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master
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225837ed0e
7個のファイルの変更75行の追加41行の削除
  1. +3
    -1
      Makefile
  2. +1
    -1
      inc/config.h
  3. +31
    -15
      inc/impl.hpp
  4. +1
    -1
      src/elearn.cpp
  5. +11
    -4
      src/main.cpp
  6. +9
    -5
      src/v3.cpp
  7. +19
    -14
      src/v4.cpp

+ 3
- 1
Makefile ファイルの表示

@@ -236,7 +236,9 @@ v4_pthreads: $(BUILD_DIR)/$(TARGET)
# > make clean
# 3) for v4 cilk for example:
# > make csal_v4_cilk
# 4) run executables from `bin/`
# 4) run executables from `bin/`. Examples:
# > ./bin/tcount_ompv3 -i mtx/NACA0015.mtx --timing -r 3 -o /dev/null
# > ./bin/tcount_pthv4 -i mtx/com_Youtube.mtx --timing --dynamic --print_count

csal_v3: CFLAGS := $(REL_CFLAGS) -DCODE_VERSION=3
csal_v3: TARGET := tcount_v3


+ 1
- 1
inc/config.h ファイルの表示

@@ -22,7 +22,7 @@
#define V3 3
#define V4 4

// Fail-safe verision selection
// Fail-safe version selection
#if !defined CODE_VERSION
#define CODE_VERSION V4
#endif


+ 31
- 15
inc/impl.hpp ファイルの表示

@@ -39,7 +39,7 @@ enum class MatrixType {
};

/*
* Forward type declerations
* Forward type declarations
*/
template<typename DataType, typename IndexType, MatrixType Type = MatrixType::SYMMETRIC> struct Matrix;
template<typename DataType, typename IndexType, MatrixType Type = MatrixType::SYMMETRIC> struct SpMat;
@@ -298,7 +298,7 @@ struct SpMat {
*/
DataType get_lin(IndexType i, IndexType j) {
IndexType idx; bool found;
std::tie(idx, found) =find_lin_idx(rows, col_ptr[j], col_ptr[j+1], i);
std::tie(idx, found) =find_place_idx(rows, col_ptr[j], col_ptr[j+1], i);
return (found) ? values[idx] : 0;
}

@@ -309,8 +309,9 @@ struct SpMat {
* If so we just change it to a new value. If not we add the item on the matrix.
*
* @note
* We don't increase the NNZ value of the struct. We expect the user has already
* change the NNZ value to the right one using @see capacity() function.
* When change a value, we don't increase the NNZ value of the struct. We expect the user has already
* change the NNZ value to the right one using @see capacity() function. When adding a value we
* increase the NNZ.
*
* @param i The row number
* @param j The column number
@@ -318,7 +319,7 @@ struct SpMat {
*/
DataType set(DataType v, IndexType i, IndexType j) {
IndexType idx; bool found;
std::tie(idx, found) = find_lin_idx(rows, col_ptr[j], col_ptr[j+1], i);
std::tie(idx, found) = find_place_idx(rows, col_ptr[j], col_ptr[j+1], i);
if (found)
return values[idx] = v; // we don't change NNZ even if we write "0"
else {
@@ -392,11 +393,13 @@ private:
* \param begin The vector's index to begin
* \param end The vector's index to end
* \param match What to search
* @return The index of the item or end on failure.
* \return An <index, status> pair.
* index is the index of the item or end if not found
* status is true if found, false otherwise
*/
std::pair<IndexType, bool> find_idx(const std::vector<IndexType>& v, IndexType begin, IndexType end, IndexType match) {
IndexType b = begin, e = end-1;
while (true) {
while (b <= e) {
IndexType m = (b+e)/2;
if (v[m] == match) return std::make_pair(m, true);
else if (b >= e) return std::make_pair(end, false);
@@ -417,8 +420,11 @@ private:
* \param begin The vector's index to begin
* \param end The vector's index to end
* \param match What to search
* \return An <index, status> pair.
* index is the index of the item or end if not found
* status is true if found, false otherwise
*/
std::pair<IndexType, bool> find_lin_idx(const std::vector<IndexType>& v, IndexType begin, IndexType end, IndexType match) {
std::pair<IndexType, bool> find_place_idx(const std::vector<IndexType>& v, IndexType begin, IndexType end, IndexType match) {
for ( ; begin < end ; ++begin) {
if (match == v[begin]) return std::make_pair(begin, true);
else if (match < v[begin]) return std::make_pair(begin, false);
@@ -437,7 +443,7 @@ private:
//! @{
std::vector<DataType> values {}; //!< vector to store the values of the matrix
std::vector<IndexType> rows{}; //!< vector to store the row information
std::vector<IndexType> col_ptr{1,0}; //!< vector to stor the column pointers
std::vector<IndexType> col_ptr{1,0}; //!< vector to store the column pointers
IndexType N{0}; //!< The dimension of the matrix (square)
IndexType NNZ{0}; //!< The NNZ (capacity of the matrix)
//! @}
@@ -496,20 +502,25 @@ struct SpMatCol {

/*!
* Multiplication operator
*
* We follow only the non-zero values and multiply only the common indexes.
*
* @tparam C Universal reference for the type right half site column
*
* @param c The right hand site matrix
* @return The value of the inner product of two vectors
* @note The time complexity is \$ O(nnz1+nnz2) \$.
* Where the nnz is the max NNZ elements of the column of the matrix
*/
template <typename C>
DataType operator* (C&& c) {
static_assert(std::is_same<remove_cvref_t<C>, SpMatCol<DataType, IndexType>>(), "");
DataType v{};
while (index() != end() && c.index() != c.end()) {
if (index() < c.index()) advance();
else if (index() > c.index()) ++c;
if (index() < c.index()) advance(); // advance me
else if (index() > c.index()) ++c; // advance other
else { //index() == c.index()
v += get() * *c;
v += get() * *c; // multiply and advance both
++c;
advance();
}
@@ -597,20 +608,25 @@ struct SpMatRow {

/*!
* Multiplication operator
*
* We follow only the non-zero values and multiply only the common indexes.
*
* @tparam C Universal reference for the type right half site column
*
* @param c The right hand site matrix
* @return The value of the inner product of two vectors
* @note The time complexity is \$ O(N+nnz2) \$ and way heavier the ColxCol multiplication.
* Where the nnz is the max NNZ elements of the column of the matrix
*/
template <typename C>
DataType operator* (C&& c) {
static_assert(std::is_same<remove_cvref_t<C>, SpMatCol<DataType, IndexType>>(), "");
DataType v{};
while (index() != end() && c.index() != c.end()) {
if (index() < c.index()) advance();
else if (index() > c.index()) ++c;
if (index() < c.index()) advance(); // advance me
else if (index() > c.index()) ++c; // advance other
else { //index() == c.index()
v += get()* *c;
v += get() * *c; // multiply and advance both
++c;
advance();
}


+ 1
- 1
src/elearn.cpp ファイルの表示

@@ -51,7 +51,7 @@ static void coo2csc_e(
*/
uint32_t find_idx(const uint32_t* v, uint32_t begin, uint32_t end, uint32_t match) {
uint32_t b = begin, e = end-1;
while (1) {
while (b <= e) {
uint32_t m = (b+e)/2;
if (v[m] == match) return m;
else if (b >= e) return end;


+ 11
- 4
src/main.cpp ファイルの表示

@@ -116,18 +116,25 @@ bool get_options(int argc, char* argv[]){
std::cout << " --print_graph <size>\n";
std::cout << " Prints the first <size> x <size> part of the matrix to stdout.\n\n";
std::cout << " -h | --help <size>\n";
std::cout << " Prints this and exit.\n";
std::cout << " Prints this and exit.\n\n";
std::cout << "Examples:\n\n";
std::cout << " Get the total count of matrix in <MFILE> and calculate the time for vector creation 5 times:\n";
std::cout << " > ./tcount -i <MFILE> --timing --print_count -r 5\n\n";
std::cout << " Get the vector to <OUTFILE> of matrix in <MFILE> and print the time to stdout using dynamic scheduling\n";
std::cout << " > ./tcount -i <MFILE> -o <OUTFILE> --timing --dynamic\n\n";
std::cout << " Get the total count of matrix in <MFILE> using <N> workers only\n";
std::cout << " > ./tcount -i <MFILE> -n <N> --print_count\n";
exit(0);
}
else { // parse error
std::cout << "Invokation error. Try -h for details.\n";
std::cout << "Invocation error. Try -h for details.\n";
status = false;
}
}

// Input checkers
if (session.inputMatrix == InputMatrix::UNSPECIFIED) {
std::cout << "Invokation error. Try -h for details.\n";
std::cout << "Invocation error. Try -h for details.\n";
status = false;
}
#if CODE_VERSION == V4
@@ -166,7 +173,7 @@ void prepare_matrix (matrix& A, Timing& timer) {
timer.print_dt("load matrix");
}

if (session.verbose && session.mtx_print) {
if (session.mtx_print) {
logger << "\nMatrix:" << logger.endl;
print_graph (A);
}


+ 9
- 5
src/v3.cpp ファイルの表示

@@ -41,8 +41,8 @@ int nworkers() {
* - A lower triangular matrix which update c[i], c[j], c[k]. This is wayyy faster.
*/
std::vector<value_t> triang_v(matrix& A) {
std::vector<std::atomic<value_t>> c(A.size());
std::vector<value_t> ret(A.size());
std::vector<std::atomic<value_t>> c(A.size()); // atomic for c[j], c[k] only
std::vector<value_t> ret(A.size()); // unrestricted c[i] access

cilk_for (int i=0 ; i<A.size() ; ++i) {
for (auto j = A.getCol(i); j.index() != j.end() ; ++j) {
@@ -61,6 +61,7 @@ std::vector<value_t> triang_v(matrix& A) {
c[i] = c[i]/2;
}
}
// merge c to ret and return it
for (index_t i =0 ; i<A.size() ; ++i) ret[i] += c[i];
return ret;
}
@@ -80,7 +81,7 @@ void do_sum (value_t& out_sum, std::vector<value_t>& v, index_t begin, index_t e
*/
value_t sum (std::vector<value_t>& v) {
int n = nworkers();
std::vector<value_t> sum_v(n, 0); // result of each do_sum invokation.
std::vector<value_t> sum_v(n, 0); // result of each do_sum invocation.

// We spawn workers in a more statically way.
for (index_t i =0 ; i < n ; ++i) {
@@ -141,8 +142,8 @@ int nworkers() {
* - A lower triangular matrix which update c[i], c[j], c[k]. This is waaayyy faster.
*/
std::vector<value_t> triang_v(matrix& A) {
std::vector<std::atomic<value_t>> c(A.size());
std::vector<value_t> ret(A.size());
std::vector<std::atomic<value_t>> c(A.size()); // atomic for c[j], c[k] only
std::vector<value_t> ret(A.size()); // unrestricted c[i] access

// OMP schedule selection
if (session.dynamic) omp_set_schedule (omp_sched_dynamic, 0);
@@ -165,6 +166,7 @@ std::vector<value_t> triang_v(matrix& A) {
c[i] = c[i]/2;
}
}
// merge c to ret and return it
for (index_t i =0 ; i<A.size() ; ++i) ret[i] += c[i];
return ret;
}
@@ -210,10 +212,12 @@ std::vector<value_t> triang_v(matrix& A) {
++c[i];
c[j.index()] += (!session.makeSymmetric)? 1:0;
c[k.index()] += (!session.makeSymmetric)? 1:0;
//^ We set other nodes in case of lower triangular
}
}
}
if (session.makeSymmetric) c[i] /= 2;
//^ We don't have to divide by 2 in case of lower triangular
}
return c;
}


+ 19
- 14
src/v4.cpp ファイルの表示

@@ -37,15 +37,16 @@ int nworkers() {
* vector = --- * (A.* (A*B))*ones_N
* 2
* We squeezed all that to one function for performance. The row*column multiplication
* uses the inner CSC structure of sparse matrix and follows only non-zero members.
* uses the inner CSC structure of sparse matrix and follows only non-zero members
* with a time complexity of \$ O(nnz1 + nnz2) \$
*
* \param A The first matrix to use.
* \param B The second matrix to use (they can be the same).
* \return The count vector. RVO is used here.
* \note
* We use two methods of calculation based on \c --make_symmetric or \c --triangular_only
* - A full matrix calculation which update only c[i]
* - A lower triangular matrix which update c[i], c[j], c[k]. This is waaayyy faster.
* - A full matrix calculation
* - A lower triangular matrix
* \warning
* The later(--triangular_only) produce correct results ONLY if we are after the total count.
*/
@@ -76,7 +77,7 @@ void do_sum (value_t& out_sum, std::vector<value_t>& v, index_t begin, index_t e
*/
value_t sum (std::vector<value_t>& v) {
int n = nworkers();
std::vector<value_t> sum_v(n, 0); // result of each do_sum invokation.
std::vector<value_t> sum_v(n, 0); // result of each do_sum invocation.

// We spawn workers in a more statically way.
for (index_t i =0 ; i < n ; ++i) {
@@ -120,15 +121,16 @@ int nworkers() {
* vector = --- * (A.* (A*B))*ones_N
* 2
* We squeezed all that to one function for performance. The row*column multiplication
* uses the inner CSC structure of sparse matrix and follows only non-zero members.
* uses the inner CSC structure of sparse matrix and follows only non-zero members
* with a time complexity of \$ O(nnz1 + nnz2) \$
*
* \param A The first matrix to use.
* \param B The second matrix to use (they can be the same).
* \return The count vector. RVO is used here.
* \note
* We use two methods of calculation based on \c --make_symmetric or \c --triangular_only
* - A full matrix calculation which update only c[i]
* - A lower triangular matrix which update c[i], c[j], c[k]. This is waaayyy faster.
* - A full matrix calculation
* - A lower triangular matrix
* \warning
* The later(--triangular_only) produce correct results ONLY if we are after the total count.
*/
@@ -189,7 +191,8 @@ int nworkers() {
* 2
*
* We squeezed all that to one function for performance. The row*column multiplication
* uses the inner CSC structure of sparse matrix and follows only non-zero members.
* uses the inner CSC structure of sparse matrix and follows only non-zero members
* with a time complexity of \$ O(nnz1 + nnz2) \$
*
* \param out Reference to output vector
* \param A The first matrix to use.
@@ -198,8 +201,8 @@ int nworkers() {
* \return The count vector. RVO is used here.
* \note
* We use two methods of calculation based on \c --make_symmetric or \c --triangular_only
* - A full matrix calculation which update only c[i]
* - A lower triangular matrix which update c[i], c[j], c[k]. This is waaayyy faster.
* - A full matrix calculation
* - A lower triangular matrix
* \warning
* The later(--triangular_only) produce correct results ONLY if we are after the total count.
*/
@@ -260,7 +263,7 @@ void do_sum (value_t& out_sum, std::vector<value_t>& v, index_t begin, index_t e
*/
value_t sum (std::vector<value_t>& v) {
int n = nworkers();
std::vector<value_t> sum_v(n, 0); // result of each do_sum invokation.
std::vector<value_t> sum_v(n, 0); // result of each do_sum invocation.
std::vector<std::thread> workers;

// We spawn workers in a more statically way.
@@ -288,16 +291,18 @@ int nworkers() { return 1; }
* 1
* vector = --- * (A.* (A*B))*ones_N
* 2
*
* We squeezed all that to one function for performance. The row*column multiplication
* uses the inner CSC structure of sparse matrix and follows only non-zero members.
* uses the inner CSC structure of sparse matrix and follows only non-zero members
* with a time complexity of \$ O(nnz1 + nnz2) \$
*
* \param A The first matrix to use.
* \param B The second matrix to use (they can be the same).
* \return The count vector. RVO is used here.
* \note
* We use two methods of calculation based on \c --make_symmetric or \c --triangular_only
* - A full matrix calculation which update only c[i]
* - A lower triangular matrix which update c[i], c[j], c[k]. This is waaayyy faster.
* - A full matrix calculation
* - A lower triangular matrix
* \warning
* The later(--triangular_only) produce correct results ONLY if we are after the total count.
*/


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