wq, diag, sj);
    ComputeDiagonalBlockC(ncam, lambda1, lambda2, jc, cmap, cmlist, diag,
                          blocks, vn, jc_transpose, use_jq, mt1);
  } else {
    blocks += bc * ncam;
    diag += 8 * ncam;
    const size_t bsz = npts * 6;
    const size_t dsz = npts * POINT_ALIGN;
    ///////////////////////////////////////////
    SetVectorZero(blocks, blocks + bsz);
    SetVectorZero(diag, diag + dsz);

    ////////////////////////////////
    ComputeDiagonalBlockP(npts, lambda1, lambda2, jp, pmap, diag, blocks, mt2);
  }
}

template <class Float>
void ComputeDiagonalBlock_(float lambda, bool dampd, const avec<Float>& camerav,
                           const avec<Float>& pointv, const avec<Float>& meas,
                           const vector<int>& jmapv, const avec<Float>& sjv,
                           avec<Float>& qwv, avec<Float>& diag,
                           avec<Float>& blocks, bool intrinsic_fixed,
                           int radial_distortion, int mode = 0) {
  const int vn = radial_distortion ? 8 : 7;
  const size_t szbc = vn * 8;
  size_t ncam = camerav.size() / 16;
  size_t npts = pointv.size() / POINT_ALIGN;
  size_t sz_jcd = ncam * 8;
  size_t sz_jcb = ncam * szbc;
  avec<Float> blockpv(blocks.size());
  SetVectorZero(blockpv);
  SetVectorZero(diag);
  //////////////////////////////////////////////////////
  float lambda1 = dampd ? 0.0f : lambda;
  float lambda2 = dampd ? (1.0f + lambda) : 1.0f;

  Float jbufv[24 + 8];  // size_t offset = ((size_t) jbufv) & 0xf;
  // Float* jxc = jbufv + ((16 - offset) / sizeof(Float));
  Float* jxc = (Float*)ALIGN_PTR(jbufv);
  Float *jyc = jxc + 8, *jxp = jxc + 16, *jyp = jxc + 20;

  //////////////////////////////
  const int* jmap = &jmapv[0];
  const Float* camera = &camerav[0];
  const Float* point = &pointv[0];
  const Float* ms = &meas[0];
  const Float* sjc0 = sjv.size() ? &sjv[0] : NULL;
  const Float* sjp0 = sjv.size() ? &sjv[sz_jcd] : NULL;
  //////////////////////////////////////////////
  Float *blockpc = &blockpv[0], *blockpp = &blockpv[sz_jcb];
  Float *bo = blockpc, *bi = &blocks[0], *di = &diag[0];

  /////////////////////////////////////////////////////////
  // diagonal blocks
  for (size_t i = 0; i < jmapv.size(); i += 2, jmap += 2, ms += 2) {
    int cidx = jmap[0], pidx = jmap[1];
    const Float *c = camera + cidx * 16, *pt = point + pidx * POINT_ALIGN;
    /////////////////////////////////////////////////////////
    JacobianOne(c, pt, ms, jxc, jyc, jxp, jyp, intrinsic_fixed,
                radial_distortion);

    ///////////////////////////////////////////////////////////
    if (mode != 2) {
      if (sjc0) {
        const Float* sjc = sjc0 + cidx * 8;
        ScaleJ8(jxc, jyc, sjc);
      }
      /////////////////////////////////////////
      Float* bc = blockpc + cidx * szbc;
      AddBlockJtJ(jxc, bc, vn);
      AddBlockJtJ(jyc, bc, vn);
    }

    if (mode != 1) {
      if (sjp0) {
        const Float* sjp = sjp0 + pidx * POINT_ALIGN;
        jxp[0] *= sjp[0];
        jxp[1] *= sjp[1];
        jxp[2] *= sjp[2];
        jyp[0] *= sjp[0];
        jyp[1] *= sjp[1];
        jyp[2] *= sjp[2];
      }

      ///////////////////////////////////////////
      Float* bp = blockpp + pidx * 6;
      bp[0] += (jxp[0] * jxp[0] + jyp[0] * jyp[0]);
      bp[1] += (jxp[0] * jxp[1] + jyp[0] * jyp[1]);
      bp[2] += (jxp[0] * jxp[2] + jyp[0] * jyp[2]);
      bp[3] += (jxp[1] * jxp[1] + jyp[1] * jyp[1]);
      bp[4] += (jxp[1] * jxp[2] + jyp[1] * jyp[2]);
      bp[5] += (jxp[2] * jxp[2] + jyp[2] * jyp[2]);
    }
  }

  /// invert the camera part
  if (mode != 2) {
    /////////////////////////////////////////
    const Float* qw = qwv.begin();
    if (qw) {
      for (size_t i = 0; i < ncam; ++i, qw += 2) {
        if (qw[0] == 0) continue;
        Float* bc = blockpc + i * szbc;
        if (sjc0) {
          const Float* sjc = sjc0 + i * 8;
          Float j1 = sjc[0] * qw[0];
          Float j2 = sjc[7] * qw[1];
          bc[0] += (j1 * j1 * 2.0f);
          if (radial_distortion) bc[63] += (j2 * j2 * 2.0f);
        } else {
          const Float* sjc = sjc0 + i * 8;
          bc[0] += (qw[0] * qw[0] * 2.0f);
          if (radial_distortion) bc[63] += (qw[1] * qw[1] * 2.0f);
        }
      }
    }

    for (size_t i = 0; i < ncam; ++i, bo += szbc, bi += szbc, di += 8) {
      Float *bp = bo, *dip = di;
      for (int j = 0; j < vn; ++j, ++dip, bp += 9) {
        dip[0] = bp[0];
        bp[0] = lambda2 * bp[0] + lambda1;
      }

      // invert the matrix?
      if (radial_distortion)
        InvertSymmetricMatrix<Float, 8, 8>(bo, bi);
      else
        InvertSymmetricMatrix<Float, 7, 8>(bo, bi);
    }
  } else {
    bo += szbc * ncam;
    bi += szbc * ncam;
    di += 8 * ncam;
  }

  ///////////////////////////////////////////
  // inverting the point part
  if (mode != 1) {
    for (size_t i = 0; i < npts; ++i, bo += 6, bi += 6, di += POINT_ALIGN) {
      Float &M00 = bo[0], &M01 = bo[1], &M02 = bo[2];
      Float &M11 = bo[3], &M12 = bo[4], &M22 = bo[5];
      di[0] = M00;
      di[1] = M11;
      di[2] = M22;

      /////////////////////////////
      M00 = M00 * lambda2 + lambda1;
      M11 = M11 * lambda2 + lambda1;
      M22 = M22 * lambda2 + lambda1;

      ///////////////////////////////
      Float det = (M00 * M11 - M01 * M01) * M22 + Float(2.0) * M01 * M12 * M02 -
                  M02 * M02 * M11 - M12 * M12 * M00;
      if (det >= FLT_MAX || det <= FLT_MIN * 2.0f) {
        for (int j = 0; j < 6; ++j) bi[j] = 0;
      } else {
        bi[0] = (M11 * M22 - M12 * M12) / det;
        bi[1] = -(M01 * M22 - M12 * M02) / det;
        bi[2] = (M01 * M12 - M02 * M11) / det;
        bi[3] = (M00 * M22 - M02 * M02) / det;
        bi[4] = -(M00 * M12 - M01 * M02) / det;
        bi[5] = (M00 * M11 - M01 * M01) / det;
      }
    }
  }
}

template <class Float>
void MultiplyBlockConditionerC(int ncam, const Float* bi, const Float* x,
                               Float* vx, int vn, int mt = 0);

DEFINE_THREAD_DATA(MultiplyBlockConditionerC)
int ncam;
const Float *bi, *x;
Float* vx;
int vn;
BEGIN_THREAD_PROC(MultiplyBlockConditionerC)
MultiplyBlockConditionerC(q->ncam, q->bi, q->x, q->vx, q->vn, 0);
END_THREAD_RPOC(MultiplyBlockConditionerC)

template <class Float>
void MultiplyBlockConditionerC(int ncam, const Float* bi, const Float* x,
                               Float* vx, int vn, int mt) {
  if (mt > 1 && ncam >= mt) {
    const size_t bc = vn * 8;
    MYTHREAD threads[THREAD_NUM_MAX];
    const int thread_num = std::min(mt, THREAD_NUM_MAX);
    for (int i = 0; i < thread_num; ++i) {
      int first = ncam * i / thread_num;
      int last_ = ncam * (i + 1) / thread_num;
      int last = std::min(last_, ncam);
      RUN_THREAD(MultiplyBlockConditionerC, threads[i], (last - first),
                 bi + first * bc, x + 8 * first, vx + 8 * first, vn);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    for (int i = 0; i < ncam; ++i, x += 8, vx += 8) {
      Float* vxc = vx;
      for (int j = 0; j < vn; ++j, bi += 8, ++vxc) *vxc = DotProduct8(bi, x);
    }
  }
}

template <class Float>
void MultiplyBlockConditionerP(int npoint, const Float* bi, const Float* x,
                               Float* vx, int mt = 0);

DEFINE_THREAD_DATA(MultiplyBlockConditionerP)
int npoint;
const Float *bi, *x;
Float* vx;
BEGIN_THREAD_PROC(MultiplyBlockConditionerP)
MultiplyBlockConditionerP(q->npoint, q->bi, q->x, q->vx, 0);
END_THREAD_RPOC(MultiplyBlockConditionerP)

template <class Float>
void MultiplyBlockConditionerP(int npoint, const Float* bi, const Float* x,
                               Float* vx, int mt) {
  if (mt > 1 && npoint >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const int thread_num = std::min(mt, THREAD_NUM_MAX);
    for (int i = 0; i < thread_num; ++i) {
      int first = npoint * i / thread_num;
      int last_ = npoint * (i + 1) / thread_num;
      int last = std::min(last_, npoint);
      RUN_THREAD(MultiplyBlockConditionerP, threads[i], (last - first),
                 bi + first * 6, x + POINT_ALIGN * first,
                 vx + POINT_ALIGN * first);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    for (int i = 0; i < npoint;
         ++i, bi += 6, x += POINT_ALIGN, vx += POINT_ALIGN) {
      vx[0] = (bi[0] * x[0] + bi[1] * x[1] + bi[2] * x[2]);
      vx[1] = (bi[1] * x[0] + bi[3] * x[1] + bi[4] * x[2]);
      vx[2] = (bi[2] * x[0] + bi[4] * x[1] + bi[5] * x[2]);
    }
  }
}

template <class Float>
void MultiplyBlockConditioner(int ncam, int npoint, const Float* blocksv,
                              const Float* vec, Float* resultv, int radial,
                              int mode, int mt1, int mt2) {
  const int vn = radial ? 8 : 7;
  if (mode != 2)
    MultiplyBlockConditionerC(ncam, blocksv, vec, resultv, vn, mt1);
  if (mt2 == 0) mt2 = AUTO_MT_NUM(npoint * 24);
  if (mode != 1)
    MultiplyBlockConditionerP(npoint, blocksv + (vn * 8 * ncam), vec + ncam * 8,
                              resultv + 8 * ncam, mt2);
}

template <class Float>
void ComputeJX(size_t nproj, size_t ncam, const Float* x, const Float* jc,
               const Float* jp, const int* jmap, Float* jx, int mode,
               int mt = 2);

DEFINE_THREAD_DATA(ComputeJX)
size_t nproj, ncam;
const Float *xc, *jc, *jp;
const int* jmap;
Float* jx;
int mode;
BEGIN_THREAD_PROC(ComputeJX)
ComputeJX(q->nproj, q->ncam, q->xc, q->jc, q->jp, q->jmap, q->jx, q->mode, 0);
END_THREAD_RPOC(ComputeJX)

template <class Float>
void ComputeJX(size_t nproj, size_t ncam, const Float* x, const Float* jc,
               const Float* jp, const int* jmap, Float* jx, int mode, int mt) {
  if (mt > 1 && nproj >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = nproj * i / thread_num;
      size_t last_ = nproj * (i + 1) / thread_num;
      size_t last = std::min(last_, nproj);
      RUN_THREAD(ComputeJX, threads[i], (last - first), ncam, x,
                 jc + 16 * first, jp + POINT_ALIGN2 * first, jmap + first * 2,
                 jx + first * 2, mode);
    }
    WAIT_THREAD(threads, thread_num);
  } else if (mode == 0) {
    const Float *pxc = x, *pxp = pxc + ncam * 8;
    // clock_t tp = clock(); double s1 = 0, s2  = 0;
    for (size_t i = 0; i < nproj;
         ++i, jmap += 2, jc += 16, jp += POINT_ALIGN2, jx += 2) {
      ComputeTwoJX(jc, jp, pxc + jmap[0] * 8, pxp + jmap[1] * POINT_ALIGN, jx);
    }
  } else if (mode == 1) {
    const Float* pxc = x;
    // clock_t tp = clock(); double s1 = 0, s2  = 0;
    for (size_t i = 0; i < nproj;
         ++i, jmap += 2, jc += 16, jp += POINT_ALIGN2, jx += 2) {
      const Float* xc = pxc + jmap[0] * 8;
      jx[0] = DotProduct8(jc, xc);
      jx[1] = DotProduct8(jc + 8, xc);
    }
  } else if (mode == 2) {
    const Float* pxp = x + ncam * 8;
    // clock_t tp = clock(); double s1 = 0, s2  = 0;
    for (size_t i = 0; i < nproj;
         ++i, jmap += 2, jc += 16, jp += POINT_ALIGN2, jx += 2) {
      const Float* xp = pxp + jmap[1] * POINT_ALIGN;
      jx[0] = (jp[0] * xp[0] + jp[1] * xp[1] + jp[2] * xp[2]);
      jx[1] = (jp[3] * xp[0] + jp[4] * xp[1] + jp[5] * xp[2]);
    }
  }
}

template <class Float>
void ComputeJX_(size_t nproj, size_t ncam, const Float* x, Float* jx,
                const Float* camera, const Float* point, const Float* ms,
                const Float* sj, const int* jmap, bool intrinsic_fixed,
                int radial_distortion, int mode, int mt = 16);

DEFINE_THREAD_DATA(ComputeJX_)
size_t nproj, ncam;
const Float* x;
Float* jx;
const Float *camera, *point, *ms, *sj;
const int* jmap;
bool intrinsic_fixed;
int radial_distortion;
int mode;
BEGIN_THREAD_PROC(ComputeJX_)
ComputeJX_(q->nproj, q->ncam, q->x, q->jx, q->camera, q->point, q->ms, q->sj,
           q->jmap, q->intrinsic_fixed, q->radial_distortion, q->mode, 0);
END_THREAD_RPOC(ComputeJX_)

template <class Float>
void ComputeJX_(size_t nproj, size_t ncam, const Float* x, Float* jx,
                const Float* camera, const Float* point, const Float* ms,
                const Float* sj, const int* jmap, bool intrinsic_fixed,
                int radial_distortion, int mode, int mt) {
  if (mt > 1 && nproj >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = nproj * i / thread_num;
      size_t last_ = nproj * (i + 1) / thread_num;
      size_t last = std::min(last_, nproj);
      RUN_THREAD(ComputeJX_, threads[i], (last - first), ncam, x,
                 jx + first * 2, camera, point, ms + 2 * first, sj,
                 jmap + first * 2, intrinsic_fixed, radial_distortion, mode);
    }
    WAIT_THREAD(threads, thread_num);
  } else if (mode == 0) {
    Float jcv[24 + 8];  // size_t offset = ((size_t) jcv) & 0xf;
    // Float* jc = jcv + (16 - offset) / sizeof(Float), *jp = jc + 16;
    Float *jc = (Float *)ALIGN_PTR(jcv), *jp = jc + 16;
    ////////////////////////////////////////
    const Float* sjc = sj;
    const Float* sjp = sjc ? (sjc + ncam * 8) : NULL;
    const Float *xc0 = x, *xp0 = x + ncam * 8;

    /////////////////////////////////
    for (size_t i = 0; i < nproj; ++i, ms += 2, jmap += 2, jx += 2) {
      const int cidx = jmap[0], pidx = jmap[1];
      const Float *c = camera + cidx * 16, *pt = point + pidx * POINT_ALIGN;
      /////////////////////////////////////////////////////
      JacobianOne(c, pt, ms, jc, jc + 8, jp, jp + POINT_ALIGN, intrinsic_fixed,
                  radial_distortion);
      if (sjc) {
        // jacobian scaling
        ScaleJ8(jc, jc + 8, sjc + cidx * 8);
        const Float* sjpi = sjp + pidx * POINT_ALIGN;
        for (int j = 0; j < 3; ++j) {
          jp[j] *= sjpi[j];
          jp[POINT_ALIGN + j] *= sjpi[j];
        }
      }
      ////////////////////////////////////
      ComputeTwoJX(jc, jp, xc0 + cidx * 8, xp0 + pidx * POINT_ALIGN, jx);
    }
  } else if (mode == 1) {
    Float jcv[24 + 8];  // size_t offset = ((size_t) jcv) & 0xf;
    // Float* jc = jcv + (16 - offset) / sizeof(Float);
    Float* jc = (Float*)ALIGN_PTR(jcv);

    ////////////////////////////////////////
    const Float *sjc = sj, *xc0 = x;

    /////////////////////////////////
    for (size_t i = 0; i < nproj; ++i, ms += 2, jmap += 2, jx += 2) {
      const int cidx = jmap[0], pidx = jmap[1];
      const Float *c = camera + cidx * 16, *pt = point + pidx * POINT_ALIGN;
      /////////////////////////////////////////////////////
      JacobianOne(c, pt, ms, jc, jc + 8, (Float*)NULL, (Float*)NULL,
                  intrinsic_fixed, radial_distortion);
      if (sjc) ScaleJ8(jc, jc + 8, sjc + cidx * 8);
      const Float* xc = xc0 + cidx * 8;
      jx[0] = DotProduct8(jc, xc);
      jx[1] = DotProduct8(jc + 8, xc);
    }
  } else if (mode == 2) {
    Float jp[8];

    ////////////////////////////////////////
    const Float* sjp = sj ? (sj + ncam * 8) : NULL;
    const Float* xp0 = x + ncam * 8;

    /////////////////////////////////
    for (size_t i = 0; i < nproj; ++i, ms += 2, jmap += 2, jx += 2) {
      const int cidx = jmap[0], pidx = jmap[1];
      const Float *c = camera + cidx * 16, *pt = point + pidx * POINT_ALIGN;
      /////////////////////////////////////////////////////
      JacobianOne(c, pt, ms, (Float*)NULL, (Float*)NULL, jp, jp + POINT_ALIGN,
                  intrinsic_fixed, radial_distortion);

      const Float* xp = xp0 + pidx * POINT_ALIGN;
      if (sjp) {
        const Float* s = sjp + pidx * POINT_ALIGN;
        jx[0] = (jp[0] * xp[0] * s[0] + jp[1] * xp[1] * s[1] +
                 jp[2] * xp[2] * s[2]);
        jx[1] = (jp[3] * xp[0] * s[0] + jp[4] * xp[1] * s[1] +
                 jp[5] * xp[2] * s[2]);
      } else {
        jx[0] = (jp[0] * xp[0] + jp[1] * xp[1] + jp[2] * xp[2]);
        jx[1] = (jp[3] * xp[0] + jp[4] * xp[1] + jp[5] * xp[2]);
      }
    }
  }
}

template <class Float>
void ComputeJtEC(size_t ncam, const Float* pe, const Float* jc, const int* cmap,
                 const int* cmlist, Float* v, bool jc_transpose, int mt);

DEFINE_THREAD_DATA(ComputeJtEC)
size_t ncam;
const Float *pe, *jc;
const int *cmap, *cmlist;
Float* v;
bool jc_transpose;
BEGIN_THREAD_PROC(ComputeJtEC)
ComputeJtEC(q->ncam, q->pe, q->jc, q->cmap, q->cmlist, q->v, q->jc_transpose,
            0);
END_THREAD_RPOC(ComputeJtEC)

template <class Float>
void ComputeJtEC(size_t ncam, const Float* pe, const Float* jc, const int* cmap,
                 const int* cmlist, Float* v, bool jc_transpose, int mt) {
  if (mt > 1 && ncam >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];  // if(ncam < mt) mt = ncam;
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = ncam * i / thread_num;
      size_t last_ = ncam * (i + 1) / thread_num;
      size_t last = std::min(last_, ncam);
      RUN_THREAD(ComputeJtEC, threads[i], (last - first), pe, jc, cmap + first,
                 cmlist, v + 8 * first, jc_transpose);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    /////////////////////////////////
    for (size_t i = 0; i < ncam; ++i, ++cmap, v += 8) {
      int idx1 = cmap[0], idx2 = cmap[1];
      for (int j = idx1; j < idx2; ++j) {
        int edx = cmlist[j];
        const Float* pj = jc + ((jc_transpose ? j : edx) * 16);
        const Float* e = pe + edx * 2;
        //////////////////////////////
        AddScaledVec8(e[0], pj, v);
        AddScaledVec8(e[1], pj + 8, v);
      }
    }
  }
}

template <class Float>
void ComputeJtEP(size_t npt, const Float* pe, const Float* jp, const int* pmap,
                 Float* v, int mt);

DEFINE_THREAD_DATA(ComputeJtEP)
size_t npt;
const Float *pe, *jp;
const int* pmap;
Float* v;
BEGIN_THREAD_PROC(ComputeJtEP)
ComputeJtEP(q->npt, q->pe, q->jp, q->pmap, q->v, 0);
END_THREAD_RPOC(ComputeJtEP)

template <class Float>
void ComputeJtEP(size_t npt, const Float* pe, const Float* jp, const int* pmap,
                 Float* v, int mt) {
  if (mt > 1 && npt >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = npt * i / thread_num;
      size_t last_ = npt * (i + 1) / thread_num;
      size_t last = std::min(last_, npt);
      RUN_THREAD(ComputeJtEP, threads[i], (last - first), pe, jp, pmap + first,
                 v + POINT_ALIGN * first);
    }
    WAIT_THREAD(threads, thread_num);
  } else {
    for (size_t i = 0; i < npt; ++i, ++pmap, v += POINT_ALIGN) {
      int idx1 = pmap[0], idx2 = pmap[1];
      const Float* pj = jp + idx1 * POINT_ALIGN2;
      const Float* e = pe + idx1 * 2;
      Float temp[3] = {0, 0, 0};
      for (int j = idx1; j < idx2; ++j, pj += POINT_ALIGN2, e += 2) {
        temp[0] += (e[0] * pj[0] + e[1] * pj[POINT_ALIGN]);
        temp[1] += (e[0] * pj[1] + e[1] * pj[POINT_ALIGN + 1]);
        temp[2] += (e[0] * pj[2] + e[1] * pj[POINT_ALIGN + 2]);
      }
      v[0] = temp[0];
      v[1] = temp[1];
      v[2] = temp[2];
    }
  }
}

template <class Float>
void ComputeJtE(size_t ncam, size_t npt, const Float* pe, const Float* jc,
                const int* cmap, const int* cmlist, const Float* jp,
                const int* pmap, Float* v, bool jc_transpose, int mode, int mt1,
                int mt2) {
  if (mode != 2) {
    SetVectorZero(v, v + ncam * 8);
    ComputeJtEC(ncam, pe, jc, cmap, cmlist, v, jc_transpose, mt1);
  }
  if (mode != 1) {
    ComputeJtEP(npt, pe, jp, pmap, v + 8 * ncam, mt2);
  }
}

template <class Float>
void ComputeJtEC_(size_t ncam, const Float* ee, Float* jte, const Float* c,
                  const Float* point, const Float* ms, const int* jmap,
                  const int* cmap, const int* cmlist, bool intrinsic_fixed,
                  int radial_distortion, int mt);

DEFINE_THREAD_DATA(ComputeJtEC_)
size_t ncam;
const Float* ee;
Float* jte;
const Float *c, *point, *ms;
const int *jmap, *cmap, *cmlist;
bool intrinsic_fixed;
int radial_distortion;
BEGIN_THREAD_PROC(ComputeJtEC_)
ComputeJtEC_(q->ncam, q->ee, q->jte, q->c, q->point, q->ms, q->jmap, q->cmap,
             q->cmlist, q->intrinsic_fixed, q->radial_distortion, 0);
END_THREAD_RPOC(ComputeJtEC_)

template <class Float>
void ComputeJtEC_(size_t ncam, const Float* ee, Float* jte, const Float* c,
                  const Float* point, const Float* ms, const int* jmap,
                  const int* cmap, const int* cmlist, bool intrinsic_fixed,
                  int radial_distortion, int mt) {
  if (mt > 1 && ncam >= mt) {
    MYTHREAD threads[THREAD_NUM_MAX];
    // if(ncam < mt) mt = ncam;
    const size_t thread_num = std::min(mt, THREAD_NUM_MAX);
    for (size_t i = 0; i < thread_num; ++i) {
      size_t first = ncam * i / thread_num;
      size_t last_ = ncam * (i + 1) / thread_num;
      size_t last = std::min(last_, ncam);
      RUN_THREAD(ComputeJtEC_, threads[i], (last - first), ee, jte + 8 * first,
                 c + first * 16, point, ms, jmap, cmap + first, cmlist,
                 intrinsic_fixed, radial_distortion);
    }
    WAIT_THREAD(threads, thread_num);

  } else {
    /////////////////////////////////
    Float jcv[16 + 8];  // size_t offset = ((size_t) jcv) & 0xf;
    // Float* jcx = jcv + ((16 - offset) / sizeof(Float)), * jcy = jcx + 8;
    Float *jcx = (Float *)ALIGN_PTR(jcv), *jcy = jcx + 8;

    for (size_t i = 0; i < ncam; ++i, ++cmap, jte += 8, c += 16) {
      int idx1 = cmap[0], idx2 = cmap[1];

      for (int j = idx1; j < idx2; ++j) {
        int index = cmlist[j];
        const Float* pt = point + jmap[2 * index + 1] * POINT_ALIGN;
        const Float* e = ee + index * 2;

        JacobianOne(c, pt, ms + index * 2, jcx, jcy, (Float*)NULL, (Float*)NULL,
                    intrinsic_fixed, radial_distortion);

        //////////////////////////////
        AddScaledVec8(e[0], jcx, jte);
        AddScaledVec8(e[1], jcy, jte);
      }
    }
  }
}

template <class Float>
void ComputeJtE_(size_t nproj, size_t ncam, size_t npt, const Float* ee,
                 Float* jte, const Float* camera, const Float* point,
                 const Float* ms, const int* jmap, const int* cmap,
                 const int* cmlist, const int* pmap, const Float* jp,
                 bool intrinsic_fixed, int radial_distortion, int mode,
                 int mt) {
  if (mode != 2) {
    SetVectorZero(jte, jte + ncam * 8);
    ComputeJtEC_(ncam, ee, jte, camera, point, ms, jmap, cmap, cmlist,
                 intrinsic_fixed, radial_distortion, mt);
  }
  if (mode != 1) {
    ComputeJtEP(npt, ee, jp, pmap, jte + 8 * ncam, mt);
  }
}

template <class Float>
void ComputeJtE_(size_t nproj, size_t ncam, size_t npt, const Float* ee,
                 Float* jte, const Float* camera, const Float* point,
                 const Float* ms, const int* jmap, bool intrinsic_fixed,
                 int radial_distortion, int mode) {
  SetVectorZero(jte, jte + (ncam * 8 + npt * POINT_ALIGN));
  Float jcv[24 + 8];  // size_t offset = ((size_t) jcv) & 0xf;
  // Float* jc = jcv + (16 - offset) / sizeof(Float), *pj = jc + 16;
  Float *jc = (Float *)ALIGN_PTR(jcv), *pj = jc + 16;

  Float *vc0 = jte, *vp0 = jte + ncam * 8;

  for (size_t i = 0; i < nproj; ++i, jmap += 2, ms += 2, ee += 2) {
    int cidx = jmap[0], pidx = jmap[1];
    const Float *c = camera + cidx * 16, *pt = point + pidx * POINT_ALIGN;

    if (mode == 0) {
      /////////////////////////////////////////////////////
      JacobianOne(c, pt, ms, jc, jc + 8, pj, pj + POINT_ALIGN, intrinsic_fixed,
                  radial_distortion);

      ////////////////////////////////////////////
      Float *vc = vc0 + cidx * 8, *vp = vp0 + pidx * POINT_ALIGN;
      AddScaledVec8(ee[0], jc, vc);
      AddScaledVec8(ee[1], jc + 8, vc);
      vp[0] += (ee[0] * pj[0] + ee[1] * pj[POINT_ALIGN]);
      vp[1] += (ee[0] * pj[1] + ee[1] * pj[POINT_ALIGN + 1]);
      vp[2] += (ee[0] * pj[2] + ee[1] * pj[POINT_ALIGN + 2]);
    } else if (mode == 1) {
      /////////////////////////////////////////////////////
      JacobianOne(c, pt, ms, jc, jc + 8, (Float*)NULL, (Float*)NULL,
                  intrinsic_fixed, radial_distortion);

      ////////////////////////////////////////////
      Float* vc = vc0 + cidx * 8;
      AddScaledVec8(ee[0], jc, vc);
      AddScaledVec8(ee[1], jc + 8, vc);
    } else {
      /////////////////////////////////////////////////////
      JacobianOne(c, pt, ms, (Float*)NULL, (Float*)NULL, pj, pj + POINT_ALIGN,
                  intrinsic_fixed, radial_distortion);

      ////////////////////////////////////////////
      Float* vp = vp0 + pidx * POINT_ALIGN;
      vp[0] += (ee[0] * pj[0] + ee[1] * pj[POINT_ALIGN]);
      vp[1] += (ee[0] * pj[1] + ee[1] * pj[POINT_ALIGN + 1]);
      vp[2] += (ee[0] * pj[2] + ee[1] * pj[POINT_ALIGN + 2]);
    }
  }
}
};

using namespace ProgramCPU;

template <class Float>
SparseBundleCPU<Float>::SparseBundleCPU(const int num_threads)
    : ParallelBA(PBA_INVALID_DEVICE),
      _num_camera(0),
      _num_point(0),
      _num_imgpt(0),
      _num_imgpt_q(0),
      _camera_data(NULL),
      _point_data(NULL),
      _imgpt_data(NULL),
      _camera_idx(NULL),
      _point_idx(NULL),
      _projection_sse(0) {
  __cpu_data_precision = sizeof(Float);
  if (num_threads <= 0) {
    __num_cpu_cores = FindProcessorCoreNum();
  } else {
    __num_cpu_cores = num_threads;
  }
  if (__verbose_level)
    std::cout << "CPU " << (__cpu_data_precision == 4 ? "single" : "double")
              << "-precision solver; " << __num_cpu_cores << " cores"
#ifdef CPUPBA_USE_AVX
              << " (AVX)"
#endif
              << ".\n";
  // the following configuration are totally based my personal experience
  // on two computers.. you should adjust them according to your system.
  // try run driver filename -profile --float to see how speed varies
  ////////////////////////////////////////
  __num_cpu_thread[FUNC_JX] = __num_cpu_cores;
  __num_cpu_thread[FUNC_JX_] = __num_cpu_cores;
  __num_cpu_thread[FUNC_JTE_] = __num_cpu_cores;
  __num_cpu_thread[FUNC_JJ_JCO_JCT_JP] = __num_cpu_cores;
  __num_cpu_thread[FUNC_JJ_JCO_JP] = __num_cpu_cores;
  __num_cpu_thread[FUNC_JJ_JCT_JP] = __num_cpu_cores;
  __num_cpu_thread[FUNC_JJ_JP] = __num_cpu_cores;
  __num_cpu_thread[FUNC_PJ] = __num_cpu_cores;
  __num_cpu_thread[FUNC_BCC_JCO] = __num_cpu_cores;
  __num_cpu_thread[FUNC_BCC_JCT] = __num_cpu_cores;
  __num_cpu_thread[FUNC_BCP] = __num_cpu_cores;

  ////this behavious is different between CPU and GPU
  __multiply_jx_usenoj = false;

  ///////////////////////////////////////////////////////////////////////////////
  // To get the best performance, you should ajust the number of threads
  // Linux and Windows may also have different thread launching overhead.

  //////////////////////////////////////////////////////////////
  __num_cpu_thread[FUNC_JTEC_JCT] = __num_cpu_cores * 2;
  __num_cpu_thread[FUNC_JTEC_JCO] = __num_cpu_cores * 2;
  __num_cpu_thread[FUNC_JTEP] = __num_cpu_cores;

  ///////////
  __num_cpu_thread[FUNC_MPC] =
      1;  // single thread always faster with my experience

  // see the AUTO_MT_NUM marcro for definition
  __num_cpu_thread[FUNC_MPP] = 0;  // automatically chosen according to size
  __num_cpu_thread[FUNC_VS] = 0;  // automatically chosen according to size
  __num_cpu_thread[FUNC_VV] = 0;  // automatically chosen accodring to size
}

template <class Float>
void SparseBundleCPU<Float>::SetCameraData(size_t ncam, CameraT* cams) {
  if (sizeof(CameraT) != 16 * sizeof(float)) return;  // never gonna happen...?
  _num_camera = (int)ncam;
  _camera_data = cams;
  _focal_mask = NULL;
}

template <class Float>
void SparseBundleCPU<Float>::SetFocalMask(const int* fmask, float weight) {
  _focal_mask = fmask;
  _weight_q = weight;
}

template <class Float>
void SparseBundleCPU<Float>::SetPointData(size_t npoint, Point3D* pts) {
  _num_point = (int)npoint;
  _point_data = (float*)pts;
}

template <class Float>
void SparseBundleCPU<Float>::SetProjection(size_t nproj, const Point2D* imgpts,
                                           const int* point_idx,
                                           const int* cam_idx) {
  _num_imgpt = (int)nproj;
  _imgpt_data = (float*)imgpts;
  _camera_idx = cam_idx;
  _point_idx = point_idx;
}

template <class Float>
float SparseBundleCPU<Float>::GetMeanSquaredError() {
  return float(_projection_sse /
               (_num_imgpt * __focal_scaling * __focal_scaling));
}

template <class Float>
int SparseBundleCPU<Float>::RunBundleAdjustment() {
  ResetBundleStatistics();
  BundleAdjustment();
  if (__num_lm_success > 0)
    SaveBundleStatistics(_num_camera, _num_point, _num_imgpt);
  if (__num_lm_success > 0) PrintBundleStatistics();
  ResetTemporarySetting();
  return __num_lm_success;
}

template <class Float>
int SparseBundleCPU<Float>::ValidateInputData() {
  if (_camera_data == NULL) return STATUS_CAMERA_MISSING;
  if (_point_data == NULL) return STATUS_POINT_MISSING;
  if (_imgpt_data == NULL) return STATUS_MEASURMENT_MISSING;
  if (_camera_idx == NULL || _point_idx == NULL)
    return STATUS_PROJECTION_MISSING;
  return STATUS_SUCCESS;
}

template <class Float>
int SparseBundleCPU<Float>::InitializeBundle() {
  /////////////////////////////////////////////////////
  TimerBA timer(this, TIMER_GPU_ALLOCATION);
  InitializeStorageForSFM();
  InitializeStorageForCG();

  if (__debug_pba) DumpCooJacobian();

  return STATUS_SUCCESS;
}

template <class Float>
int SparseBundleCPU<Float>::GetParameterLength() {
  return _num_camera * 8 + POINT_ALIGN * _num_point;
}

template <class Float>
void SparseBundleCPU<Float>::BundleAdjustment() {
  if (ValidateInputData() != STATUS_SUCCESS) return;

  ////////////////////////
  TimerBA timer(this, TIMER_OVERALL);

  NormalizeData();
  if (InitializeBundle() != STATUS_SUCCESS) {
    // failed to allocate gpu storage
  } else if (__profile_pba) {
    // profiling some stuff
    RunProfileSteps();
  } else {
    // real optimization
    AdjustBundleAdjsutmentMode();
    NonlinearOptimizeLM();
    TransferDataToHost();
  }
  DenormalizeData();
}

template <class Float>
void SparseBundleCPU<Float>::NormalizeData() {
  TimerBA timer(this, TIMER_PREPROCESSING);
  NormalizeDataD();
  NormalizeDataF();
}

template <class Float>
void SparseBundleCPU<Float>::TransferDataToHost() {
  TimerBA timer(this, TIMER_GPU_DOWNLOAD);
  std::copy(_cuCameraData.begin(), _cuCameraData.end(), ((float*)_camera_data));
#ifdef POINT_DATA_ALIGN4
  std::copy(_cuPointData.begin(), _cuPointData.end(), _point_data);
#else
  for (size_t i = 0, j = 0; i < _cuPointData.size(); j++) {
    _point_data[j++] = (float)_cuPointData[i++];
    _point_data[j++] = (float)_cuPointData[i++];
    _point_data[j++] = (float)_cuPointData[i++];
  }
#endif
}

#define ALLOCATE_REQUIRED_DATA(NAME, num, channels) \
  {                                                 \
    NAME.resize((num) * (channels));                \
    total_sz += NAME.size() * sizeof(Float);        \
  }
#define ALLOCATE_OPTIONAL_DATA(NAME, num, channels, option)      \
  if (option) ALLOCATE_REQUIRED_DATA(NAME, num, channels) else { \
      NAME.resize(0);                                            \
    }
//////////////////////////////////////////////
template <class Float>
bool SparseBundleCPU<Float>::InitializeStorageForSFM() {
  size_t total_sz = 0;
  //////////////////////////////////////////////////
  ProcessIndexCameraQ(_cuCameraQMap, _cuCameraQList);
  total_sz += ((_cuCameraQMap.size() + _cuCameraQList.size()) * sizeof(int) /
               1024 / 1024);

  ///////////////////////////////////////////////////////////////////
  ALLOCATE_REQUIRED_DATA(_cuPointData, _num_point, POINT_ALIGN);  // 4n
  ALLOCATE_REQUIRED_DATA(_cuCameraData, _num_camera, 16);  // 16m
  ALLOCATE_REQUIRED_DATA(_cuCameraDataEX, _num_camera, 16);  // 16m

  ////////////////////////////////////////////////////////////////
  ALLOCATE_REQUIRED_DATA(_cuCameraMeasurementMap, _num_camera + 1, 1);  // m
  ALLOCATE_REQUIRED_DATA(_cuCameraMeasurementList, _num_imgpt, 1);  // k
  ALLOCATE_REQUIRED_DATA(_cuPointMeasurementMap, _num_point + 1, 1);  // n
  ALLOCATE_REQUIRED_DATA(_cuProjectionMap, _num_imgpt, 2);  // 2k
  ALLOCATE_REQUIRED_DATA(_cuImageProj, _num_imgpt + _num_imgpt_q, 2);  // 2k
  ALLOCATE_REQUIRED_DATA(_cuPointDataEX, _num_point, POINT_ALIGN);  // 4n
  ALLOCATE_REQUIRED_DATA(_cuMeasurements, _num_imgpt, 2);  // 2k
  ALLOCATE_REQUIRED_DATA(_cuCameraQMapW, _num_imgpt_q, 2);
  ALLOCATE_REQUIRED_DATA(_cuCameraQListW, (_num_imgpt_q > 0 ? _num_camera : 0),
                         2);

  ALLOCATE_OPTIONAL_DATA(_cuJacobianPoint, _num_imgpt * 2, POINT_ALIGN,
                         !__no_jacobian_store);  // 8k
  ALLOCATE_OPTIONAL_DATA(_cuJacobianCameraT, _num_imgpt * 2, 8,
                         !__no_jacobian_store && __jc_store_transpose);  // 16k
  ALLOCATE_OPTIONAL_DATA(_cuJacobianCamera, _num_imgpt * 2, 8,
                         !__no_jacobian_store && __jc_store_original);  // 16k
  ALLOCATE_OPTIONAL_DATA(_cuCameraMeasurementListT, _num_imgpt, 1,
                         __jc_store_transpose);  // k

  //////////////////////////////////////////
  BundleTimerSwap(TIMER_PREPROCESSING, TIMER_GPU_ALLOCATION);
  ////mapping from camera to measuremnts
  vector<int>& cpi = _cuCameraMeasurementMap;
  cpi.resize(_num_camera + 1);
  vector<int>& cpidx = _cuCameraMeasurementList;
  cpidx.resize(_num_imgpt);
  vector<int> cpnum(_num_camera, 0);
  cpi[0] = 0;
  for (int i = 0; i < _num_imgpt; ++i) cpnum[_camera_idx[i]]++;
  for (int i = 1; i <= _num_camera; ++i) cpi[i] = cpi[i - 1] + cpnum[i - 1];
  ///////////////////////////////////////////////////////
  vector<int> cptidx = cpi;
  for (int i = 0; i < _num_imgpt; ++i) cpidx[cptidx[_camera_idx[i]]++] = i;

  ///////////////////////////////////////////////////////////
  if (_cuCameraMeasurementListT.size()) {
    vector<int>& ridx = _cuCameraMeasurementListT;
    ridx.resize(_num_imgpt);
    for (int i = 0; i < _num_imgpt; ++i) ridx[cpidx[i]] = i;
  }

  ////////////////////////////////////////
  /////constaraint weights.
  if (_num_imgpt_q > 0)
    ProcessWeightCameraQ(cpnum, _cuCameraQMap, _cuCameraQMapW.begin(),
                         _cuCameraQListW.begin());

  ///////////////////////////////////////////////////////////////////////////////
  std::copy((float*)_camera_data, ((float*)_camera_data) + _cuCameraData.size(),
            _cuCameraData.begin());

#ifdef POINT_DATA_ALIGN4
  std::copy(_point_data, _point_data + _cuPointData.size(),
            _cuPointData.begin());
#else
  for (size_t i = 0, j = 0; i < _cuPointData.size(); j++) {
    _cuPointData[i++] = _point_data[j++];
    _cuPointData[i++] = _point_data[j++];
    _cuPointData[i++] = _point_data[j++];
  }
#endif

  ////////////////////////////////////////////
  ///////mapping from point to measurment
  vector<int>& ppi = _cuPointMeasurementMap;
  ppi.resize(_num_point + 1);
  for (int i = 0, last_point = -1; i < _num_imgpt; ++i) {
    int pt = _point_idx[i];
    while (last_point < pt) ppi[++last_point] = i;
  }
  ppi[_num_point] = _num_imgpt;

  //////////projection map
  vector<int>& pmp = _cuProjectionMap;
  pmp.resize(_num_imgpt * 2);
  for (int i = 0; i < _num_imgpt; ++i) {
    int* imp = &pmp[i * 2];
    imp[0] = _camera_idx[i];
    imp[1] = _point_idx[i];
  }
  BundleTimerSwap(TIMER_PREPROCESSING, TIMER_GPU_ALLOCATION);
  //////////////////////////////////////////////////////////////

  __memory_usage = total_sz;
  if (__verbose_level > 1)
    std::cout << "Memory for Motion/Structure/Jacobian:\t"
              << (total_sz / 1024 / 1024) << "MB\n";

  return true;
}

template <class Float>
bool SparseBundleCPU<Float>::ProcessIndexCameraQ(vector<int>& qmap,
                                                 vector<int>& qlist) {
  ///////////////////////////////////
  qlist.resize(0);
  qmap.resize(0);
  _num_imgpt_q = 0;

  if (_camera_idx == NULL) return true;
  if (_point_idx == NULL) return true;
  if (_focal_mask == NULL) return true;
  if (_num_camera == 0) return true;
  if (_weight_q <= 0) return true;

  ///////////////////////////////////////

  int error = 0;
  vector<int> temp(_num_camera * 2, -1);

  for (int i = 0; i < _num_camera; ++i) {
    int iq = _focal_mask[i];
    if (iq > i) {
      error = 1;
      break;
    }
    if (iq < 0) continue;
    if (iq == i) continue;
    int ip = temp[2 * iq];
    // float ratio = _camera_data[i].f / _camera_data[iq].f;
    // if(ratio < 0.01 || ratio > 100)
    //{
    //  std::cout << "Warning: constaraints on largely different camreas\n";
    //  continue;
    //}else
    if (_focal_mask[iq] != iq) {
      error = 1;
      break;
    } else if (ip == -1) {
      temp[2 * iq] = i;
      temp[2 * iq + 1] = i;
      temp[2 * i] = iq;
      temp[2 * i + 1] = iq;
    } else {
      // maintain double-linked list
      temp[2 * i] = ip;
      temp[2 * i + 1] = iq;
      temp[2 * ip + 1] = i;
      temp[2 * iq] = i;
    }
  }

  if (error) {
    std::cout << "Error: incorrect constraints\n";
    _focal_mask = NULL;
    return false;
  }

  ////////////////////////////////////////
  qlist.resize(_num_camera * 2, -1);
  for (int i = 0; i < _num_camera; ++i) {
    int inext = temp[2 * i + 1];
    if (inext == -1) continue;
    qlist[2 * i] = _num_imgpt_q;
    qlist[2 * inext + 1] = _num_imgpt_q;
    qmap.push_back(i);
    qmap.push_back(inext);
    _num_imgpt_q++;
  }
  return true;
}

template <class Float>
void SparseBundleCPU<Float>::ProcessWeightCameraQ(vector<int>& cpnum,
                                                  vector<int>& qmap,
                                                  Float* qmapw, Float* qlistw) {
  // set average focal length and average radial distortion
  vector<Float> qpnum(_num_camera, 0), qcnum(_num_camera, 0);
  vector<Float> fs(_num_camera, 0), rs(_num_camera, 0);

  for (int i = 0; i < _num_camera; ++i) {
    int qi = _focal_mask[i];
    if (qi == -1) continue;
    // float ratio = _camera_data[i].f / _camera_data[qi].f;
    // if(ratio < 0.01 || ratio > 100)      continue;
    fs[qi] += _camera_data[i].f;
    rs[qi] += _camera_data[i].radial;
    qpnum[qi] += cpnum[i];
    qcnum[qi] += 1.0f;
  }

  // this seems not really matter..they will converge anyway
  for (int i = 0; i < _num_camera; ++i) {
    int qi = _focal_mask[i];
    if (qi == -1) continue;
    // float ratio = _camera_data[i].f / _camera_data[qi].f;
    // if(ratio < 0.01 || ratio > 100)      continue;
    _camera_data[i].f = fs[qi] / qcnum[qi];
    _camera_data[i].radial = rs[qi] / qcnum[qi];
  } /**/

  /////////////////////////////////////////
  std::fill(qlistw, qlistw + _num_camera * 2, 0);

  for (int i = 0; i < _num_imgpt_q; ++i) {
    int cidx = qmap[i * 2], qi = _focal_mask[cidx];
    Float wi = sqrt(qpnum[qi] / qcnum[qi]) * _weight_q;
    Float wr = (__use_radial_distortion ? wi * _camera_data[qi].f : 0.0);
    qmapw[i * 2] = wi;
    qmapw[i * 2 + 1] = wr;
    qlistw[cidx * 2] = wi;
    qlistw[cidx * 2 + 1] = wr;
  }
}

/////////////////////////////////////////////////
template <class Float>
bool SparseBundleCPU<Float>::InitializeStorageForCG() {
  size_t total_sz = 0;
  int plen = GetParameterLength();  // q = 8m + 3n

  //////////////////////////////////////////// 6q
  ALLOCATE_REQUIRED_DATA(_cuVectorJtE, plen, 1);
  ALLOCATE_REQUIRED_DATA(_cuVectorXK, plen, 1);
  ALLOCATE_REQUIRED_DATA(_cuVectorJJ, plen, 1);
  ALLOCATE_REQUIRED_DATA(_cuVectorZK, plen, 1);
  ALLOCATE_REQUIRED_DATA(_cuVectorPK, plen, 1);
  ALLOCATE_REQUIRED_DATA(_cuVectorRK, plen, 1);

  ///////////////////////////////////////////
  unsigned int cblock_len = (__use_radial_distortion ? 64 : 56);
  ALLOCATE_REQUIRED_DATA(_cuBlockPC, _num_camera * cblock_len + 6 * _num_point,
                         1);  // 64m + 12n
  ALLOCATE_REQUIRED_DATA(_cuVectorJX, _num_imgpt + _num_imgpt_q, 2);  // 2k
  ALLOCATE_OPTIONAL_DATA(_cuVectorSJ, plen, 1, __jacobian_normalize);

  /////////////////////////////////////////
  __memory_usage += total_sz;
  if (__verbose_level > 1)
    std::cout << "Memory for Conjugate Gradient Solver:\t"
              << (total_sz / 1024 / 1024) << "MB\n";
  return true;
}

///////////////////////////////////////////////////
template <class Float>
void SparseBundleCPU<Float>::PrepareJacobianNormalization() {
  if (!_cuVectorSJ.size()) return;

  if ((__jc_store_transpose || __jc_store_original) &&
      _cuJacobianPoint.size() && !__bundle_current_mode) {
    VectorF null;
    null.swap(_cuVectorSJ);
    EvaluateJacobians();
    null.swap(_cuVectorSJ);
    ComputeDiagonal(_cuVectorSJ);
    ComputeSQRT(_cuVectorSJ);
  } else {
    VectorF null;
    null.swap(_cuVectorSJ);
    EvaluateJacobians();
    ComputeBlockPC(0, true);
    null.swap(_cuVectorSJ);
    _cuVectorJJ.swap(_cuVectorSJ);
    ComputeRSQRT(_cuVectorSJ);
  }
}

template <class Float>
void SparseBundleCPU<Float>::EvaluateJacobians() {
  if (__no_jacobian_store) return;
  if (__bundle_current_mode == BUNDLE_ONLY_MOTION && !__jc_store_original &&
      !__jc_store_transpose)
    return;

  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_JJ, true);

  if (__jc_store_original || !__jc_store_transpose) {
    int fid = __jc_store_original
                  ? (__jc_store_transpose ? FUNC_JJ_JCO_JCT_JP : FUNC_JJ_JCO_JP)
                  : FUNC_JJ_JP;
    ComputeJacobian(
        _num_imgpt, _num_camera, _cuCameraData.begin(), _cuPointData.begin(),
        _cuJacobianCamera.begin(), _cuJacobianPoint.begin(),
        &_cuProjectionMap.front(), _cuVectorSJ.begin(), _cuMeasurements.begin(),
        __jc_store_transpose ? &_cuCameraMeasurementListT.front() : NULL,
        __fixed_intrinsics, __use_radial_distortion, false,
        _cuJacobianCameraT.begin(), __num_cpu_thread[fid]);
  } else {
    ComputeJacobian(_num_imgpt, _num_camera, _cuCameraData.begin(),
                    _cuPointData.begin(), _cuJacobianCameraT.begin(),
                    _cuJacobianPoint.begin(), &_cuProjectionMap.front(),
                    _cuVectorSJ.begin(), _cuMeasurements.begin(),
                    &_cuCameraMeasurementListT.front(), __fixed_intrinsics,
                    __use_radial_distortion, true, ((Float*)0),
                    __num_cpu_thread[FUNC_JJ_JCT_JP]);
  }
  ++__num_jacobian_eval;
}

template <class Float>
void SparseBundleCPU<Float>::ComputeJtE(VectorF& E, VectorF& JtE, int mode) {
  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_JTE, true);
  if (mode == 0) mode = __bundle_current_mode;

  if (__no_jacobian_store || (!__jc_store_original && !__jc_store_transpose)) {
    if (_cuJacobianPoint.size()) {
      ProgramCPU::ComputeJtE_(
          _num_imgpt, _num_camera, _num_point, E.begin(), JtE.begin(),
          _cuCameraData.begin(), _cuPointData.begin(), _cuMeasurements.begin(),
          &_cuProjectionMap.front(), &_cuCameraMeasurementMap.front(),
          &_cuCameraMeasurementList.front(), &_cuPointMeasurementMap.front(),
          _cuJacobianPoint.begin(), __fixed_intrinsics, __use_radial_distortion,
          mode, __num_cpu_thread[FUNC_JTE_]);

      if (_cuVectorSJ.size() && mode != 2)
        ProgramCPU::ComputeVXY(JtE, _cuVectorSJ, JtE, _num_camera * 8);
    } else {
      ProgramCPU::ComputeJtE_(_num_imgpt, _num_camera, _num_point, E.begin(),
                              JtE.begin(), _cuCameraData.begin(),
                              _cuPointData.begin(), _cuMeasurements.begin(),
                              &_cuProjectionMap.front(), __fixed_intrinsics,
                              __use_radial_distortion, mode);

      //////////////////////////////////////////////////////////
      // if(_cuVectorSJ.size())  ProgramCPU::ComputeVXY(JtE, _cuVectorSJ, JtE);
      if (!_cuVectorSJ.size()) {
      } else if (mode == 2)
        ComputeVXY(JtE, _cuVectorSJ, JtE, _num_point * POINT_ALIGN,
                   _num_camera * 8);
      else if (mode == 1)
        ComputeVXY(JtE, _cuVectorSJ, JtE, _num_camera * 8);
      else
        ComputeVXY(JtE, _cuVectorSJ, JtE);
    }
  } else if (__jc_store_transpose) {
    ProgramCPU::ComputeJtE(
        _num_camera, _num_point, E.begin(), _cuJacobianCameraT.begin(),
        &_cuCameraMeasurementMap.front(), &_cuCameraMeasurementList.front(),
        _cuJacobianPoint.begin(), &_cuPointMeasurementMap.front(), JtE.begin(),
        true, mode, __num_cpu_thread[FUNC_JTEC_JCT],
        __num_cpu_thread[FUNC_JTEP]);
  } else {
    ProgramCPU::ComputeJtE(
        _num_camera, _num_point, E.begin(), _cuJacobianCamera.begin(),
        &_cuCameraMeasurementMap.front(), &_cuCameraMeasurementList.front(),
        _cuJacobianPoint.begin(), &_cuPointMeasurementMap.front(), JtE.begin(),
        false, mode, __num_cpu_thread[FUNC_JTEC_JCO],
        __num_cpu_thread[FUNC_JTEP]);
  }

  if (mode != 2 && _num_imgpt_q > 0) {
    ProgramCPU::ComputeJQtEC(_num_camera, E.begin() + 2 * _num_imgpt,
                             &_cuCameraQList.front(), _cuCameraQListW.begin(),
                             _cuVectorSJ.begin(), JtE.begin());
  }
}

template <class Float>
void SparseBundleCPU<Float>::SaveBundleRecord(int iter, float res,
                                              float damping, float& g_norm,
                                              float& g_inf) {
  // do not really compute if parameter not specified...
  // for large dataset, it never converges..
  g_inf = __lm_check_gradient ? float(ComputeVectorMax(_cuVectorJtE)) : 0;
  g_norm =
      __save_gradient_norm ? float(ComputeVectorNorm(_cuVectorJtE)) : g_inf;
  ConfigBA::SaveBundleRecord(iter, res, damping, g_norm, g_inf);
}

template <class Float>
float SparseBundleCPU<Float>::EvaluateProjection(VectorF& cam, VectorF& point,
                                                 VectorF& proj) {
  ++__num_projection_eval;
  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_PJ, true);
  ComputeProjection(_num_imgpt, cam.begin(), point.begin(),
                    _cuMeasurements.begin(), &_cuProjectionMap.front(),
                    proj.begin(), __use_radial_distortion,
                    __num_cpu_thread[FUNC_PJ]);
  if (_num_imgpt_q > 0)
    ComputeProjectionQ(_num_imgpt_q, cam.begin(), &_cuCameraQMap.front(),
                       _cuCameraQMapW.begin(), proj.begin() + 2 * _num_imgpt);
  return (float)ComputeVectorNorm(proj, __num_cpu_thread[FUNC_VS]);
}

template <class Float>
float SparseBundleCPU<Float>::EvaluateProjectionX(VectorF& cam, VectorF& point,
                                                  VectorF& proj) {
  ++__num_projection_eval;
  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_PJ, true);
  ComputeProjectionX(_num_imgpt, cam.begin(), point.begin(),
                     _cuMeasurements.begin(), &_cuProjectionMap.front(),
                     proj.begin(), __use_radial_distortion,
                     __num_cpu_thread[FUNC_PJ]);
  if (_num_imgpt_q > 0)
    ComputeProjectionQ(_num_imgpt_q, cam.begin(), &_cuCameraQMap.front(),
                       _cuCameraQMapW.begin(), proj.begin() + 2 * _num_imgpt);
  return (float)ComputeVectorNorm(proj, __num_cpu_thread[FUNC_VS]);
}

template <class Float>
void SparseBundleCPU<Float>::ComputeJX(VectorF& X, VectorF& JX, int mode) {
  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_JX, true);
  if (__no_jacobian_store || (__multiply_jx_usenoj && mode != 2) ||
      !__jc_store_original) {
    ProgramCPU::ComputeJX_(
        _num_imgpt, _num_camera, X.begin(), JX.begin(), _cuCameraData.begin(),
        _cuPointData.begin(), _cuMeasurements.begin(), _cuVectorSJ.begin(),
        &_cuProjectionMap.front(), __fixed_intrinsics, __use_radial_distortion,
        mode, __num_cpu_thread[FUNC_JX_]);
  } else {
    ProgramCPU::ComputeJX(_num_imgpt, _num_camera, X.begin(),
                          _cuJacobianCamera.begin(), _cuJacobianPoint.begin(),
                          &_cuProjectionMap.front(), JX.begin(), mode,
                          __num_cpu_thread[FUNC_JX]);
  }

  if (_num_imgpt_q > 0 && mode != 2) {
    ProgramCPU::ComputeJQX(_num_imgpt_q, X.begin(), &_cuCameraQMap.front(),
                           _cuCameraQMapW.begin(), _cuVectorSJ.begin(),
                           JX.begin() + 2 * _num_imgpt);
  }
}

template <class Float>
void SparseBundleCPU<Float>::ComputeBlockPC(float lambda, bool dampd) {
  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_BC, true);

  if (__no_jacobian_store || (!__jc_store_original && !__jc_store_transpose &&
                              __bundle_current_mode != 2)) {
    ComputeDiagonalBlock_(
        lambda, dampd, _cuCameraData, _cuPointData, _cuMeasurements,
        _cuProjectionMap, _cuVectorSJ, _cuCameraQListW, _cuVectorJJ, _cuBlockPC,
        __fixed_intrinsics, __use_radial_distortion, __bundle_current_mode);
  } else if (__jc_store_transpose) {
    ComputeDiagonalBlock(
        _num_camera, _num_point, lambda, dampd, _cuJacobianCameraT.begin(),
        &_cuCameraMeasurementMap.front(), _cuJacobianPoint.begin(),
        &_cuPointMeasurementMap.front(), &_cuCameraMeasurementList.front(),
        _cuVectorSJ.begin(), _cuCameraQListW.begin(), _cuVectorJJ.begin(),
        _cuBlockPC.begin(), __use_radial_distortion, true,
        __num_cpu_thread[FUNC_BCC_JCT], __num_cpu_thread[FUNC_BCP],
        __bundle_current_mode);
  } else {
    ComputeDiagonalBlock(
        _num_camera, _num_point, lambda, dampd, _cuJacobianCamera.begin(),
        &_cuCameraMeasurementMap.front(), _cuJacobianPoint.begin(),
        &_cuPointMeasurementMap.front(), &_cuCameraMeasurementList.front(),
        _cuVectorSJ.begin(), _cuCameraQListW.begin(), _cuVectorJJ.begin(),
        _cuBlockPC.begin(), __use_radial_distortion, false,
        __num_cpu_thread[FUNC_BCC_JCO], __num_cpu_thread[FUNC_BCP],
        __bundle_current_mode);
  }
}

template <class Float>
void SparseBundleCPU<Float>::ApplyBlockPC(VectorF& v, VectorF& pv, int mode) {
  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_MP, true);
  MultiplyBlockConditioner(_num_camera, _num_point, _cuBlockPC.begin(),
                           v.begin(), pv.begin(), __use_radial_distortion, mode,
                           __num_cpu_thread[FUNC_MPC],
                           __num_cpu_thread[FUNC_MPP]);
}

template <class Float>
void SparseBundleCPU<Float>::ComputeDiagonal(VectorF& JJ) {
  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_DD, true);
  if (__no_jacobian_store) {
  } else if (__jc_store_transpose) {
    ProgramCPU::ComputeDiagonal(
        _cuJacobianCameraT, _cuCameraMeasurementMap, _cuJacobianPoint,
        _cuPointMeasurementMap, _cuCameraMeasurementList,
        _cuCameraQListW.begin(), JJ, true, __use_radial_distortion);
  } else if (__jc_store_original) {
    ProgramCPU::ComputeDiagonal(
        _cuJacobianCamera, _cuCameraMeasurementMap, _cuJacobianPoint,
        _cuPointMeasurementMap, _cuCameraMeasurementList,
        _cuCameraQListW.begin(), JJ, false, __use_radial_distortion);
  }
}

template <class Float>
void SparseBundleCPU<Float>::NormalizeDataF() {
  int incompatible_radial_distortion = 0;
  _cuMeasurements.resize(_num_imgpt * 2);
  if (__focal_normalize) {
    if (__focal_scaling == 1.0f) {
      //------------------------------------------------------------------
      //////////////////////////////////////////////////////////////
      vector<float> focals(_num_camera);
      for (int i = 0; i < _num_camera; ++i) focals[i] = _camera_data[i].f;
      std::nth_element(focals.begin(), focals.begin() + _num_camera / 2,
                       focals.end());
      float median_focal_length = focals[_num_camera / 2];
      __focal_scaling = __data_normalize_median / median_focal_length;
      Float radial_factor = median_focal_length * median_focal_length * 4.0f;

      ///////////////////////////////

      for (int i = 0; i < _num_imgpt * 2; ++i) {
        _cuMeasurements[i] = Float(_imgpt_data[i] * __focal_scaling);
      }
      for (int i = 0; i < _num_camera; ++i) {
        _camera_data[i].f *= __focal_scaling;
        if (!__use_radial_distortion) {
        } else if (__reset_initial_distortion) {
          _camera_data[i].radial = 0;
        } else if (_camera_data[i].distortion_type != __use_radial_distortion) {
          incompatible_radial_distortion++;
          _camera_data[i].radial = 0;
        } else if (__use_radial_distortion == -1) {
          _camera_data[i].radial *= radial_factor;
        }
      }
      if (__verbose_level > 2)
        std::cout << "Focal length normalized by " << __focal_scaling << '\n';
      __reset_initial_distortion = false;
    }
  } else {
    if (__use_radial_distortion) {
      for (int i = 0; i < _num_camera; ++i) {
        if (__reset_initial_distortion) {
          _camera_data[i].radial = 0;
        } else if (_camera_data[i].distortion_type != __use_radial_distortion) {
          _camera_data[i].radial = 0;
          incompatible_radial_distortion++;
        }
      }
      __reset_initial_distortion = false;
    }
    std::copy(_imgpt_data, _imgpt_data + _cuMeasurements.size(),
              _cuMeasurements.begin());
  }

  if (incompatible_radial_distortion) {
    std::cout << "ERROR: incompatible radial distortion input; reset to 0;\n";
  }
}

template <class Float>
void SparseBundleCPU<Float>::NormalizeDataD() {
  if (__depth_scaling == 1.0f) {
    const float dist_bound = 1.0f;
    vector<float> oz(_num_imgpt);
    vector<float> cpdist1(_num_camera, dist_bound);
    vector<float> cpdist2(_num_camera, -dist_bound);
    vector<int> camnpj(_num_camera, 0), cambpj(_num_camera, 0);
    int bad_point_count = 0;
    for (int i = 0; i < _num_imgpt; ++i) {
      int cmidx = _camera_idx[i];
      CameraT* cam = _camera_data + cmidx;
      float* rz = cam->m[2];
      float* x = _point_data + 4 * _point_idx[i];
      oz[i] = (rz[0] * x[0] + rz[1] * x[1] + rz[2] * x[2] + cam->t[2]);

      /////////////////////////////////////////////////
      // points behind camera may causes big problem
      float ozr = oz[i] / cam->t[2];
      if (fabs(ozr) < __depth_check_epsilon) {
        bad_point_count++;
        float px = cam->f * (cam->m[0][0] * x[0] + cam->m[0][1] * x[1] +
                             cam->m[0][2] * x[2] + cam->t[0]);
        float py = cam->f * (cam->m[1][0] * x[0] + cam->m[1][1] * x[1] +
                             cam->m[1][2] * x[2] + cam->t[1]);
        float mx = _imgpt_data[i * 2], my = _imgpt_data[2 * i + 1];
        bool checkx = fabs(mx) > fabs(my);
        if ((checkx && px * oz[i] * mx < 0 && fabs(mx) > 64) ||
            (!checkx && py * oz[i] * my < 0 && fabs(my) > 64)) {
          if (__verbose_level > 3)
            std::cout << "Warning: proj of #" << cmidx
                      << " on the wrong side, oz = " << oz[i] << " ("
                      << (px / oz[i]) << ',' << (py / oz[i]) << ") (" << mx
                      << ',' << my << ")\n";
          /////////////////////////////////////////////////////////////////////////
          if (oz[i] > 0)
            cpdist2[cmidx] = 0;
          else
            cpdist1[cmidx] = 0;
        }
        if (oz[i] >= 0)
          cpdist1[cmidx] = std::min(cpdist1[cmidx], oz[i]);
        else
          cpdist2[cmidx] = std::max(cpdist2[cmidx], oz[i]);
      }
      if (oz[i] < 0) {
        __num_point_behind++;
        cambpj[cmidx]++;
      }
      camnpj[cmidx]++;
    }
    if (bad_point_count > 0 && __depth_degeneracy_fix) {
      if (!__focal_normalize || !__depth_normalize)
        std::cout << "Enable data normalization on degeneracy\n";
      __focal_normalize = true;
      __depth_normalize = true;
    }
    if (__depth_normalize) {
      std::nth_element(oz.begin(), oz.begin() + _num_imgpt / 2, oz.end());
      float oz_median = oz[_num_imgpt / 2];
      float shift_min = std::min(oz_median * 0.001f, 1.0f);
      float dist_threshold = shift_min * 0.1f;
      __depth_scaling = (1.0 / oz_median) / __data_normalize_median;
      if (__verbose_level > 2)
        std::cout << "Depth normalized by " << __depth_scaling << " ("
                  << oz_median << ")\n";

      for (int i = 0; i < _num_camera; ++i) {
        // move the camera a little bit?
        if (!__depth_degeneracy_fix) {
        } else if ((cpdist1[i] < dist_threshold ||
                    cpdist2[i] > -dist_threshold)) {
          float shift_epsilon = fabs(_camera_data[i].t[2] * FLT_EPSILON);
          float shift = std::max(shift_min, shift_epsilon);
          bool boths =
              cpdist1[i] < dist_threshold && cpdist2[i] > -dist_threshold;
          _camera_data[i].t[2] += shift;
          if (__verbose_level > 3)
            std::cout << "Adjust C" << std::setw(5) << i << " by "
                      << std::setw(12) << shift << " [B" << std::setw(2)
                      << cambpj[i] << "/" << std::setw(5) << camnpj[i] << "] ["
                      << (boths ? 'X' : ' ') << "][" << cpdist1[i] << ", "
                      << cpdist2[i] << "]\n";
          __num_camera_modified++;
        }
        _camera_data[i].t[0] *= __depth_scaling;
        _camera_data[i].t[1] *= __depth_scaling;
        _camera_data[i].t[2] *= __depth_scaling;
      }
      for (int i = 0; i < _num_point; ++i) {
        /////////////////////////////////
        _point_data[4 * i + 0] *= __depth_scaling;
        _point_data[4 * i + 1] *= __depth_scaling;
        _point_data[4 * i + 2] *= __depth_scaling;
      }
    }
    if (__num_point_behind > 0)
      std::cout << "WARNING: " << __num_point_behind
                << " points are behind cameras.\n";
    if (__num_camera_modified > 0)
      std::cout << "WARNING: " << __num_camera_modified
                << " camera moved to avoid degeneracy.\n";
  }
}

template <class Float>
void SparseBundleCPU<Float>::DenormalizeData() {
  if (__focal_normalize && __focal_scaling != 1.0f) {
    float squared_focal_factor = (__focal_scaling * __focal_scaling);
    for (int i = 0; i < _num_camera; ++i) {
      _camera_data[i].f /= __focal_scaling;
      if (__use_radial_distortion == -1)
        _camera_data[i].radial *= squared_focal_factor;
      _camera_data[i].distortion_type = __use_radial_distortion;
    }
    _projection_sse /= squared_focal_factor;
    __focal_scaling = 1.0f;
  } else if (__use_radial_distortion) {
    for (int i = 0; i < _num_camera; ++i)
      _camera_data[i].distortion_type = __use_radial_distortion;
  }

  if (__depth_normalize && __depth_scaling != 1.0f) {
    for (int i = 0; i < _num_camera; ++i) {
      _camera_data[i].t[0] /= __depth_scaling;
      _camera_data[i].t[1] /= __depth_scaling;
      _camera_data[i].t[2] /= __depth_scaling;
    }
    for (int i = 0; i < _num_point; ++i) {
      _point_data[4 * i + 0] /= __depth_scaling;
      _point_data[4 * i + 1] /= __depth_scaling;
      _point_data[4 * i + 2] /= __depth_scaling;
    }
    __depth_scaling = 1.0f;
  }
}

template <class Float>
int SparseBundleCPU<Float>::SolveNormalEquationPCGX(float lambda) {
  //----------------------------------------------------------
  //(Jt * J + lambda * diag(Jt * J)) X = Jt * e
  //-------------------------------------------------------------
  TimerBA timer(this, TIMER_CG_ITERATION);
  __recent_cg_status = ' ';

  // diagonal for jacobian preconditioning...
  int plen = GetParameterLength();
  VectorF null;
  VectorF& VectorDP = __lm_use_diagonal_damp ? _cuVectorJJ : null;  // diagonal
  ComputeBlockPC(lambda, __lm_use_diagonal_damp);

  ////////////////////////////////////////////////

  ///////////////////////////////////////////////////////
  // B = [BC 0 ; 0 BP]
  // m = [mc 0; 0 mp];
  // A x= BC * x - JcT * Jp * mp * JpT * Jc * x
  //   = JcT * Jc x + lambda * D * x + ........
  ////////////////////////////////////////////////////////////

  VectorF r;
  r.set(_cuVectorRK.data(), 8 * _num_camera);
  VectorF p;
  p.set(_cuVectorPK.data(), 8 * _num_camera);
  VectorF z;
  z.set(_cuVectorZK.data(), 8 * _num_camera);
  VectorF x;
  x.set(_cuVectorXK.data(), 8 * _num_camera);
  VectorF d;
  d.set(VectorDP.data(), 8 * _num_camera);

  VectorF& u = _cuVectorRK;
  VectorF& v = _cuVectorPK;
  VectorF up;
  up.set(u.data() + 8 * _num_camera, 3 * _num_point);
  VectorF vp;
  vp.set(v.data() + 8 * _num_camera, 3 * _num_point);
  VectorF uc;
  uc.set(z.data(), 8 * _num_camera);

  VectorF& e = _cuVectorJX;
  VectorF& e2 = _cuImageProj;

  ApplyBlockPC(_cuVectorJtE, u, 2);
  ComputeJX(u, e, 2);
  ComputeJtE(e, uc, 1);
  ComputeSAXPY(Float(-1.0f), uc, _cuVectorJtE, r);  // r
  ApplyBlockPC(r, p, 1);  // z = p = M r

  float_t rtz0 = (float_t)ComputeVectorDot(r, p);  // r(0)' * z(0)
  ComputeJX(p, e, 1);  // Jc * x
  ComputeJtE(e, u, 2);  // JpT * jc * x
  ApplyBlockPC(u, v, 2);
  float_t qtq0 =
      (float_t)ComputeVectorNorm(e, __num_cpu_thread[FUNC_VS]);  // q(0)' * q(0)
  float_t pdp0 = (float_t)ComputeVectorNormW(p, d);  // p(0)' * DDD * p(0)
  float_t uv0 = (float_t)ComputeVectorDot(up, vp);
  float_t alpha0 = rtz0 / (qtq0 + lambda * pdp0 - uv0);

  if (__verbose_cg_iteration)
    std::cout << " --0,\t alpha = " << alpha0
              << ", t = " << BundleTimerGetNow(TIMER_CG_ITERATION) << "\n";
  if (!std::isfinite(alpha0)) {
    return 0;
  }
  if (alpha0 == 0) {
    __recent_cg_status = 'I';
    return 1;
  }

  ////////////////////////////////////////////////////////////
  ComputeSAX((Float)alpha0, p, x);  // x(k+1) = x(k) + a(k) * p(k)
  ComputeJX(v, e2, 2);  //                          //Jp * mp * JpT * JcT * p
  ComputeSAXPY(Float(-1.0f), e2, e, e, __num_cpu_thread[FUNC_VV]);
  ComputeJtE(e, uc, 1);  // JcT * ....
  ComputeSXYPZ((Float)lambda, d, p, uc, uc);
  ComputeSAXPY((Float)-alpha0, uc, r, r);  // r(k + 1) = r(k) - a(k) * A * pk

  //////////////////////////////////////////////////////////////////////////
  float_t rtzk = rtz0, rtz_min = rtz0, betak;
  int iteration = 1;
  ++__num_cg_iteration;

  while (true) {
    ApplyBlockPC(r, z, 1);

    ///////////////////////////////////////////////////////////////////////////
    float_t rtzp = rtzk;
    rtzk = (float_t)ComputeVectorDot(
        r, z);  //[r(k + 1) = M^(-1) * z(k + 1)] * z(k+1)
    float_t rtz_ratio = sqrt(fabs(rtzk / rtz0));
    if (rtz_ratio < __cg_norm_threshold) {
      if (__recent_cg_status == ' ')
        __recent_cg_status = iteration < std::min(10, __cg_min_iteration)
                                 ? '0' + iteration
                                 : 'N';
      if (iteration >= __cg_min_iteration) break;
    }
    ////////////////////////////////////////////////////////////////////////////
    betak = rtzk / rtzp;  // beta
    rtz_min = std::min(rtz_min, rtzk);

    ComputeSAXPY((Float)betak, p, z, p);  // p(k) = z(k) + b(k) * p(k - 1)
    ComputeJX(p, e, 1);  // Jc * p
    ComputeJtE(e, u, 2);  // JpT * jc * p
    ApplyBlockPC(u, v, 2);
    //////////////////////////////////////////////////////////////////////

    float_t qtqk =
        (float_t)ComputeVectorNorm(e, __num_cpu_thread[FUNC_VS]);  // q(k)' q(k)
    float_t pdpk = (float_t)ComputeVectorNormW(p, d);  // p(k)' * DDD * p(k)
    float_t uvk = (float_t)ComputeVectorDot(up, vp);
    float_t alphak = rtzk / (qtqk + lambda * pdpk - uvk);

    /////////////////////////////////////////////////////
    if (__verbose_cg_iteration)
      std::cout << " --" << iteration << ",\t alpha= " << alphak
                << ", rtzk/rtz0 = " << rtz_ratio
                << ", t = " << BundleTimerGetNow(TIMER_CG_ITERATION) << "\n";

    ///////////////////////////////////////////////////
    if (!std::isfinite(alphak) || rtz_ratio > __cg_norm_guard) {
      __recent_cg_status = 'X';
      break;
    }  // something doesn't converge..

    ////////////////////////////////////////////////
    ComputeSAXPY((Float)alphak, p, x, x);  // x(k+1) = x(k) + a(k) * p(k)

    /////////////////////////////////////////////////
    ++iteration;
    ++__num_cg_iteration;
    if (iteration >= std::min(__cg_max_iteration, plen)) break;

    ComputeJX(v, e2, 2);  //                          //Jp * mp * JpT * JcT * p
    ComputeSAXPY((Float)-1.0f, e2, e, e, __num_cpu_thread[FUNC_VV]);
    ComputeJtE(e, uc, 1);  // JcT * ....
    ComputeSXYPZ((Float)lambda, d, p, uc, uc);
    ComputeSAXPY((Float)-alphak, uc, r, r);  // r(k + 1) = r(k) - a(k) * A * pk
  }

  ComputeJX(x, e, 1);
  ComputeJtE(e, u, 2);
  VectorF jte_p;
  jte_p.set(_cuVectorJtE.data() + 8 * _num_camera, _num_point * 3);
  ComputeSAXPY((Float)-1.0f, up, jte_p, vp);
  ApplyBlockPC(v, _cuVectorXK, 2);
  return iteration;
}

template <class Float>
int SparseBundleCPU<Float>::SolveNormalEquationPCGB(float lambda) {
  //----------------------------------------------------------
  //(Jt * J + lambda * diag(Jt * J)) X = Jt * e
  //-------------------------------------------------------------
  TimerBA timer(this, TIMER_CG_ITERATION);
  __recent_cg_status = ' ';

  // diagonal for jacobian preconditioning...
  int plen = GetParameterLength();
  VectorF null;
  VectorF& VectorDP = __lm_use_diagonal_damp ? _cuVectorJJ : null;  // diagonal
  VectorF& VectorQK = _cuVectorZK;  // temporary
  ComputeBlockPC(lambda, __lm_use_diagonal_damp);

  ////////////////////////////////////////////////////////
  ApplyBlockPC(_cuVectorJtE,
               _cuVectorPK);  // z(0) = p(0) = M * r(0)//r(0) = Jt * e
  ComputeJX(_cuVectorPK, _cuVectorJX);  // q(0) = J * p(0)

  //////////////////////////////////////////////////
  float_t rtz0 =
      (float_t)ComputeVectorDot(_cuVectorJtE, _cuVectorPK);  // r(0)' * z(0)
  float_t qtq0 = (float_t)ComputeVectorNorm(
      _cuVectorJX, __num_cpu_thread[FUNC_VS]);  // q(0)' * q(0)
  float_t ptdp0 =
      (float_t)ComputeVectorNormW(_cuVectorPK, VectorDP);  // p(0)' * DDD * p(0)
  float_t alpha0 = rtz0 / (qtq0 + lambda * ptdp0);

  if (__verbose_cg_iteration)
    std::cout << " --0,\t alpha = " << alpha0
              << ", t = " << BundleTimerGetNow(TIMER_CG_ITERATION) << "\n";
  if (!std::isfinite(alpha0)) {
    return 0;
  }
  if (alpha0 == 0) {
    __recent_cg_status = 'I';
    return 1;
  }

  ////////////////////////////////////////////////////////////

  ComputeSAX((Float)alpha0, _cuVectorPK,
             _cuVectorXK);  // x(k+1) = x(k) + a(k) * p(k)
  ComputeJtE(_cuVectorJX, VectorQK);  // Jt * (J * p0)

  ComputeSXYPZ((Float)lambda, VectorDP, _cuVectorPK, VectorQK,
               VectorQK);  // Jt * J * p0 + lambda * DDD * p0

  ComputeSAXPY(
      (Float)-alpha0, VectorQK, _cuVectorJtE,
      _cuVectorRK);  // r(k+1) = r(k) - a(k) * (Jt * q(k)  + DDD * p(k)) ;

  float_t rtzk = rtz0, rtz_min = rtz0, betak;
  int iteration = 1;
  ++__num_cg_iteration;

  while (true) {
    ApplyBlockPC(_cuVectorRK, _cuVectorZK);

    ///////////////////////////////////////////////////////////////////////////
    float_t rtzp = rtzk;
    rtzk = (float_t)ComputeVectorDot(
        _cuVectorRK, _cuVectorZK);  //[r(k + 1) = M^(-1) * z(k + 1)] * z(k+1)
    float_t rtz_ratio = sqrt(fabs(rtzk / rtz0));
    if (rtz_ratio < __cg_norm_threshold) {
      if (__recent_cg_status == ' ')
        __recent_cg_status = iteration < std::min(10, __cg_min_iteration)
                                 ? '0' + iteration
                                 : 'N';
      if (iteration >= __cg_min_iteration) break;
    }
    //////////////////////////////////////////////////////////////////////////
    betak = rtzk / rtzp;  // beta
    rtz_min = std::min(rtz_min, rtzk);

    ComputeSAXPY((Float)betak, _cuVectorPK, _cuVectorZK,
                 _cuVectorPK);  // p(k) = z(k) + b(k) * p(k - 1)
    ComputeJX(_cuVectorPK, _cuVectorJX);  // q(k) = J * p(k)
    //////////////////////////////////////////////////////////////////////

    float_t qtqk = (float_t)ComputeVectorNorm(
        _cuVectorJX, __num_cpu_thread[FUNC_VS]);  // q(k)' q(k)
    float_t ptdpk = (float_t)ComputeVectorNormW(
        _cuVectorPK, VectorDP);  // p(k)' * DDD * p(k)

    float_t alphak = rtzk / (qtqk + lambda * ptdpk);

    /////////////////////////////////////////////////////
    if (__verbose_cg_iteration)
      std::cout << " --" << iteration << ",\t alpha= " << alphak
                << ", rtzk/rtz0 = " << rtz_ratio
                << ", t = " << BundleTimerGetNow(TIMER_CG_ITERATION) << "\n";

    ///////////////////////////////////////////////////
    if (!std::isfinite(alphak) || rtz_ratio > __cg_norm_guard) {
      __recent_cg_status = 'X';
      break;
    }  // something doesn't converge..

    ////////////////////////////////////////////////
    ComputeSAXPY((Float)alphak, _cuVectorPK, _cuVectorXK,
                 _cuVectorXK);  // x(k+1) = x(k) + a(k) * p(k)

    /////////////////////////////////////////////////
    ++iteration;
    ++__num_cg_iteration;
    if (iteration >= std::min(__cg_max_iteration, plen)) break;

    if (__cg_recalculate_freq > 0 && iteration % __cg_recalculate_freq == 0) {
      ////r = JtE - (Jt J + lambda * D) x
      ComputeJX(_cuVectorXK, _cuVectorJX);
      ComputeJtE(_cuVectorJX, VectorQK);
      ComputeSXYPZ((Float)lambda, VectorDP, _cuVectorXK, VectorQK, VectorQK);
      ComputeSAXPY((Float)-1.0f, VectorQK, _cuVectorJtE, _cuVectorRK);
    } else {
      ComputeJtE(_cuVectorJX, VectorQK);
      ComputeSXYPZ((Float)lambda, VectorDP, _cuVectorPK, VectorQK,
                   VectorQK);  //
      ComputeSAXPY(
          (Float)-alphak, VectorQK, _cuVectorRK,
          _cuVectorRK);  // r(k+1) = r(k) - a(k) * (Jt * q(k)  + DDD * p(k)) ;
    }
  }
  return iteration;
}

template <class Float>
int SparseBundleCPU<Float>::SolveNormalEquation(float lambda) {
  if (__bundle_current_mode == BUNDLE_ONLY_MOTION) {
    ComputeBlockPC(lambda, __lm_use_diagonal_damp);
    ApplyBlockPC(_cuVectorJtE, _cuVectorXK, 1);
    return 1;
  } else if (__bundle_current_mode == BUNDLE_ONLY_STRUCTURE) {
    ComputeBlockPC(lambda, __lm_use_diagonal_damp);
    ApplyBlockPC(_cuVectorJtE, _cuVectorXK, 2);
    return 1;
  } else {
    ////solve linear system using Conjugate Gradients
    return __cg_schur_complement ? SolveNormalEquationPCGX(lambda)
                                 : SolveNormalEquationPCGB(lambda);
  }
}

template <class Float>
void SparseBundleCPU<Float>::DumpCooJacobian() {
  //////////
  ofstream jo("j.txt");
  int cn = __use_radial_distortion ? 8 : 7;
  int width = cn * _num_camera + 3 * _num_point;
  jo << "%%MatrixMarket matrix coordinate real general\n";
  jo << (_num_imgpt * 2) << " " << width << " " << (cn + 3) * _num_imgpt * 2
     << '\n';
  for (int i = 0; i < _num_imgpt; ++i) {
    int ci = _camera_idx[i];
    int pi = _point_idx[i];
    int row = i * 2 + 1;
    // Float * jc = _cuJacobianCamera.data() + i * 16;
    // Float * jp = _cuJacobianPoint.data() + i * 6;
    int idx1 = ci * cn;
    int idx2 = _num_camera * cn + 3 * pi;

    for (int k = 0; k < 2; ++k, ++row) {
      for (int j = 0; j < cn; ++j) {
        jo << row << " " << (idx1 + j + 1) << " 1\n";
      }
      for (int j = 0; j < 3; ++j) {
        jo << row << " " << (idx2 + j + 1) << " 1\n";
      }
    }
  }

  ofstream jt("jt.txt");
  jt << "%%MatrixMarket matrix coordinate real general\n";
  jt << width << " " << (_num_imgpt * 2) << " " << (cn + 3) * _num_imgpt * 2
     << '\n';

  int* lisc = &_cuCameraMeasurementList[0];
  int* mapc = &_cuCameraMeasurementMap[0];
  int* mapp = &_cuPointMeasurementMap[0];

  for (int i = 0; i < _num_camera; ++i) {
    int c0 = mapc[i];
    int c1 = mapc[i + 1];
    for (int k = 0; k < cn; ++k) {
      int row = i * cn + k + 1;
      for (int j = c0; j < c1; ++j)
        jt << row << " " << (lisc[j] * 2 + 1) << " 1\n" << row << " "
           << (2 * lisc[j] + 2) << " 1\n";
      ;
    }
  }
  for (int i = 0; i < _num_point; ++i) {
    int p0 = mapp[i];
    int p1 = mapp[i + 1];
    for (int k = 0; k < 3; ++k) {
      int row = i * 3 + _num_camera * cn + k + 1;
      for (int j = p0; j < p1; ++j)
        jt << row << " " << (2 * j + 1) << " 1\n" << row << " " << (2 * j + 2)
           << " 1\n";
      ;
    }
  }
}

template <class Float>
void SparseBundleCPU<Float>::RunTestIterationLM(bool reduced) {
  EvaluateProjection(_cuCameraData, _cuPointData, _cuImageProj);
  EvaluateJacobians();
  ComputeJtE(_cuImageProj, _cuVectorJtE);
  if (reduced)
    SolveNormalEquationPCGX(__lm_initial_damp);
  else
    SolveNormalEquationPCGB(__lm_initial_damp);
  UpdateCameraPoint(_cuVectorZK, _cuImageProj);
  ComputeVectorDot(_cuVectorXK, _cuVectorJtE);
  ComputeJX(_cuVectorXK, _cuVectorJX);
  ComputeVectorNorm(_cuVectorJX, __num_cpu_thread[FUNC_VS]);
}

template <class Float>
float SparseBundleCPU<Float>::UpdateCameraPoint(VectorF& dx,
                                                VectorF& cuImageTempProj) {
  ConfigBA::TimerBA timer(this, TIMER_FUNCTION_UP, true);

  if (__bundle_current_mode == BUNDLE_ONLY_MOTION) {
    if (__jacobian_normalize)
      ComputeVXY(_cuVectorXK, _cuVectorSJ, dx, 8 * _num_camera);
    ProgramCPU::UpdateCameraPoint(
        _num_camera, _cuCameraData, _cuPointData, dx, _cuCameraDataEX,
        _cuPointDataEX, __bundle_current_mode, __num_cpu_thread[FUNC_VV]);
    return EvaluateProjection(_cuCameraDataEX, _cuPointData, cuImageTempProj);
  } else if (__bundle_current_mode == BUNDLE_ONLY_STRUCTURE) {
    if (__jacobian_normalize)
      ComputeVXY(_cuVectorXK, _cuVectorSJ, dx, _num_point * POINT_ALIGN,
                 _num_camera * 8);
    ProgramCPU::UpdateCameraPoint(
        _num_camera, _cuCameraData, _cuPointData, dx, _cuCameraDataEX,
        _cuPointDataEX, __bundle_current_mode, __num_cpu_thread[FUNC_VV]);
    return EvaluateProjection(_cuCameraData, _cuPointDataEX, cuImageTempProj);
  } else {
    if (__jacobian_normalize) ComputeVXY(_cuVectorXK, _cuVectorSJ, dx);
    ProgramCPU::UpdateCameraPoint(
        _num_camera, _cuCameraData, _cuPointData, dx, _cuCameraDataEX,
        _cuPointDataEX, __bundle_current_mode, __num_cpu_thread[FUNC_VV]);
    return EvaluateProjection(_cuCameraDataEX, _cuPointDataEX, cuImageTempProj);
  }
}

template <class Float>
float SparseBundleCPU<Float>::SaveUpdatedSystem(float residual_reduction,
                                                float dx_sqnorm,
                                                float damping) {
  float expected_reduction;
  if (__bundle_current_mode == BUNDLE_ONLY_MOTION) {
    VectorF xk;
    xk.set(_cuVectorXK.data(), 8 * _num_camera);
    VectorF jte;
    jte.set(_cuVectorJtE.data(), 8 * _num_camera);
    float dxtg = (float)ComputeVectorDot(xk, jte);
    if (__lm_use_diagonal_damp) {
      VectorF jj;
      jj.set(_cuVectorJJ.data(), 8 * _num_camera);
      float dq = (float)ComputeVectorNormW(xk, jj);
      expected_reduction = damping * dq + dxtg;
    } else {
      expected_reduction = damping * dx_sqnorm + dxtg;
    }
    _cuCameraData.swap(_cuCameraDataEX);
  } else if (__bundle_current_mode == BUNDLE_ONLY_STRUCTURE) {
    VectorF xk;
    xk.set(_cuVectorXK.data() + 8 * _num_camera, POINT_ALIGN * _num_point);
    VectorF jte;
    jte.set(_cuVectorJtE.data() + 8 * _num_camera, POINT_ALIGN * _num_point);
    float dxtg = (float)ComputeVectorDot(xk, jte);
    if (__lm_use_diagonal_damp) {
      VectorF jj;
      jj.set(_cuVectorJJ.data() + 8 * _num_camera, POINT_ALIGN * _num_point);
      float dq = (float)ComputeVectorNormW(xk, jj);
      expected_reduction = damping * dq + dxtg;
    } else {
      expected_reduction = damping * dx_sqnorm + dxtg;
    }
    _cuPointData.swap(_cuPointDataEX);
  } else {
    float dxtg = (float)ComputeVectorDot(_cuVectorXK, _cuVectorJtE);
    if (__accurate_gain_ratio) {
      ComputeJX(_cuVectorXK, _cuVectorJX);
      float njx =
          (float)ComputeVectorNorm(_cuVectorJX, __num_cpu_thread[FUNC_VS]);
      expected_reduction = 2.0f * dxtg - njx;

      // could the expected reduction be negative??? not sure
      if (expected_reduction <= 0)
        expected_reduction = 0.001f * residual_reduction;
    } else if (__lm_use_diagonal_damp) {
      float dq = (float)ComputeVectorNormW(_cuVectorXK, _cuVectorJJ);
      expected_reduction = damping * dq + dxtg;
    } else {
      expected_reduction = damping * dx_sqnorm + dxtg;
    }
    /// save the new motion/struture
    _cuCameraData.swap(_cuCameraDataEX);
    _cuPointData.swap(_cuPointDataEX);
  }
  ////////////////////////////////////////////
  return float(residual_reduction / expected_reduction);
}

template <class Float>
void SparseBundleCPU<Float>::AdjustBundleAdjsutmentMode() {
  if (__bundle_current_mode == BUNDLE_ONLY_MOTION) {
    _cuJacobianPoint.resize(0);
  } else if (__bundle_current_mode == BUNDLE_ONLY_STRUCTURE) {
    _cuJacobianCamera.resize(0);
    _cuJacobianCameraT.resize(0);
  }
}

template <class Float>
float SparseBundleCPU<Float>::EvaluateDeltaNorm() {
  if (__bundle_current_mode == BUNDLE_ONLY_MOTION) {
    VectorF temp;
    temp.set(_cuVectorXK.data(), 8 * _num_camera);
    return (float)ComputeVectorNorm(temp);
  } else if (__bundle_current_mode == BUNDLE_ONLY_STRUCTURE) {
    VectorF temp;
    temp.set(_cuVectorXK.data() + 8 * _num_camera, POINT_ALIGN * _num_point);
    return (float)ComputeVectorNorm(temp);
  } else {
    return (float)ComputeVectorNorm(_cuVectorXK);
  }
}

template <class Float>
void SparseBundleCPU<Float>::NonlinearOptimizeLM() {
  ////////////////////////////////////////
  TimerBA timer(this, TIMER_OPTIMIZATION);

  ////////////////////////////////////////////////
  float mse_convert_ratio =
      1.0f / (_num_imgpt * __focal_scaling * __focal_scaling);
  float error_display_ratio = __verbose_sse ? _num_imgpt : 1.0f;
  const int edwidth = __verbose_sse ? 12 : 8;
  _projection_sse =
      EvaluateProjection(_cuCameraData, _cuPointData, _cuImageProj);
  __initial_mse = __final_mse = _projection_sse * mse_convert_ratio;

  // compute jacobian diagonals for normalization
  if (__jacobian_normalize) PrepareJacobianNormalization();

  // evalaute jacobian
  EvaluateJacobians();
  ComputeJtE(_cuImageProj, _cuVectorJtE);
  ///////////////////////////////////////////////////////////////
  if (__verbose_level)
    std::cout << "Initial " << (__verbose_sse ? "sumed" : "mean")
              << " squared error = " << __initial_mse * error_display_ratio
              << "\n----------------------------------------------\n";

  //////////////////////////////////////////////////
  VectorF& cuImageTempProj = _cuVectorJX;
  // VectorF& cuVectorTempJX  =   _cuVectorJX;
  VectorF& cuVectorDX = _cuVectorSJ.size() ? _cuVectorZK : _cuVectorXK;

  //////////////////////////////////////////////////
  float damping_adjust = 2.0f, damping = __lm_initial_damp, g_norm, g_inf;
  SaveBundleRecord(0, _projection_sse * mse_convert_ratio, damping, g_norm,
                   g_inf);

  ////////////////////////////////////
  std::cout << std::left;
  for (int i = 0; i < __lm_max_iteration && !__abort_flag;
       __current_iteration = (++i)) {
    ////solve linear system
    int num_cg_iteration = SolveNormalEquation(damping);

    // there must be NaN somewhere
    if (num_cg_iteration == 0) {
      if (__verbose_level)
        std::cout << "#" << std::setw(3) << i << " quit on numeric errors\n";
      __pba_return_code = 'E';
      break;
    }

    // there must be infinity somewhere
    if (__recent_cg_status == 'I') {
      std::cout << "#" << std::setw(3) << i << " 0  I e=" << std::setw(edwidth)
                << "------- "
                << " u=" << std::setprecision(3) << std::setw(9) << damping
                << '\n' << std::setprecision(6);
      /////////////increase damping factor
      damping = damping * damping_adjust;
      damping_adjust = 2.0f * damping_adjust;
      --i;
      continue;
    }

    /////////////////////
    ++__num_lm_iteration;

    ////////////////////////////////////
    float dx_sqnorm = EvaluateDeltaNorm(), dx_norm = sqrt(dx_sqnorm);

    // In this library, we check absolute difference instead of realtive
    // difference
    if (dx_norm <= __lm_delta_threshold) {
      // damping factor must be way too big...or it converges
      if (__verbose_level > 1)
        std::cout << "#" << std::setw(3) << i << " " << std::setw(3)
                  << num_cg_iteration << char(__recent_cg_status)
                  << " quit on too small change (" << dx_norm << "  < "
                  << __lm_delta_threshold << ")\n";
      __pba_return_code = 'S';
      break;
    }
    ///////////////////////////////////////////////////////////////////////
    // update structure and motion, check reprojection error
    float new_residual = UpdateCameraPoint(cuVectorDX, cuImageTempProj);
    float average_residual = new_residual * mse_convert_ratio;
    float residual_reduction = _projection_sse - new_residual;

    // do we find a better solution?
    if (std::isfinite(new_residual) && residual_reduction > 0) {
      ////compute relative norm change
      float relative_reduction = 1.0f - (new_residual / _projection_sse);

      ////////////////////////////////////
      __num_lm_success++;  // increase counter
      _projection_sse = new_residual;  // save the new residual
      _cuImageProj.swap(cuImageTempProj);  // save the new projection

      ////////////////////compute gain ratio///////////
      float gain_ratio =
          SaveUpdatedSystem(residual_reduction, dx_sqnorm, damping);

      ////////////////////////////////////////////////
      SaveBundleRecord(i + 1, _projection_sse * mse_convert_ratio, damping,
                       g_norm, g_inf);

      /////////////////////////////////////////////
      if (__verbose_level > 1)
        std::cout << "#" << std::setw(3) << i << " " << std::setw(3)
                  << num_cg_iteration << char(__recent_cg_status)
                  << " e=" << std::setw(edwidth)
                  << average_residual * error_display_ratio
                  << " u=" << std::setprecision(3) << std::setw(9) << damping
                  << " r=" << std::setw(6)
                  << floor(gain_ratio * 1000.f) * 0.001f
                  << " g=" << std::setw(g_norm > 0 ? 9 : 1) << g_norm << " "
                  << std::setw(9) << relative_reduction << ' ' << std::setw(9)
                  << dx_norm << " t=" << int(BundleTimerGetNow()) << "\n"
                  << std::setprecision(6);

      /////////////////////////////
      if (!IsTimeBudgetAvailable()) {
        if (__verbose_level > 1)
          std::cout << "#" << std::setw(3) << i << " used up time budget.\n";
        __pba_return_code = 'T';
        break;
      } else if (__lm_check_gradient && g_inf < __lm_gradient_threshold) {
        if (__verbose_level > 1)
          std::cout << "#" << std::setw(3) << i
                    << " converged with small gradient\n";
        __pba_return_code = 'G';
        break;
      } else if (average_residual * error_display_ratio <= __lm_mse_threshold) {
        if (__verbose_level > 1)
          std::cout << "#" << std::setw(3) << i << " satisfies MSE threshold\n";
        __pba_return_code = 'M';
        break;
      } else {
        /////////////////////////////adjust damping factor
        float temp = gain_ratio * 2.0f - 1.0f;
        float adaptive_adjust = 1.0f - temp * temp * temp;  // powf(, 3.0f); //
        float auto_adjust = std::max(1.0f / 3.0f, adaptive_adjust);

        //////////////////////////////////////////////////
        damping = damping * auto_adjust;
        damping_adjust = 2.0f;
        if (damping < __lm_minimum_damp)
          damping = __lm_minimum_damp;
        else if (__lm_damping_auto_switch == 0 && damping > __lm_maximum_damp &&
                 __lm_use_diagonal_damp)
          damping = __lm_maximum_damp;

        EvaluateJacobians();
        ComputeJtE(_cuImageProj, _cuVectorJtE);
      }
    } else {
      if (__verbose_level > 1)
        std::cout << "#" << std::setw(3) << i << " " << std::setw(3)
                  << num_cg_iteration << char(__recent_cg_status)
                  << " e=" << std::setw(edwidth) << std::left
                  << average_residual * error_display_ratio
                  << " u=" << std::setprecision(3) << std::setw(9) << damping
                  << " r=----- " << (__lm_check_gradient || __save_gradient_norm
                                         ? " g=---------"
                                         : " g=0")
                  << " --------- " << std::setw(9) << dx_norm
                  << " t=" << int(BundleTimerGetNow()) << "\n"
                  << std::setprecision(6);

      if (__lm_damping_auto_switch > 0 && __lm_use_diagonal_damp &&
          damping > __lm_damping_auto_switch) {
        __lm_use_diagonal_damp = false;
        damping = __lm_damping_auto_switch;
        damping_adjust = 2.0f;
        if (__verbose_level > 1)
          std::cout << "NOTE: switch to damping with an identity matix\n";
      } else {
        /////////////increase damping factor
        damping = damping * damping_adjust;
        damping_adjust = 2.0f * damping_adjust;
      }
    }

    if (__verbose_level == 1) std::cout << '.';
  }

  __final_mse = float(_projection_sse * mse_convert_ratio);
  __final_mse_x =
      __use_radial_distortion
          ? EvaluateProjectionX(_cuCameraData, _cuPointData, _cuImageProj) *
                mse_convert_ratio
          : __final_mse;
}

#define PROFILE_REPORT2(A, T) \
  std::cout << std::setw(24) << A << ": " << (T) << "\n";

#define PROFILE_REPORT(A)                 \
  std::cout << std::setw(24) << A << ": " \
            << (BundleTimerGet(TIMER_PROFILE_STEP) / repeat) << "\n";

#define PROFILE_(B)                     \
  BundleTimerStart(TIMER_PROFILE_STEP); \
  for (int i = 0; i < repeat; ++i) {    \
    B;                                  \
  }                                     \
  BundleTimerSwitch(TIMER_PROFILE_STEP);

#define PROFILE(A, B) PROFILE_(A B) PROFILE_REPORT(#A)
#define PROXILE(A, B) PROFILE_(B) PROFILE_REPORT(A)
#define PROTILE(FID, A, B)                                   \
  {                                                          \
    float tbest = FLT_MAX;                                   \
    int nbest = 1;                                           \
    int nto = nthread[FID];                                  \
    {                                                        \
      std::ostringstream os1;                                \
      os1 << #A "(" << nto << ")";                           \
      PROXILE(os1.str(), A B);                               \
    }                                                        \
    for (int j = 1; j <= THREAD_NUM_MAX; j *= 2) {           \
      nthread[FID] = j;                                      \
      PROFILE_(A B);                                         \
      float t = BundleTimerGet(TIMER_PROFILE_STEP) / repeat; \
      if (t > tbest) {                                       \
        if (j >= max(nto, 16)) break;                        \
      } else {                                               \
        tbest = t;                                           \
        nbest = j;                                           \
      }                                                      \
    }                                                        \
    if (nto != 0) nthread[FID] = nbest;                      \
    {                                                        \
      std::ostringstream os;                                 \
      os << #A "(" << nbest << ")";                          \
      PROFILE_REPORT2(os.str(), tbest);                      \
    }                                                        \
  }

#define PROTILE2(FID1, FID2, A, B)                           \
  {                                                          \
    int nt1 = nthread[FID1], nt2 = nthread[FID2];            \
    {                                                        \
      std::ostringstream os1;                                \
      os1 << #A "(" << nt1 << "," << nt2 << ")";             \
      PROXILE(os1.str(), A B);                               \
    }                                                        \
    float tbest = FLT_MAX;                                   \
    int nbest1 = 1, nbest2 = 1;                              \
    nthread[FID2] = 1;                                       \
    for (int j = 1; j <= THREAD_NUM_MAX; j *= 2) {           \
      nthread[FID1] = j;                                     \
      PROFILE_(A B);                                         \
      float t = BundleTimerGet(TIMER_PROFILE_STEP) / repeat; \
      if (t > tbest) {                                       \
        if (j >= max(nt1, 16)) break;                        \
      } else {                                               \
        tbest = t;                                           \
        nbest1 = j;                                          \
      }                                                      \
    }                                                        \
    nthread[FID1] = nbest1;                                  \
    for (int j = 2; j <= THREAD_NUM_MAX; j *= 2) {           \
      nthread[FID2] = j;                                     \
      PROFILE_(A B);                                         \
      float t = BundleTimerGet(TIMER_PROFILE_STEP) / repeat; \
      if (t > tbest) {                                       \
        if (j >= max(nt2, 16)) break;                        \
      } else {                                               \
        tbest = t;                                           \
        nbest2 = j;                                          \
      }                                                      \
    }                                                        \
    nthread[FID2] = nbest2;                                  \
    {                                                        \
      std::ostringstream os;                                 \
      os << #A "(" << nbest1 << "," << nbest2 << ")";        \
      PROFILE_REPORT2(os.str(), tbest);                      \
    }                                                        \
    if (nt1 == 0) nthread[FID1] = 0;                         \
    if (nt2 == 0) nthread[FID2] = 0;                         \
  }

template <class Float>
void SparseBundleCPU<Float>::RunProfileSteps() {
  const int repeat = std::max(__profile_pba, 1);
  int* nthread = __num_cpu_thread;
  std::cout << "---------------------------------\n"
               "|    Run profiling steps ("
            << repeat << ")  |\n"
                         "---------------------------------\n"
            << std::left;
  ;

  ///////////////////////////////////////////////
  EvaluateProjection(_cuCameraData, _cuPointData, _cuImageProj);
  if (__jacobian_normalize) PrepareJacobianNormalization();
  EvaluateJacobians();
  ComputeJtE(_cuImageProj, _cuVectorJtE);
  ComputeBlockPC(__lm_initial_damp, true);
  ///////////////////////////////
  do {
    if (SolveNormalEquationPCGX(__lm_initial_damp) == 10 &&
        SolveNormalEquationPCGB(__lm_initial_damp) == 10)
      break;
    __lm_initial_damp *= 2.0f;
  } while (__lm_initial_damp < 1024.0f);
  std::cout << "damping set to " << __lm_initial_damp << " for profiling\n"
            << "---------------------------------\n";
  ///////////////////////
  {
    int repeat = 10, cgmin = __cg_min_iteration, cgmax = __cg_max_iteration;
    __cg_max_iteration = __cg_min_iteration = 10;
    __num_cg_iteration = 0;
    PROFILE(SolveNormalEquationPCGX, (__lm_initial_damp));
    if (__num_cg_iteration != 100)
      std::cout << __num_cg_iteration << " cg iterations in all\n";
    //////////////////////////////////////////////////////
    __num_cg_iteration = 0;
    PROFILE(SolveNormalEquationPCGB, (__lm_initial_damp));
    if (__num_cg_iteration != 100)
      std::cout << __num_cg_iteration << " cg iterations in all\n";
    std::cout << "---------------------------------\n";
    //////////////////////////////////////////////////////
    __num_cg_iteration = 0;
    PROXILE("Single iteration LMX", RunTestIterationLM(true));
    if (__num_cg_iteration != 100)
      std::cout << __num_cg_iteration << " cg iterations in all\n";
    //////////////////////////////////////////////////////
    __num_cg_iteration = 0;
    PROXILE("Single iteration LMB", RunTestIterationLM(false));
    if (__num_cg_iteration != 100)
      std::cout << __num_cg_iteration << " cg iterations in all\n";
    std::cout << "---------------------------------\n";
    __cg_max_iteration = cgmax;
    __cg_min_iteration = cgmin;
  }

  /////////////////////////////////////////////////////
  PROFILE(UpdateCameraPoint, (_cuVectorZK, _cuImageProj));
  PROFILE(ComputeVectorNorm, (_cuVectorXK));
  PROFILE(ComputeVectorDot, (_cuVectorXK, _cuVectorRK));
  PROFILE(ComputeVectorNormW, (_cuVectorXK, _cuVectorRK));
  PROFILE(ComputeSAXPY, ((Float)0.01f, _cuVectorXK, _cuVectorRK, _cuVectorZK));
  PROFILE(ComputeSXYPZ,
          ((Float)0.01f, _cuVectorXK, _cuVectorPK, _cuVectorRK, _cuVectorZK));
  std::cout << "---------------------------------\n";
  PROTILE(FUNC_VS, ComputeVectorNorm,
          (_cuImageProj, nthread[FUNC_VS]));  // reset the parameter to 0

  ///////////////////////////////////////
  {
    avec<Float> temp1(_cuImageProj.size()), temp2(_cuImageProj.size());
    SetVectorZero(temp1);
    PROTILE(FUNC_VV, ComputeSAXPY,
            ((Float)0.01f, _cuImageProj, temp1, temp2, nthread[FUNC_VV]));
  }

  std::cout << "---------------------------------\n";
  __multiply_jx_usenoj = false;

  ////////////////////////////////////////////////////
  PROTILE(FUNC_PJ, EvaluateProjection,
          (_cuCameraData, _cuPointData, _cuImageProj));
  PROTILE2(FUNC_MPC, FUNC_MPP, ApplyBlockPC, (_cuVectorJtE, _cuVectorPK));

  /////////////////////////////////////////////////
  if (!__no_jacobian_store) {
    if (__jc_store_original) {
      PROTILE(FUNC_JX, ComputeJX, (_cuVectorJtE, _cuVectorJX));

      if (__jc_store_transpose) {
        PROTILE(FUNC_JJ_JCO_JCT_JP, EvaluateJacobians, ());
        PROTILE2(FUNC_JTEC_JCT, FUNC_JTEP, ComputeJtE,
                 (_cuImageProj, _cuVectorJtE));
        PROTILE2(FUNC_BCC_JCT, FUNC_BCP, ComputeBlockPC, (0.001f, true));
        PROFILE(ComputeDiagonal, (_cuVectorPK));

        std::cout << "---------------------------------\n"
                     "|   Not storing original  JC    | \n"
                     "---------------------------------\n";
        __jc_store_original = false;
        PROTILE(FUNC_JJ_JCT_JP, EvaluateJacobians, ());
        __jc_store_original = true;
      }

      //////////////////////////////////////////////////
      std::cout << "---------------------------------\n"
                   "|   Not storing transpose JC    | \n"
                   "---------------------------------\n";
      __jc_store_transpose = false;
      _cuJacobianCameraT.resize(0);
      PROTILE(FUNC_JJ_JCO_JP, EvaluateJacobians, ());
      PROTILE2(FUNC_JTEC_JCO, FUNC_JTEP, ComputeJtE,
               (_cuImageProj, _cuVectorJtE));
      PROTILE2(FUNC_BCC_JCO, FUNC_BCP, ComputeBlockPC, (0.001f, true));
      PROFILE(ComputeDiagonal, (_cuVectorPK));
    } else if (__jc_store_transpose) {
      PROTILE2(FUNC_JTEC_JCT, FUNC_JTEP, ComputeJtE,
               (_cuImageProj, _cuVectorJtE));
      PROTILE2(FUNC_BCC_JCT, FUNC_BCP, ComputeBlockPC, (0.001f, true));
      PROFILE(ComputeDiagonal, (_cuVectorPK));

      std::cout << "---------------------------------\n"
                   "|   Not storing original  JC    | \n"
                   "---------------------------------\n";
      PROTILE(FUNC_JJ_JCT_JP, EvaluateJacobians, ());
    }
  }

  if (!__no_jacobian_store) {
    std::cout << "---------------------------------\n"
                 "| Not storing Camera Jacobians  | \n"
                 "---------------------------------\n";
    __jc_store_transpose = false;
    __jc_store_original = false;
    _cuJacobianCamera.resize(0);
    _cuJacobianCameraT.resize(0);
    PROTILE(FUNC_JJ_JP, EvaluateJacobians, ());
    PROTILE(FUNC_JTE_, ComputeJtE, (_cuImageProj, _cuVectorJtE));
    // PROFILE(ComputeBlockPC, (0.001f, true));
  }

  ///////////////////////////////////////////////
  std::cout << "---------------------------------\n"
               "|   Not storing any jacobians   |\n"
               "---------------------------------\n";
  __no_jacobian_store = true;
  _cuJacobianPoint.resize(0);
  PROTILE(FUNC_JX_, ComputeJX, (_cuVectorJtE, _cuVectorJX));
  PROFILE(ComputeJtE, (_cuImageProj, _cuVectorJtE));
  PROFILE(ComputeBlockPC, (0.001f, true));
  std::cout << "---------------------------------\n";
}

template <class Float>
int SparseBundleCPU<Float>::FindProcessorCoreNum() {
#ifdef _WIN32
#if defined(WINAPI_FAMILY) && WINAPI_FAMILY == WINAPI_FAMILY_APP
  SYSTEM_INFO sysinfo;
  GetNativeSystemInfo(&sysinfo);
#else
  SYSTEM_INFO sysinfo;
  GetSystemInfo(&sysinfo);
#endif
  return sysinfo.dwNumberOfProcessors;
#else
  return sysconf(_SC_NPROCESSORS_ONLN);
#endif
}

ParallelBA* NewSparseBundleCPU(bool dp, const int num_threads) {
#ifndef SIMD_NO_DOUBLE
  if (dp)
    return new SparseBundleCPU<double>(num_threads);
  else
#endif
    return new SparseBundleCPU<float>(num_threads);
}

}  // namespace pba