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cpu_spmm_v2.cpp
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/******************************************************************************
* Copyright (c) 2011-2015, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIAeBILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
******************************************************************************/
/******************************************************************************
* How to build:
*
* VC++
* cl.exe mergebased_spmv.cpp /fp:strict /MT /O2 /openmp
*
* GCC (OMP is terrible)
* g++ mergebased_spmv.cpp -lm -ffloat-store -O3 -fopenmp
*
* Intel
* icpc mergebased_spmv.cpp -openmp -O3 -lrt -fno-alias -xHost -lnuma
* export KMP_AFFINITY=granularity=core,scatter
*
*
******************************************************************************/
//---------------------------------------------------------------------
// SpMV comparison tool
//---------------------------------------------------------------------
#include <omp.h>
#include <stdio.h>
#include <vector>
#include <algorithm>
#include <cstdio>
#include <fstream>
#include <sstream>
#include <iostream>
#include <limits>
#include <immintrin.h>
#include <mkl.h>
#include "sparse_matrix.h"
#include "utils.h"
//---------------------------------------------------------------------
// Globals, constants, and type declarations
//---------------------------------------------------------------------
bool g_quiet = false; // Whether to display stats in CSV format
bool g_verbose = false; // Whether to display output to console
bool g_verbose2 = false; // Whether to display input to console
int g_omp_threads = -1; // Number of openMP threads
int g_expected_calls = 1000000;
bool g_input_row_major = true;
bool g_output_row_major = true;
//---------------------------------------------------------------------
// Utility types
//---------------------------------------------------------------------
struct int2
{
int x;
int y;
};
/**
* Counting iterator
*/
template <
typename ValueType,
typename OffsetT = ptrdiff_t>
struct CountingInputIterator
{
// Required iterator traits
typedef CountingInputIterator self_type; ///< My own type
typedef OffsetT difference_type; ///< Type to express the result of subtracting one iterator from another
typedef ValueType value_type; ///< The type of the element the iterator can point to
typedef ValueType* pointer; ///< The type of a pointer to an element the iterator can point to
typedef ValueType reference; ///< The type of a reference to an element the iterator can point to
typedef std::random_access_iterator_tag iterator_category; ///< The iterator category
ValueType val;
/// Constructor
inline CountingInputIterator(
const ValueType &val) ///< Starting value for the iterator instance to report
:
val(val)
{}
/// Postfix increment
inline self_type operator++(int)
{
self_type retval = *this;
val++;
return retval;
}
/// Prefix increment
inline self_type operator++()
{
val++;
return *this;
}
/// Indirection
inline reference operator*() const
{
return val;
}
/// Addition
template <typename Distance>
inline self_type operator+(Distance n) const
{
self_type retval(val + n);
return retval;
}
/// Addition assignment
template <typename Distance>
inline self_type& operator+=(Distance n)
{
val += n;
return *this;
}
/// Subtraction
template <typename Distance>
inline self_type operator-(Distance n) const
{
self_type retval(val - n);
return retval;
}
/// Subtraction assignment
template <typename Distance>
inline self_type& operator-=(Distance n)
{
val -= n;
return *this;
}
/// Distance
inline difference_type operator-(self_type other) const
{
return val - other.val;
}
/// Array subscript
template <typename Distance>
inline reference operator[](Distance n) const
{
return val + n;
}
/// Structure dereference
inline pointer operator->()
{
return &val;
}
/// Equal to
inline bool operator==(const self_type& rhs)
{
return (val == rhs.val);
}
/// Not equal to
inline bool operator!=(const self_type& rhs)
{
return (val != rhs.val);
}
/// ostream operator
friend std::ostream& operator<<(std::ostream& os, const self_type& itr)
{
os << "[" << itr.val << "]";
return os;
}
};
//---------------------------------------------------------------------
// MergePath Search
//---------------------------------------------------------------------
/**
* Computes the begin offsets into A and B for the specific diagonal
*/
template <
typename AIteratorT,
typename BIteratorT,
typename OffsetT,
typename CoordinateT>
inline void MergePathSearch(
OffsetT diagonal, ///< [in]The diagonal to search
AIteratorT a, ///< [in]List A
BIteratorT b, ///< [in]List B
OffsetT a_len, ///< [in]Length of A
OffsetT b_len, ///< [in]Length of B
CoordinateT& path_coordinate) ///< [out] (x,y) coordinate where diagonal intersects the merge path
{
OffsetT x_min = std::max(diagonal - b_len, 0);
OffsetT x_max = std::min(diagonal, a_len);
while (x_min < x_max)
{
OffsetT x_pivot = (x_min + x_max) >> 1;
if (a[x_pivot] <= b[diagonal - x_pivot - 1])
x_min = x_pivot + 1; // Contract range up A (down B)
else
x_max = x_pivot; // Contract range down A (up B)
}
path_coordinate.x = std::min(x_min, a_len);
path_coordinate.y = diagonal - x_min;
}
//---------------------------------------------------------------------
// SpMV verification
//---------------------------------------------------------------------
// Compute reference SpMV y = Ax
template <
typename ValueT,
typename OffsetT>
void SpmvGold(
CsrMatrix<ValueT, OffsetT>& a,
ValueT* vector_x,
ValueT* vector_y_in,
ValueT* vector_y_out,
ValueT alpha,
ValueT beta)
{
for (OffsetT row = 0; row < a.num_rows; ++row)
{
ValueT partial = beta * vector_y_in[row];
for (
OffsetT offset = a.row_offsets[row];
offset < a.row_offsets[row + 1];
++offset)
{
partial += alpha * a.values[offset] * vector_x[a.column_indices[offset]];
}
vector_y_out[row] = partial;
}
}
//---------------------------------------------------------------------
// CPU normal omp SpMV
//---------------------------------------------------------------------
template <
typename ValueT,
typename OffsetT>
void OmpCsrSpmmT(
int num_threads,
CsrMatrix<ValueT, OffsetT>& a,
ValueT* __restrict vector_x,
ValueT* __restrict vector_y_out,
int num_vectors,
ValueT* __restrict vector_x_row_major)
{
int num_cols = a.num_cols;
int num_rows = a.num_rows;
int xt_index = 0;
if (!g_input_row_major)
{
#pragma omp parallel for schedule(static) num_threads(num_threads) private(xt_index)
for (int i=0; i<num_cols; i++){
for(int j=i; j<num_cols*num_vectors; j+=num_cols){
vector_x_row_major[xt_index] = vector_x[j];
xt_index += 1;
}
}
}
#pragma omp parallel for schedule(static) num_threads(num_threads)
for (OffsetT row = 0; row < a.num_rows; ++row)
{
ValueT partial[num_vectors] = {0.0};
for (
OffsetT offset = a.row_offsets[row];
offset < a.row_offsets[row + 1];
++offset)
{
ValueT val = a.values[offset];
int ind = a.column_indices[offset]*num_vectors;
for (int i=0; i<num_vectors; i++){
partial[i] += val * vector_x_row_major[ind + i];
}
}
if (g_output_row_major)
{
int ind = row*num_vectors;
for (int i=0; i<num_vectors; i++){
vector_y_out[ind + i] = partial[i];
}
}
else
{
for (int i=0; i<num_vectors; i++){
vector_y_out[row + i * num_rows] = partial[i];
}
}
}
}
/**
* Run OmpCsrSpmm
*/
template <
typename ValueT,
typename OffsetT>
float TestOmpCsrSpmmT(
CsrMatrix<ValueT, OffsetT>& a,
ValueT* vector_x,
ValueT* reference_vector_y_out,
ValueT* vector_y_out,
int timing_iterations,
float &setup_ms,
int num_vectors,
ValueT* vector_x_row_major)
{
setup_ms = 0.0;
if (g_omp_threads == -1)
g_omp_threads = omp_get_num_procs();
int num_threads = g_omp_threads;
if (!g_quiet)
printf("\tUsing %d threads on %d procs\n", g_omp_threads, omp_get_num_procs());
// Warmup/correctness
memset(vector_y_out, -1, sizeof(ValueT) * a.num_rows * num_vectors);
OmpCsrSpmmT(g_omp_threads, a, vector_x, vector_y_out, num_vectors, vector_x_row_major);
if (!g_quiet)
{
// Check answer
int compare = CompareResults(reference_vector_y_out, vector_y_out, a.num_rows, true);
printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
if (g_input_row_major || g_output_row_major){
for(int i=0; i<a.num_rows; i++){
ValueT diff = reference_vector_y_out[i] - vector_y_out[i*num_vectors];
if(diff>0.000001 || diff<-0.0000001){
printf("%lf, %lf\n",reference_vector_y_out[i],vector_y_out[i*num_vectors]);
}
}
}
}
// Re-populate caches, etc.
for(int it = 0; it < timing_iterations; ++it)
{
OmpCsrSpmmT(g_omp_threads, a, vector_x, vector_y_out, num_vectors, vector_x_row_major);
}
// Timing
float elapsed_ms = 0.0;
CpuTimer timer;
timer.Start();
for(int it = 0; it < timing_iterations; ++it)
{
OmpCsrSpmmT(g_omp_threads, a, vector_x, vector_y_out, num_vectors, vector_x_row_major);
}
timer.Stop();
elapsed_ms += timer.ElapsedMillis();
return elapsed_ms / timing_iterations;
}
//---------------------------------------------------------------------
// MKL SpMV
//---------------------------------------------------------------------
/**
* MKL CPU SpMV (specialized for fp32)
*/
template <typename OffsetT>
void MKLCsrmm(
int num_threads,
CsrMatrix<float, OffsetT>& a,
OffsetT* __restrict row_end_offsets, ///< Merge list A (row end-offsets)
OffsetT* __restrict column_indices,
float* __restrict values,
float* __restrict vector_x,
float* __restrict vector_y_out,
int num_vectors)
{
struct matrix_descr A_descr;
A_descr.type = SPARSE_MATRIX_TYPE_GENERAL;
sparse_matrix_t csrA;
mkl_sparse_s_create_csr( &csrA, SPARSE_INDEX_BASE_ZERO, a.num_rows, a.num_cols, a.row_offsets, row_end_offsets, a.column_indices, a.values);
mkl_sparse_s_mm(SPARSE_OPERATION_NON_TRANSPOSE, 1.0, csrA, A_descr, SPARSE_LAYOUT_ROW_MAJOR, vector_x, num_vectors, num_vectors, 0.0, vector_y_out, num_vectors);
}
/**
* MKL CPU SpMV (specialized for fp64)
*/
template <typename OffsetT>
void MKLCsrmm(
int num_threads,
CsrMatrix<double, OffsetT>& a,
OffsetT* __restrict row_end_offsets, ///< Merge list A (row end-offsets)
OffsetT* __restrict column_indices,
double* __restrict values,
double* __restrict vector_x,
double* __restrict vector_y_out,
int num_vectors)
{
struct matrix_descr A_descr;
A_descr.type = SPARSE_MATRIX_TYPE_GENERAL;
sparse_matrix_t csrA;
mkl_sparse_d_create_csr( &csrA, SPARSE_INDEX_BASE_ZERO, a.num_rows, a.num_cols, a.row_offsets, row_end_offsets, a.column_indices, a.values);
mkl_sparse_d_mm(SPARSE_OPERATION_NON_TRANSPOSE, 1.0, csrA, A_descr, SPARSE_LAYOUT_ROW_MAJOR, vector_x, num_vectors, num_vectors, 0.0, vector_y_out, num_vectors);
}
/**
* Run MKL CsrMV
*/
template <
typename ValueT,
typename OffsetT>
float TestMKLCsrmm(
CsrMatrix<ValueT, OffsetT>& a,
ValueT* vector_x,
ValueT* reference_vector_y_out,
ValueT* vector_y_out,
int timing_iterations,
float &setup_ms,
int num_vectors)
{
setup_ms = 0.0;
// Warmup/correctness
memset(vector_y_out, -1, sizeof(ValueT) * a.num_rows * num_vectors);
MKLCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors);
if (!g_quiet)
{
// Check answer
int compare = CompareResults(reference_vector_y_out, vector_y_out, a.num_rows, true);
printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
}
// Re-populate caches, etc.
for(int it = 0; it < timing_iterations; ++it)
{
MKLCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors);
}
// Timing
float elapsed_ms = 0.0;
CpuTimer timer;
timer.Start();
for(int it = 0; it < timing_iterations; ++it)
{
MKLCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors);
}
timer.Stop();
elapsed_ms += timer.ElapsedMillis();
return elapsed_ms / timing_iterations;
}
//---------------------------------------------------------------------
// CPU merge-based SpMV
//---------------------------------------------------------------------
/**
* OpenMP CPU merge-based SpMV
*/
template <
typename ValueT,
typename OffsetT>
void OmpMergeCsrmm(
int num_threads,
CsrMatrix<ValueT, OffsetT>& a,
OffsetT* __restrict row_end_offsets, ///< Merge list A (row end-offsets)
OffsetT* __restrict column_indices,
ValueT* __restrict values,
ValueT* __restrict vector_x,
ValueT* __restrict vector_y_out,
int num_vectors,
ValueT* __restrict vector_x_row_major)
{
// Temporary storage for inter-thread fix-up after load-balanced work
OffsetT row_carry_out[256]; // The last row-id each worked on by each thread when it finished its path segment
ValueT* value_carry_out[256]; // The running total within each thread when it finished its path segment
int num_cols = a.num_cols;
int num_rows = a.num_rows;
int xt_index = 0;
#pragma omp parallel for schedule(static) num_threads(num_threads)
for (int tid = 0; tid < num_threads; tid++)
{
// Merge list B (NZ indices)
CountingInputIterator<OffsetT> nonzero_indices(0);
OffsetT num_merge_items = a.num_rows + a.num_nonzeros; // Merge path total length
OffsetT items_per_thread = (num_merge_items + num_threads - 1) / num_threads; // Merge items per thread
// Find starting and ending MergePath coordinates (row-idx, nonzero-idx) for each thread
int2 thread_coord;
int2 thread_coord_end;
int start_diagonal = std::min(items_per_thread * tid, num_merge_items);
int end_diagonal = std::min(start_diagonal + items_per_thread, num_merge_items);
MergePathSearch(start_diagonal, row_end_offsets, nonzero_indices, a.num_rows, a.num_nonzeros, thread_coord);
MergePathSearch(end_diagonal, row_end_offsets, nonzero_indices, a.num_rows, a.num_nonzeros, thread_coord_end);
// Consume whole rows
ValueT running_total[num_vectors] = {0.0};
ValueT val;
int ind;
ValueT* tmp;
for (; thread_coord.x < thread_coord_end.x; ++thread_coord.x)
{
for (; thread_coord.y < row_end_offsets[thread_coord.x]; ++thread_coord.y)
{
val = values[thread_coord.y];
ind = column_indices[thread_coord.y]*num_vectors;
tmp = vector_x_row_major+ind;
for (int i=0; i<num_vectors; i++){
running_total[i] += val * tmp[i];
}
}
ind = thread_coord.x*num_vectors;
tmp = vector_y_out+ind;
for (int i=0; i<num_vectors; i++){
tmp[i] = running_total[i];
running_total[i] = 0.0;
}
}
// Consume partial portion of thread's last row
for (; thread_coord.y < thread_coord_end.y; ++thread_coord.y)
{
val = values[thread_coord.y];
ind = column_indices[thread_coord.y]*num_vectors;
tmp = vector_x_row_major+ind;
for (int i=0; i<num_vectors; i++){
running_total[i] += val * tmp[i];
}
}
// Save carry-outs
row_carry_out[tid] = thread_coord_end.x;
value_carry_out[tid] = running_total;
}
// Carry-out fix-up (rows spanning multiple threads)
int ind;
ValueT* tmp;
for (int tid = 0; tid < num_threads - 1; ++tid)
{
if (row_carry_out[tid] < a.num_rows){
ind = row_carry_out[tid]*num_vectors;
tmp = vector_y_out+ind;
for (int i=0; i<num_vectors; i++){
tmp[i] += value_carry_out[tid][i];
}
}
}
}
/**
* Run OmpMergeCsrmm
*/
template <
typename ValueT,
typename OffsetT>
float TestOmpMergeCsrmm(
CsrMatrix<ValueT, OffsetT>& a,
ValueT* vector_x,
ValueT* reference_vector_y_out,
ValueT* vector_y_out,
int timing_iterations,
float &setup_ms,
int num_vectors,
ValueT* vector_x_row_major)
{
setup_ms = 0.0;
if (g_omp_threads == -1)
g_omp_threads = omp_get_num_procs();
int num_threads = g_omp_threads;
if (!g_quiet)
printf("\tUsing %d threads on %d procs\n", g_omp_threads, omp_get_num_procs());
// Warmup/correctness
memset(vector_y_out, -1, sizeof(ValueT) * a.num_rows * num_vectors);
OmpMergeCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors, vector_x_row_major);
if (!g_quiet)
{
// Check answer
int compare = CompareResults(reference_vector_y_out, vector_y_out, a.num_rows, true);
printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
}
// Re-populate caches, etc.
for(int it = 0; it < timing_iterations; ++it)
{
OmpMergeCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors, vector_x_row_major);
}
// Timing
float elapsed_ms = 0.0;
CpuTimer timer;
timer.Start();
for(int it = 0; it < timing_iterations; ++it)
{
OmpMergeCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors, vector_x_row_major);
}
timer.Stop();
elapsed_ms += timer.ElapsedMillis();
return elapsed_ms / timing_iterations;
}
template <
typename AIteratorT,
typename BIteratorT,
typename OffsetT,
typename CoordinateT>
inline void RowPathSearch(
AIteratorT a, ///< [in]List A
BIteratorT b, ///< [in]List B
OffsetT a_len, ///< [in]Length of A
CoordinateT& path_coordinate) ///< [out] (x,y) coordinate where diagonal intersects the merge path
{
OffsetT x_min = 0;
OffsetT x_max = a_len;
while (x_min < x_max)
{
OffsetT x_pivot = (x_min + x_max) >> 1;
if (a[x_pivot] <= b[path_coordinate.y - 1])
x_min = x_pivot + 1; // Contract range up A (down B)
else
x_max = x_pivot; // Contract range down A (up B)
}
path_coordinate.x = std::min(x_min, a_len);
}
/**
* OpenMP CPU row-based SpMM
*/
template <
typename ValueT,
typename OffsetT>
void OmpNonzeroSplitCsrmm(
int num_threads,
CsrMatrix<ValueT, OffsetT>& a,
OffsetT* __restrict row_end_offsets, ///< Merge list A (row end-offsets)
OffsetT* __restrict column_indices,
ValueT* __restrict values,
ValueT* __restrict vector_x,
ValueT* __restrict vector_y_out,
int num_vectors,
ValueT* __restrict vector_x_row_major)
{
// Temporary storage for inter-thread fix-up after load-balanced work
OffsetT row_carry_out[256]; // The last row-id each worked on by each thread when it finished its path segment
ValueT* value_carry_out[256]; // The running total within each thread when it finished its path segment
int num_cols = a.num_cols;
int num_rows = a.num_rows;
int xt_index = 0;
#pragma omp parallel for schedule(static) num_threads(num_threads)
for (int tid = 0; tid < num_threads; tid++)
{
// Merge list B (NZ indices)
CountingInputIterator<OffsetT> nonzero_indices(0);
OffsetT num_nonzeros = a.num_nonzeros;
OffsetT items_per_thread = (num_nonzeros + num_threads - 1) / num_threads;
// Find starting and ending MergePath coordinates (row-idx, nonzero-idx) for each thread
int2 thread_coord;
int2 thread_coord_end;
thread_coord.y = std::min(items_per_thread * tid, num_nonzeros);
thread_coord_end.y = std::min(thread_coord.y + items_per_thread, num_nonzeros);
RowPathSearch(row_end_offsets, nonzero_indices, a.num_rows, thread_coord);
RowPathSearch(row_end_offsets, nonzero_indices, a.num_rows, thread_coord_end);
// Consume whole rows
ValueT running_total[num_vectors] = {0.0};
ValueT val;
ValueT* tmp;
int ind;
for (; thread_coord.x < thread_coord_end.x; ++thread_coord.x)
{
for (; thread_coord.y < row_end_offsets[thread_coord.x]; ++thread_coord.y)
{
val = values[thread_coord.y];
ind = column_indices[thread_coord.y]*num_vectors;
tmp = vector_x_row_major+ind;
for (int i=0; i<num_vectors; i++){
running_total[i] += val * tmp[i];
}
}
ind = thread_coord.x*num_vectors;
tmp = vector_y_out+ind;
for (int i=0; i<num_vectors; i++){
tmp[i] = running_total[i];
running_total[i] = 0.0;
}
}
// Consume partial portion of thread's last row
for (; thread_coord.y < thread_coord_end.y; ++thread_coord.y)
{
val = values[thread_coord.y];
ind = column_indices[thread_coord.y]*num_vectors;
tmp = vector_x_row_major+ind;
for (int i=0; i<num_vectors; i++){
running_total[i] += val * tmp[i];
}
}
// Save carry-outs
row_carry_out[tid] = thread_coord_end.x;
value_carry_out[tid] = running_total;
}
// Carry-out fix-up (rows spanning multiple threads)
int ind;
ValueT* tmp;
for (int tid = 0; tid < num_threads - 1; ++tid)
{
if (row_carry_out[tid] < a.num_rows){
ind = row_carry_out[tid]*num_vectors;
tmp = vector_y_out+ind;
for (int i=0; i<num_vectors; i++){
tmp[i] += value_carry_out[tid][i];
}
}
}
}
template <
typename ValueT,
typename OffsetT>
float TestOmpNonzeroSplitCsrmm(
CsrMatrix<ValueT, OffsetT>& a,
ValueT* vector_x,
ValueT* reference_vector_y_out,
ValueT* vector_y_out,
int timing_iterations,
float &setup_ms,
int num_vectors,
ValueT* vector_x_row_major)
{
setup_ms = 0.0;
if (g_omp_threads == -1)
g_omp_threads = omp_get_num_procs();
int num_threads = g_omp_threads;
if (!g_quiet)
printf("\tUsing %d threads on %d procs\n", g_omp_threads, omp_get_num_procs());
// Warmup/correctness
memset(vector_y_out, -1, sizeof(ValueT) * a.num_rows * num_vectors);
OmpNonzeroSplitCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors, vector_x_row_major);
if (!g_quiet)
{
// Check answer
int compare = CompareResults(reference_vector_y_out, vector_y_out, a.num_rows, true);
printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
if (g_input_row_major == g_output_row_major){
for(int i=0; i<a.num_rows; i++){
ValueT diff = reference_vector_y_out[i] - vector_y_out[i*num_vectors];
if(diff>0.000001 || diff<-0.0000001){
printf("%lf, %lf, %d\n",reference_vector_y_out[i],vector_y_out[i*num_vectors],i);
}
}
}
}
// Re-populate caches, etc.
for(int it = 0; it < timing_iterations; ++it)
{
OmpNonzeroSplitCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors, vector_x_row_major);
}
// Timing
float elapsed_ms = 0.0;
CpuTimer timer;
timer.Start();
for(int it = 0; it < timing_iterations; ++it)
{
OmpNonzeroSplitCsrmm(g_omp_threads, a, a.row_offsets + 1, a.column_indices, a.values, vector_x, vector_y_out, num_vectors, vector_x_row_major);
}
timer.Stop();
elapsed_ms += timer.ElapsedMillis();
return elapsed_ms / timing_iterations;
}
//---------------------------------------------------------------------
// Test generation
//---------------------------------------------------------------------
/**
* Display perf
*/
template <typename ValueT, typename OffsetT>
void DisplayPerf(
double setup_ms,
double avg_ms,
CsrMatrix<ValueT, OffsetT>& csr_matrix,
int num_vectors)
{
double nz_throughput, effective_bandwidth;
size_t total_bytes = (csr_matrix.num_nonzeros * (sizeof(ValueT) * 2 + sizeof(OffsetT))) +
(csr_matrix.num_rows * num_vectors) * (sizeof(OffsetT) + sizeof(ValueT));
nz_throughput = double(csr_matrix.num_nonzeros)*double(num_vectors) / avg_ms / 1.0e6;
effective_bandwidth = double(total_bytes) / avg_ms / 1.0e6;
if (!g_quiet)
printf("fp%d: %.4f setup ms, %.4f avg ms, %.5f gflops, %.3lf effective GB/s\n",
int(sizeof(ValueT) * 8),
setup_ms,
avg_ms,
2 * nz_throughput,
effective_bandwidth);
else
printf("%.5f, %.5f, %.6f, %.3lf, ",
setup_ms, avg_ms,
2 * nz_throughput,
effective_bandwidth);
fflush(stdout);
}
/**
* Run tests
*/
template <
typename ValueT,
typename OffsetT>
void RunTests(
ValueT alpha,
ValueT beta,
const std::string& mtx_filename,
int grid2d,
int grid3d,
int wheel,
int dense,
int timing_iterations,
int num_vectors,
CommandLineArgs& args)
{
// Initialize matrix in COO form
CooMatrix<ValueT, OffsetT> coo_matrix;
if (!mtx_filename.empty())
{
// Parse matrix market file
coo_matrix.InitMarket(mtx_filename, 1.0, !g_quiet);
if ((coo_matrix.num_rows == 1) || (coo_matrix.num_cols == 1) || (coo_matrix.num_nonzeros == 1))
{
if (!g_quiet) printf("Trivial dataset\n");
exit(0);
}
printf("%s, ", mtx_filename.c_str()); fflush(stdout);
}
else if (grid2d > 0)
{
// Generate 2D lattice
printf("grid2d_%d, ", grid2d); fflush(stdout);
coo_matrix.InitGrid2d(grid2d, false);
}
else if (grid3d > 0)
{
// Generate 3D lattice
printf("grid3d_%d, ", grid3d); fflush(stdout);
coo_matrix.InitGrid3d(grid3d, false);
}
else if (wheel > 0)
{
// Generate wheel graph
printf("wheel_%d, ", grid2d); fflush(stdout);
coo_matrix.InitWheel(wheel);
}
else if (dense > 0)
{
// Generate dense graph
OffsetT rows = (1<<24) / dense; // 16M nnz
printf("dense_%d_x_%d, ", rows, dense); fflush(stdout);
coo_matrix.InitDense(rows, dense);
}
else
{
fprintf(stderr, "No graph type specified.\n");
exit(1);
}
CsrMatrix<ValueT, OffsetT> csr_matrix(coo_matrix);
coo_matrix.Clear();
// Display matrix info
csr_matrix.Stats().Display(!g_quiet);
if (!g_quiet)
{
printf("\n");
csr_matrix.DisplayHistogram();
printf("\n");
if (g_verbose2)
csr_matrix.Display();
printf("\n");
}
fflush(stdout);
// count max-min row nums
if (!g_quiet){
int max=0;
int min=100000000;
for(int i=0; i < csr_matrix.num_cols; i++){
int diff = csr_matrix.row_offsets[i+1] - csr_matrix.row_offsets[i];
if (diff > max){
max = diff;
}
if (diff < min){
min = diff;
}
}
printf("min:%d, max:%d\n", min, max);
}
// Determine # of timing iterations (aim to run 16 billion nonzeros through, total)
if (timing_iterations == -1)
{
timing_iterations = std::min(1000ull, std::max(10ull, ((16ull << 30) / (csr_matrix.num_nonzeros * num_vectors))));
timing_iterations = timing_iterations;
if (timing_iterations<2){
timing_iterations = 3;
}
if (!g_quiet)
printf("\t%d timing iterations\n", timing_iterations);
}
// Allocate input and output vectors (if available, use NUMA allocation to force storage on the
// sockets for performance consistency)
ValueT *vector_x, *vector_y_in, *reference_vector_y_out, *vector_y_out, *vector_x_row_major;
if (csr_matrix.IsNumaMalloc())
{
vector_x = (ValueT*) numa_alloc_onnode(sizeof(ValueT) * csr_matrix.num_cols * num_vectors, 0);