/
optimization.cu
1291 lines (1048 loc) · 45.8 KB
/
optimization.cu
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// General
#include <iostream>
#include <algorithm>
#include <sstream>
// Warpkernel
#include "warpkernel.hpp"
// cusp
#include <cusp/coo_matrix.h>
#include <cusp/io/matrix_market.h>
#include <cusp/csr_matrix.h>
#include <cusp/multiply.h>
#include <cusp/detail/timer.h>
#include <cusp/hyb_matrix.h>
// boost
// stats
#include <boost/accumulators/accumulators.hpp>
#include <boost/accumulators/statistics/stats.hpp>
#include <boost/accumulators/statistics/mean.hpp>
#define DeviceSpace cusp::device_memory
#define CPUSpace cusp::host_memory
struct rand_float {
double operator() ()
{
return 1.1;//((double)(rand() % 100))/100. -0.3;
}
};
template <bool usecache>
__inline__ __device__ double fetch_cache(const int& i, const double* x) {
if (usecache) {
int2 v = tex1Dfetch(x_tex,i);
return __hiloint2double(v.y, v.x);
} else {
return x[i];
}
}
/**********/
template <bool usecache, typename ValueType, typename IndexType>
__global__ void warpKernel2_noregister(IndexType nrows, int nwarps, ValueType* A, IndexType *colinds,
IndexType *rowmap, uint* maxrows, IndexType *warp_offset,
uint* reduction, uint* rows_offset_warp,
ValueType* x , ValueType* y) {
const uint tid = threadIdx.x;
const uint id = tid + blockIdx.x * blockDim.x;
const uint wid = tid & (WARP_SIZE-1);
const uint warpid = id / WARP_SIZE;
extern volatile __shared__ ValueType sumvalues[];
if (warpid >= nwarps) return;
const uint offsets = reduction[warpid];
const uint row_start = rows_offset_warp[warpid];
const uint rowid = row_start + wid/offsets;
if (rowid < nrows) {
IndexType toffset = warp_offset[warpid] + wid;
const uint maxnz = maxrows[warpid] * WARP_SIZE + toffset;
ValueType sum = A[toffset] * fetch_cache<usecache> (colinds[toffset],x);
for(toffset += WARP_SIZE; toffset<maxnz; toffset += WARP_SIZE) {
sum += A[toffset] * fetch_cache<usecache> (colinds[toffset],x);
}
sumvalues[tid] = sum;
// // possible reduction
for (int i = 1; i< offsets; i <<= 1) {
if (offsets > i ) {
sumvalues[tid] += sumvalues[tid+i];
}
}
if ((wid & (offsets-1)) == 0) {
y[rowmap[rowid]] = sumvalues[tid];
}
}
}
/**********/
template <bool usecache, typename ValueType, typename IndexType >
__global__ void warpKernel_nocoalesced(uint nrows, ValueType* A, IndexType *colinds,
IndexType *rowmap, uint* maxrows, IndexType *warp_offset,
ValueType* x, ValueType* y) {
const uint tid = threadIdx.x;
const uint id = tid + blockIdx.x * blockDim.x;
const uint wid = tid & (WARP_SIZE-1);
const uint warpid = id / WARP_SIZE;
if (id < nrows) {
uint maxnz = maxrows[warpid];
IndexType toffset = warp_offset[warpid] + wid * maxnz;
maxnz += toffset;
ValueType sum = A[toffset] * fetch_cache<usecache> (colinds[toffset],x);
for(toffset ++; toffset < maxnz; toffset ++) {
sum += A[toffset] * fetch_cache<usecache> (colinds[toffset],x);
}
y[rowmap[id]] = sum;
}
}
/*********/
template<typename R>
bool verify(R orig, R comp, uint nrows) {
bool check = true;
for (int i=0; i< nrows; i++) {
if (abs((orig[i]-comp[i])/orig[i]) > 1E-5) {
std::cout << orig[i] << "\t" << comp[i] << "\t" << i << std::endl;
check = false;
return check;
}
}
return check;
}
template<typename R>
bool verify_x(R orig, R comp, uint nrows, int *row_map) {
bool temp = true;
for (int i=0; i< nrows; i++) {
if (abs((orig[row_map[i]]-comp[i])/orig[row_map[i]]) > 1E-5) {
std::cout << orig[row_map[i]] << "," << comp[i] << " : " << i << std::endl;
temp = false;
return temp;
}
}
return temp;
}
bool checkPairOrder(std::pair<uint,uint> pair1, std::pair<uint,uint> pair2) {
return pair1.second > pair2.second ||
(pair1.second == pair2.second && pair1.first < pair2.first);
}
template<typename ValueType>
void sort(ValueType *rows, std::vector<std::pair<uint,uint> > & nnz_r, uint & nwarps, uint nrows) {
nwarps = (nrows + WARP_SIZE-1)/(WARP_SIZE);
nnz_r.resize(nrows);
uint nznrows = 0;
// Re-arrange rows to reach our assumptions
for (int w = 0; w < nwarps; w++) {
for (int r = WARP_SIZE * w; r < nrows && r < WARP_SIZE*(w+1); r++) {
uint rowsize = rows[r+1] - rows[r];
nnz_r[r] = std::make_pair(r,rowsize);
if (rowsize > 0)
nznrows++;
}
}
// sort by rowsize
std::sort( nnz_r.begin(), nnz_r.end(), checkPairOrder);
}
template <typename ValueType, typename IndexType>
void scan(uint & nz, uint & nrows,
ValueType * A, IndexType * rows, IndexType *colinds,
uint & nwarps, uint & allocate_nz,
std::vector<uint> & reorder_rows, // new_values[reorder_rows[i]] = A[i]
std::vector<int> & warp_offsets,
std::vector<uint> & max_nz_rows,
std::vector<int> &row_map_,
std::vector<int> &row_map_inv_, uint &nznrows) {
std::vector<std::pair<uint,uint> > nnz_r; // non-zeros per row
sort(rows, nnz_r, nwarps, nrows);
std::vector<int> row_map(nrows);
for(int r = 0; r < nrows; r++)
row_map[r] = nnz_r[r].first;
row_map_ = row_map;
std::vector<int> row_map_inv(nrows);
for(int i=0;i<nrows;i++) {
row_map_inv[row_map[i]] = i;
}
row_map_inv_ = row_map_inv;
std::vector<uint> A_w(nwarps); // max non-zeros per row
std::vector<uint> nnz_imin(nwarps,nrows); // minimum non-zeros per row
std::vector<uint> nnz_imax(nwarps); // maximum non-zeros per row
// Use sorted row-sizes to calculate A_w, nnz_w, etc.
for (int w = 0; w < nwarps; w++) {
for (int r = WARP_SIZE * w; r < nrows && r < WARP_SIZE*(w+1); r++) {
uint rowsize = nnz_r[r].second;
if (rowsize < nnz_imin[w]) nnz_imin[w] = rowsize; // min
if (rowsize > nnz_imax[w]) nnz_imax[w] = rowsize; // max
}
A_w[w] = nnz_imax[w];
}
max_nz_rows = A_w;
// set warp_offsets and allocate_nz;
warp_offsets.resize(nwarps+1);
warp_offsets[0] = 0;
for(int w = 0; w < nwarps; w++) {
warp_offsets[w+1] = warp_offsets[w] + A_w[w] * WARP_SIZE;
}
allocate_nz = warp_offsets[nwarps];
// Generate reordering map for future use
reorder_rows.resize(nz);
for (int w_s = 0; w_s < nwarps; w_s++) {
for (int r_s = WARP_SIZE * w_s; r_s < nrows
&& r_s < WARP_SIZE * (w_s+1); r_s++) {
int r = nnz_r[r_s].first;
int rowsize = nnz_r[r_s].second;
for(int i = 0; i < rowsize; i++) {
reorder_rows[rows[r] + i] =
warp_offsets[w_s] + (r_s % WARP_SIZE) + i*WARP_SIZE;
}
}
}
}
/******* scan no reorder coalesce ****/
template <typename ValueType, typename IndexType>
void scan_nocoalesced(uint & nz, uint & nrows,
ValueType * A, IndexType * rows, IndexType *colinds,
uint & nwarps, uint & allocate_nz,
std::vector<uint> & reorder_rows, // new_values[reorder_rows[i]] = A[i]
std::vector<int> & warp_offsets,
std::vector<uint> & max_nz_rows,
std::vector<int> &row_map_,
std::vector<int> &row_map_inv_) {
std::vector<std::pair<uint,uint> > nnz_r; // non-zeros per row
sort(rows, nnz_r, nwarps, nrows);
std::vector<int> row_map(nrows);
for(int r = 0; r < nrows; r++)
row_map[r] = nnz_r[r].first;
row_map_ = row_map;
std::vector<int> row_map_inv(nrows);
for(int i=0;i<nrows;i++) {
row_map_inv[row_map[i]] = i;
}
row_map_inv_ = row_map_inv;
std::vector<uint> A_w(nwarps); // max non-zeros per row
std::vector<uint> nnz_imin(nwarps,nrows); // minimum non-zeros per row
std::vector<uint> nnz_imax(nwarps); // maximum non-zeros per row
// Use sorted row-sizes to calculate A_w, nnz_w, etc.
for (int w = 0; w < nwarps; w++) {
for (int r = WARP_SIZE * w; r < nrows && r < WARP_SIZE*(w+1); r++) {
uint rowsize = nnz_r[r].second;
if (rowsize < nnz_imin[w]) nnz_imin[w] = rowsize; // min
if (rowsize > nnz_imax[w]) nnz_imax[w] = rowsize; // max
}
A_w[w] = nnz_imax[w];
}
max_nz_rows = A_w;
// set warp_offsets and allocate_nz;
warp_offsets.resize(nwarps+1);
warp_offsets[0] = 0;
for(int w = 0; w < nwarps; w++) {
warp_offsets[w+1] = warp_offsets[w] + A_w[w] * WARP_SIZE;
}
allocate_nz = warp_offsets[nwarps];
// Generate reordering map for future use
reorder_rows.resize(nz);
for (int w_s = 0; w_s < nwarps; w_s++) {
for (int r_s = WARP_SIZE * w_s; r_s < nrows
&& r_s < WARP_SIZE * (w_s+1); r_s++) {
int r = nnz_r[r_s].first;
int rowsize = nnz_r[r_s].second;
for(int i = 0; i < rowsize; i++) {
reorder_rows[rows[r] + i] =
warp_offsets[w_s] + (r_s % WARP_SIZE) * max_nz_rows[w_s] + i; // undid reodering
}
}
}
}
/******* scan no reorder coalesce no sort ****/
template <typename ValueType, typename IndexType>
void scan_nocoalesced_nosort(uint & nz, uint & nrows,
ValueType * A, IndexType * rows, IndexType *colinds,
uint & nwarps, uint & allocate_nz,
std::vector<uint> & reorder_rows, // new_values[reorder_rows[i]] = A[i]
std::vector<int> & warp_offsets,
std::vector<uint> & max_nz_rows,
std::vector<int> &row_map_,
std::vector<int> &row_map_inv_) {
std::vector<std::pair<uint,uint> > nnz_r; // non-zeros per row
nwarps = (nrows + WARP_SIZE-1)/(WARP_SIZE);
// Re-arrange rows to reach our assumptions
for (int w = 0; w < nwarps; w++) {
for (int r = WARP_SIZE * w; r < nrows && r < WARP_SIZE*(w+1); r++) {
uint rowsize = rows[r+1] - rows[r];
if (rowsize > 0)
nnz_r.push_back(std::make_pair(r,rowsize));
}
}
std::vector<int> row_map(nrows);
for(int r = 0; r < nrows; r++)
row_map[r] = nnz_r[r].first;
row_map_ = row_map;
std::vector<int> row_map_inv(nrows);
for(int i=0;i<nrows;i++) {
row_map_inv[row_map[i]] = i;
}
row_map_inv_ = row_map_inv;
std::vector<uint> A_w(nwarps); // max non-zeros per row
std::vector<uint> nnz_imin(nwarps,nrows); // minimum non-zeros per row
std::vector<uint> nnz_imax(nwarps); // maximum non-zeros per row
// Use sorted row-sizes to calculate A_w, nnz_w, etc.
for (int w = 0; w < nwarps; w++) {
for (int r = WARP_SIZE * w; r < nrows && r < WARP_SIZE*(w+1); r++) {
uint rowsize = nnz_r[r].second;
if (rowsize < nnz_imin[w]) nnz_imin[w] = rowsize; // min
if (rowsize > nnz_imax[w]) nnz_imax[w] = rowsize; // max
}
A_w[w] = nnz_imax[w];
}
max_nz_rows = A_w;
// set warp_offsets and allocate_nz;
warp_offsets.resize(nwarps+1);
warp_offsets[0] = 0;
for(int w = 0; w < nwarps; w++) {
warp_offsets[w+1] = warp_offsets[w] + A_w[w] * WARP_SIZE;
}
allocate_nz = warp_offsets[nwarps];
// Generate reordering map for future use
reorder_rows.resize(nz);
for (int w_s = 0; w_s < nwarps; w_s++) {
for (int r_s = WARP_SIZE * w_s; r_s < nrows
&& r_s < WARP_SIZE * (w_s+1); r_s++) {
int r = nnz_r[r_s].first;
int rowsize = nnz_r[r_s].second;
for(int i = 0; i < rowsize; i++) {
reorder_rows[rows[r] + i] =
warp_offsets[w_s] + (r_s % WARP_SIZE) * max_nz_rows[w_s] + i; // undid reodering
}
}
}
}
/******* scan no sort ****/
template <typename ValueType, typename IndexType>
void scan_nosort(uint & nz, uint & nrows,
ValueType * A, IndexType * rows, IndexType *colinds,
uint & nwarps, uint & allocate_nz,
std::vector<uint> & reorder_rows, // new_values[reorder_rows[i]] = A[i]
std::vector<int> & warp_offsets,
std::vector<uint> & max_nz_rows,
std::vector<int> &row_map_,
std::vector<int> &row_map_inv_) {
std::vector<std::pair<uint,uint> > nnz_r; // non-zeros per row
nwarps = (nrows + WARP_SIZE-1)/(WARP_SIZE);
// Re-arrange rows to reach our assumptions
for (int w = 0; w < nwarps; w++) {
for (int r = WARP_SIZE * w; r < nrows && r < WARP_SIZE*(w+1); r++) {
uint rowsize = rows[r+1] - rows[r];
if (rowsize > 0)
nnz_r.push_back(std::make_pair(r,rowsize));
}
}
std::vector<int> row_map(nrows);
for(int r = 0; r < nrows; r++)
row_map[r] = nnz_r[r].first;
row_map_ = row_map;
std::vector<int> row_map_inv(nrows);
for(int i=0;i<nrows;i++) {
row_map_inv[row_map[i]] = i;
}
row_map_inv_ = row_map_inv;
std::vector<uint> A_w(nwarps); // max non-zeros per row
std::vector<uint> nnz_imin(nwarps,nrows); // minimum non-zeros per row
std::vector<uint> nnz_imax(nwarps); // maximum non-zeros per row
// Use sorted row-sizes to calculate A_w, nnz_w, etc.
for (int w = 0; w < nwarps; w++) {
for (int r = WARP_SIZE * w; r < nrows && r < WARP_SIZE*(w+1); r++) {
uint rowsize = nnz_r[r].second;
if (rowsize < nnz_imin[w]) nnz_imin[w] = rowsize; // min
if (rowsize > nnz_imax[w]) nnz_imax[w] = rowsize; // max
}
A_w[w] = nnz_imax[w];
}
max_nz_rows = A_w;
// set warp_offsets and allocate_nz;
warp_offsets.resize(nwarps+1);
warp_offsets[0] = 0;
for(int w = 0; w < nwarps; w++) {
warp_offsets[w+1] = warp_offsets[w] + A_w[w] * WARP_SIZE;
}
allocate_nz = warp_offsets[nwarps];
// Generate reordering map for future use
reorder_rows.resize(nz);
for (int w_s = 0; w_s < nwarps; w_s++) {
for (int r_s = WARP_SIZE * w_s; r_s < nrows
&& r_s < WARP_SIZE * (w_s+1); r_s++) {
int r = nnz_r[r_s].first;
int rowsize = nnz_r[r_s].second;
for(int i = 0; i < rowsize; i++) {
reorder_rows[rows[r] + i] =
warp_offsets[w_s] + (r_s % WARP_SIZE) + i*WARP_SIZE;
}
}
}
}
/********* Reorder */
template<typename IndexType, typename ValueType>
void reorder(ValueType *A, IndexType * colinds,
cusp::array1d<ValueType, DeviceSpace> &device_values, // allocate nz
cusp::array1d<IndexType, DeviceSpace> &device_colinds, // allocate nz
uint nz, uint allocate_nz,
std::vector<uint> reorder_rows)
{
cusp::array1d<ValueType, CPUSpace> new_values(allocate_nz,0);
cusp::array1d<IndexType, CPUSpace> new_colinds(allocate_nz,0);
device_values.resize(allocate_nz);
device_colinds.resize(allocate_nz);
for(int i=0; i< nz; i++) {
new_values[reorder_rows[i]] = A[i];
new_colinds[reorder_rows[i]] = colinds[i];
}
device_values = new_values;
device_colinds = new_colinds;
}
/* warpkernel1 process */
// preform the reordering
template<typename IndexType, typename ValueType>
void process(ValueType *A, IndexType * colinds,
cusp::array1d<ValueType, DeviceSpace> &device_values, // nz
cusp::array1d<IndexType, DeviceSpace> &device_colinds, // nz
cusp::array1d<IndexType, DeviceSpace> &device_row_map, // nrows
cusp::array1d<uint, DeviceSpace> &device_max_nz_per_row, // nwarps
cusp::array1d<IndexType, DeviceSpace> &device_warp_offsets, // nwarps
uint nz, uint allocate_nz,
std::vector<uint> reorder_rows,
std::vector<int> warp_offsets,
std::vector<uint> max_nz_rows,
std::vector<int> row_map)
{
reorder(A,colinds, device_values, device_colinds,
nz, allocate_nz, reorder_rows);
device_row_map = row_map;
device_max_nz_per_row = max_nz_rows;
device_warp_offsets = warp_offsets;
}
/********
Purpose of this executable is to examine different effects of optimization
*******/
#define ValueType double
#define IndexType int
int main(int argc, char *argv[]) {
std::string matrixfilename = argv[1];
int ntests = 1;
if (argc == 3) ntests = atoi(argv[2]);
cusp::coo_matrix<IndexType, ValueType, CPUSpace> B;
cusp::io::read_matrix_market_file(B, matrixfilename.c_str());
cusp::csr_matrix<IndexType, ValueType, CPUSpace> A = B;
uint N = A.num_cols;
uint nz = A.num_entries;
// open up data file
std::string filename;
size_t pos = matrixfilename.find_last_of("/");
std::string matrixname;
if (pos != std::string::npos )
matrixname.assign(matrixfilename.begin()+pos+1, matrixfilename.end());
else
matrixname = matrixfilename;
std::string datapath = "./data/" + matrixname + "_optimize.txt";
std::cout << "Starting data file = " << datapath << std::endl;
std::ofstream datafile(datapath.c_str());
warpkernel::startDatafile(datafile, N, nz,ntests);
cusp::array1d<ValueType, CPUSpace> x(N, 1.0);
// thrust::generate(x.begin(),x.end(), rand_float());
cusp::array1d<ValueType, CPUSpace> y(N);
{
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
// cusp hyb multiplication
for (int i=0;i<ntests;i++)
{
cusp::hyb_matrix<IndexType, ValueType, DeviceSpace> A1 = A;
cusp::array1d<ValueType, DeviceSpace> dx = x;
cusp::array1d<ValueType, DeviceSpace> dy = y;
cusp::detail::timer cusptimer;
cusptimer.start();
cusp::multiply(A1,dx,dy);
ValueType measuredtime = cusptimer.seconds_elapsed();
statstime(measuredtime);
y = dy;
}
std::cout << "cusp gpu time "
<< std::scientific << boost::accumulators::mean(statstime) << std::endl;
warpkernel::addData(datafile, "cusp-csr", boost::accumulators::mean(statstime), -1, -1, -1, -1);
}
// setup for warpkernel1
cusp::array1d<ValueType, DeviceSpace> dx = x;
warpkernel::structure kernel1( N, nz, 0);
std::vector<uint> reorder_rows;
std::vector<int> warp_offsets;
std::vector<uint> max_nz_rows;
std::vector<int> row_map;
std::vector<int> row_map_inv;
uint nznrows;
scan(kernel1.nz, kernel1.nrows,
&(A.values[0]), &(A.row_offsets[0]), &(A.column_indices[0]),
kernel1.nwarps, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map,
row_map_inv, nznrows);
uint warps_per_block = 6;
uint nblocks = (kernel1.nwarps + warps_per_block -1)/warps_per_block;
uint blocksize = warps_per_block * WARP_SIZE;
// original warpkernel1 with cache
{
cusp::array1d<ValueType, DeviceSpace> dy(N, 0);
// allocate arrays
cusp::array1d<ValueType, DeviceSpace> device_values; // nz
cusp::array1d<IndexType, DeviceSpace> device_colinds; // nz
cusp::array1d<IndexType, DeviceSpace> device_row_map; // nrows
cusp::array1d<uint, DeviceSpace> device_max_nz_per_row; // nwarps
cusp::array1d<IndexType, DeviceSpace> device_warp_offsets; // nwarps
cusp::array1d<uint, DeviceSpace> device_threads_per_row; // offsets - nwarps
cusp::array1d<uint, DeviceSpace> device_row_offset_warp; // rows - nwarps
process(&(A.values[0]), &(A.column_indices[0]),
device_values, device_colinds,
device_row_map, device_max_nz_per_row,
device_warp_offsets,
kernel1.nz, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map);
const bool cache = true;
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
if (cache) cudaBindTexture(0, x_tex, thrust::raw_pointer_cast(&dx[0]));
for (int i=0; i < ntests; i++) {
cusp::detail::timer t;
t.start();
warpkernel::warpKernel<cache> <<< nblocks, blocksize >>>
(kernel1.nrows,
thrust::raw_pointer_cast(&device_values[0]),
thrust::raw_pointer_cast(&device_colinds[0]),
thrust::raw_pointer_cast(&device_row_map[0]),
thrust::raw_pointer_cast(&device_max_nz_per_row[0]),
thrust::raw_pointer_cast(&device_warp_offsets[0]),
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));
ValueType measuretime = t.seconds_elapsed();
statstime(measuretime);
}
cusp::array1d<ValueType, CPUSpace> ycheck = dy;
if (verify(y,ycheck,N)) {
std::cout << "current warpkernel_cache time =" << boost::accumulators::mean(statstime) << std::endl;
warpkernel::addData(datafile, "warpkernel_cache", boost::accumulators::mean(statstime), kernel1.allocate_nz, kernel1.nwarps, nblocks, blocksize);
} else std::cout << "failed to verify warp_kernel_cache" << std::endl;
}
// warpkernel.hpp
{
cusp::array1d<ValueType, DeviceSpace> dx = x;
warpkernel::structure kernel1;
kernel1.scan(nz, N, A);
uint warps_per_block = 6;
uint nblocks = (kernel1.nwarps + warps_per_block-1)/warps_per_block;
uint blocksize = warps_per_block * WARP_SIZE;
warpkernel::engine<ValueType, IndexType, warpkernel::structure> eng(kernel1,
&(A.values[0]),
&(A.column_indices[0]));
cusp::array1d<ValueType, DeviceSpace> dy(N,0);
ValueType measuretime = 0;
for (int t = 0; t < ntests; t++) {
measuretime += eng.run<true>(nblocks, blocksize,
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));
}
cusp::array1d<ValueType, CPUSpace> ycheck = dy;
if (eng.verify(y,ycheck))
{
std::cout << "warpkernel (" << nblocks << "," << blocksize <<") time = "
<< std::scientific << measuretime/ntests << std::endl;
} else {
std::cout << "Failed original warpkernel.hpp" << std::endl;
}
}
// original warpkernel1 no cache
{
cusp::array1d<ValueType, DeviceSpace> dy(N, 0);
// allocate arrays
cusp::array1d<ValueType, DeviceSpace> device_values; // nz
cusp::array1d<IndexType, DeviceSpace> device_colinds; // nz
cusp::array1d<IndexType, DeviceSpace> device_row_map; // nrows
cusp::array1d<uint, DeviceSpace> device_max_nz_per_row; // nwarps
cusp::array1d<IndexType, DeviceSpace> device_warp_offsets; // nwarps
cusp::array1d<uint, DeviceSpace> device_threads_per_row; // offsets - nwarps
cusp::array1d<uint, DeviceSpace> device_row_offset_warp; // rows - nwarps
process(&(A.values[0]), &(A.column_indices[0]),
device_values, device_colinds,
device_row_map, device_max_nz_per_row,
device_warp_offsets,
kernel1.nz, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map);
const bool cache = false;
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
if (cache) cudaBindTexture(0, x_tex, thrust::raw_pointer_cast(&dx[0]));
for (int i=0; i < ntests; i++) {
cusp::detail::timer t;
t.start();
warpkernel::warpKernel<cache> <<< nblocks, blocksize >>>
(kernel1.nrows,
thrust::raw_pointer_cast(&device_values[0]),
thrust::raw_pointer_cast(&device_colinds[0]),
thrust::raw_pointer_cast(&device_row_map[0]),
thrust::raw_pointer_cast(&device_max_nz_per_row[0]),
thrust::raw_pointer_cast(&device_warp_offsets[0]),
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));
ValueType measuretime = t.seconds_elapsed();
statstime(measuretime);
}
cusp::array1d<ValueType, CPUSpace> ycheck = dy;
if (verify(y,ycheck,N)) {
std::cout << "current warpkernel_nocache time =" << boost::accumulators::mean(statstime) << std::endl;
warpkernel::addData(datafile, "warpkernel_nocache", boost::accumulators::mean(statstime), kernel1.allocate_nz, kernel1.nwarps, nblocks, blocksize);
} else std::cout << "failed to verify warp_kernel_nocache" << std::endl;
}
// original warpkernel1 no reordering with cache
{
// allocate arrays
cusp::array1d<ValueType, DeviceSpace> device_values; // nz
cusp::array1d<IndexType, DeviceSpace> device_colinds; // nz
cusp::array1d<IndexType, DeviceSpace> device_row_map; // nrows
cusp::array1d<uint, DeviceSpace> device_max_nz_per_row; // nwarps
cusp::array1d<IndexType, DeviceSpace> device_warp_offsets; // nwarps
cusp::array1d<uint, DeviceSpace> device_threads_per_row; // offsets - nwarps
cusp::array1d<uint, DeviceSpace> device_row_offset_warp; // rows - nwarps
scan_nocoalesced(kernel1.nz, kernel1.nrows,
&(A.values[0]), &(A.row_offsets[0]), &(A.column_indices[0]),
kernel1.nwarps, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map,
row_map_inv);
process(&(A.values[0]), &(A.column_indices[0]),
device_values, device_colinds,
device_row_map, device_max_nz_per_row,
device_warp_offsets,
kernel1.nz, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map);
// original warpkernel1 not coalesced with cache
{
cusp::array1d<ValueType, DeviceSpace> dy(N, 0);
const bool cache = true;
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
if (cache) cudaBindTexture(0, x_tex, thrust::raw_pointer_cast(&dx[0]));
for (int i=0; i < ntests; i++) {
cusp::detail::timer t;
t.start();
warpKernel_nocoalesced<cache> <<< nblocks, blocksize >>>
(kernel1.nrows,
thrust::raw_pointer_cast(&device_values[0]),
thrust::raw_pointer_cast(&device_colinds[0]),
thrust::raw_pointer_cast(&device_row_map[0]),
thrust::raw_pointer_cast(&device_max_nz_per_row[0]),
thrust::raw_pointer_cast(&device_warp_offsets[0]),
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));
ValueType measuretime = t.seconds_elapsed();
statstime(measuretime);
}
cusp::array1d<ValueType, CPUSpace> ycheck = dy;
if (verify(y,ycheck,N)) {
std::cout << "current warpkernel_cache_nocoalesced time =" << boost::accumulators::mean(statstime) << std::endl;
warpkernel::addData(datafile, "warpkernel_cache_nocoalesced", boost::accumulators::mean(statstime), kernel1.allocate_nz, kernel1.nwarps, nblocks, blocksize);
} else std::cout << "failed to verify warp_kernel_cache_nocoalesced" << std::endl;
}
// original warpkernel1 not coalesced with no cache
{
cusp::array1d<ValueType, DeviceSpace> dy(N, 0);
const bool cache = false;
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
if (cache) cudaBindTexture(0, x_tex, thrust::raw_pointer_cast(&dx[0]));
for (int i=0; i < ntests; i++) {
cusp::detail::timer t;
t.start();
warpKernel_nocoalesced<cache> <<< nblocks, blocksize >>>
(kernel1.nrows,
thrust::raw_pointer_cast(&device_values[0]),
thrust::raw_pointer_cast(&device_colinds[0]),
thrust::raw_pointer_cast(&device_row_map[0]),
thrust::raw_pointer_cast(&device_max_nz_per_row[0]),
thrust::raw_pointer_cast(&device_warp_offsets[0]),
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));
ValueType measuretime = t.seconds_elapsed();
statstime(measuretime);
}
cusp::array1d<ValueType, CPUSpace> ycheck = dy;
if (verify(y,ycheck,N)) {
std::cout << "current warpkernel_nocache_nocoalesced time =" << boost::accumulators::mean(statstime) << std::endl;
warpkernel::addData(datafile, "warpkernel_nocache_nocoalesced", boost::accumulators::mean(statstime), kernel1.allocate_nz, kernel1.nwarps, nblocks, blocksize);
} else std::cout << "failed to verify warp_kernel_nocahce_nocoalesced" << std::endl;
}
}
// original warpkernel1 no reordering with cache no sorting
{
// allocate arrays
cusp::array1d<ValueType, DeviceSpace> device_values; // nz
cusp::array1d<IndexType, DeviceSpace> device_colinds; // nz
cusp::array1d<IndexType, DeviceSpace> device_row_map; // nrows
cusp::array1d<uint, DeviceSpace> device_max_nz_per_row; // nwarps
cusp::array1d<IndexType, DeviceSpace> device_warp_offsets; // nwarps
cusp::array1d<uint, DeviceSpace> device_threads_per_row; // offsets - nwarps
cusp::array1d<uint, DeviceSpace> device_row_offset_warp; // rows - nwarps
scan_nocoalesced_nosort(kernel1.nz, kernel1.nrows,
&(A.values[0]), &(A.row_offsets[0]), &(A.column_indices[0]),
kernel1.nwarps, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map,
row_map_inv);
process(&(A.values[0]), &(A.column_indices[0]),
device_values, device_colinds,
device_row_map, device_max_nz_per_row,
device_warp_offsets,
kernel1.nz, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map);
// original warpkernel1 not coalesced with cache no sort
{
cusp::array1d<ValueType, DeviceSpace> dy(N, 0);
const bool cache = true;
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
if (cache) cudaBindTexture(0, x_tex, thrust::raw_pointer_cast(&dx[0]));
for (int i=0; i < ntests; i++) {
cusp::detail::timer t;
t.start();
warpKernel_nocoalesced<cache> <<< nblocks, blocksize >>>
(kernel1.nrows,
thrust::raw_pointer_cast(&device_values[0]),
thrust::raw_pointer_cast(&device_colinds[0]),
thrust::raw_pointer_cast(&device_row_map[0]),
thrust::raw_pointer_cast(&device_max_nz_per_row[0]),
thrust::raw_pointer_cast(&device_warp_offsets[0]),
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));
ValueType measuretime = t.seconds_elapsed();
statstime(measuretime);
}
cusp::array1d<ValueType, CPUSpace> ycheck = dy;
if (verify(y,ycheck,N)) {
std::cout << "current warpkernel_cache_nocoalesced_nosort time =" << boost::accumulators::mean(statstime) << std::endl;
warpkernel::addData(datafile, "warpkernel_cache_nocoalesced_nosort", boost::accumulators::mean(statstime), kernel1.allocate_nz, kernel1.nwarps, nblocks, blocksize);
} else std::cout << "failed to verify warp_kernel_cache_nocoalesced_nosort" << std::endl;
}
// original warpkernel1 not coalesced with no cache no sort
{
cusp::array1d<ValueType, DeviceSpace> dy(N, 0);
const bool cache = false;
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
if (cache) cudaBindTexture(0, x_tex, thrust::raw_pointer_cast(&dx[0]));
for (int i=0; i < ntests; i++) {
cusp::detail::timer t;
t.start();
warpKernel_nocoalesced<cache> <<< nblocks, blocksize >>>
(kernel1.nrows,
thrust::raw_pointer_cast(&device_values[0]),
thrust::raw_pointer_cast(&device_colinds[0]),
thrust::raw_pointer_cast(&device_row_map[0]),
thrust::raw_pointer_cast(&device_max_nz_per_row[0]),
thrust::raw_pointer_cast(&device_warp_offsets[0]),
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));
ValueType measuretime = t.seconds_elapsed();
statstime(measuretime);
}
cusp::array1d<ValueType, CPUSpace> ycheck = dy;
if (verify(y,ycheck,N)) {
std::cout << "current warpkernel_nocache_nocoalesced_nosort time =" << boost::accumulators::mean(statstime) << std::endl;
warpkernel::addData(datafile, "warpkernel_nocache_nocoalesced_nosort", boost::accumulators::mean(statstime), kernel1.allocate_nz, kernel1.nwarps, nblocks, blocksize);
} else std::cout << "failed to verify warp_kernel_cache_nocache_nocoalesced_nosort" << std::endl;
}
// original warpkernel1 no reordering with cache
{
// allocate arrays
cusp::array1d<ValueType, DeviceSpace> device_values; // nz
cusp::array1d<IndexType, DeviceSpace> device_colinds; // nz
cusp::array1d<IndexType, DeviceSpace> device_row_map; // nrows
cusp::array1d<uint, DeviceSpace> device_max_nz_per_row; // nwarps
cusp::array1d<IndexType, DeviceSpace> device_warp_offsets; // nwarps
cusp::array1d<uint, DeviceSpace> device_threads_per_row; // offsets - nwarps
cusp::array1d<uint, DeviceSpace> device_row_offset_warp; // rows - nwarps
scan_nosort(kernel1.nz, kernel1.nrows,
&(A.values[0]), &(A.row_offsets[0]), &(A.column_indices[0]),
kernel1.nwarps, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map,
row_map_inv);
process(&(A.values[0]), &(A.column_indices[0]),
device_values, device_colinds,
device_row_map, device_max_nz_per_row,
device_warp_offsets,
kernel1.nz, kernel1.allocate_nz,
reorder_rows,
warp_offsets,
max_nz_rows,
row_map);
// original warpkernel1 not coalesced with cache
{
cusp::array1d<ValueType, DeviceSpace> dy(N, 0);
const bool cache = true;
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
if (cache) cudaBindTexture(0, x_tex, thrust::raw_pointer_cast(&dx[0]));
for (int i=0; i < ntests; i++) {
cusp::detail::timer t;
t.start();
warpkernel::warpKernel<cache> <<< nblocks, blocksize >>>
(kernel1.nrows,
thrust::raw_pointer_cast(&device_values[0]),
thrust::raw_pointer_cast(&device_colinds[0]),
thrust::raw_pointer_cast(&device_row_map[0]),
thrust::raw_pointer_cast(&device_max_nz_per_row[0]),
thrust::raw_pointer_cast(&device_warp_offsets[0]),
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));
ValueType measuretime = t.seconds_elapsed();
statstime(measuretime);
}
cusp::array1d<ValueType, CPUSpace> ycheck = dy;
if (verify(y,ycheck,N)) {
std::cout << "current warpkernel_cache_nosort time =" << boost::accumulators::mean(statstime) << std::endl;
warpkernel::addData(datafile, "warpkernel_cache_nosort", boost::accumulators::mean(statstime), kernel1.allocate_nz, kernel1.nwarps, nblocks, blocksize);
} else std::cout << "failed to verify warp_kernel_cache_nosort" << std::endl;
}
// original warpkernel1 not coalesced with no cache
{
cusp::array1d<ValueType, DeviceSpace> dy(N, 0);
const bool cache = false;
boost::accumulators::accumulator_set<ValueType, boost::accumulators::stats<boost::accumulators::tag::mean> > statstime;
if (cache) cudaBindTexture(0, x_tex, thrust::raw_pointer_cast(&dx[0]));
for (int i=0; i < ntests; i++) {
cusp::detail::timer t;
t.start();
warpkernel::warpKernel<cache> <<< nblocks, blocksize >>>
(kernel1.nrows,
thrust::raw_pointer_cast(&device_values[0]),
thrust::raw_pointer_cast(&device_colinds[0]),
thrust::raw_pointer_cast(&device_row_map[0]),
thrust::raw_pointer_cast(&device_max_nz_per_row[0]),
thrust::raw_pointer_cast(&device_warp_offsets[0]),
thrust::raw_pointer_cast(&dx[0]),
thrust::raw_pointer_cast(&dy[0]));