OpenACC 梯度下降法求解线性方程的优化
▶ 书上第二章，用一系列步骤优化梯度下降法解线性方程组。才发现 PGI community 编译器不支持 Windows 下的 C++ 编译（有 pgCC 命令但是不支持 .cpp 文件，要专业版才支持），以后 OpenACC - C++ 全盘转向 Ubuntu 中。
● 代码
// matrix.h
#pragma once
#include <cstdlib> struct matrix
{
unsigned int num_rows;
unsigned int nnz;
unsigned int *row_offsets;
unsigned int *cols;
double *coefs;
}; void allocate_3d_poission_matrix(matrix &A, int N)
{
const int num_rows = (N + ) * (N + ) * (N + );
A.num_rows = num_rows;
A.row_offsets = (unsigned int*)malloc((num_rows + ) * sizeof(unsigned int));
A.cols = (unsigned int*)malloc( * num_rows * sizeof(unsigned int));
A.coefs = (double*)malloc( * num_rows * sizeof(double)); const int ystride = N, zstride = N * N;
int i, j, n, nnz, offsets[];
double coefs[]; i = ;
for (int z = -; z <= ; z++)
{
for (int y = -; y <= ; y++)
{
for (int x = -; x <= ; x++)
{
offsets[i] = zstride * z + ystride * y + x;
if (x == && y == && z == )
coefs[i] = ;
else
coefs[i] = -;
i++;
}
}
}
nnz = ;
for (i = ; i < num_rows; i++)
{
A.row_offsets[i] = nnz;
for (j = ; j < ; j++)
{
n = i + offsets[j];
if (n >= && n<num_rows)
{
A.cols[nnz] = n;
A.coefs[nnz] = coefs[j];
nnz++;
}
}
}
A.row_offsets[num_rows] = nnz;
A.nnz = nnz;
} void free_matrix(matrix &A)
{
unsigned int *row_offsets = A.row_offsets, *cols = A.cols;
double *coefs = A.coefs;
free(row_offsets);
free(cols);
free(coefs);
}
// matrix_function.h
#pragma once
#include "vector.h"
#include "matrix.h" void matvec(const matrix& A, const vector& x, const vector &y)
{ const unsigned int num_rows = A.num_rows;
unsigned int *row_offsets = A.row_offsets, *cols = A.cols;
double *Acoefs = A.coefs, *xcoefs = x.coefs, *ycoefs = y.coefs; for (int i = ; i < num_rows; i++)
{
const int row_start = row_offsets[i], row_end = row_offsets[i + ];
double sum = ;
for (int j = row_start; j < row_end; j++)
sum += Acoefs[j] * xcoefs[cols[j]];
ycoefs[i] = sum;
}
}
// vector.h
#pragma once
#include<cstdlib>
#include<cmath> struct vector
{
unsigned int n;
double *coefs;
}; void allocate_vector(vector &v, const unsigned int n)
{
v.n = n;
v.coefs = (double*)malloc(n * sizeof(double));
} void free_vector(vector &v)
{
v.n = ;
free(v.coefs);
} void initialize_vector(vector &v, const double val)
{
for (int i = ; i < v.n; i++)
v.coefs[i] = val;
}
// vector_function.h
#pragma once
#include<cstdlib>
#include "vector.h" double dot(const vector& x, const vector& y)
{
const unsigned int n = x.n;
double sum = , *xcoefs = x.coefs, *ycoefs = y.coefs; for (int i = ; i < n; i++)
sum += xcoefs[i] * ycoefs[i];
return sum;
} void waxpby(double alpha, const vector &x, double beta, const vector &y, const vector& w)
{
const unsigned int n = x.n;
double *xcoefs = x.coefs, *ycoefs = y.coefs, *wcoefs = w.coefs; for (int i = ; i < n; i++)
wcoefs[i] = alpha * xcoefs[i] + beta * ycoefs[i];
}
// main.cpp
#include <cstdlib>
#include <cstdio>
#include <chrono> #include "vector.h"
#include "vector_functions.h"
#include "matrix.h"
#include "matrix_functions.h" using namespace std::chrono; #define N 200
#define MAX_ITERS 100
#define TOL 1e-12 int main()
{
int iter;
double normr, rtrans, oldtrans, alpha;
vector x, p, Ap, b, r;
matrix A;
high_resolution_clock::time_point t1, t2; allocate_3d_poission_matrix(A, N);
allocate_vector(x, A.num_rows);
allocate_vector(p, A.num_rows);
allocate_vector(Ap, A.num_rows);
allocate_vector(b, A.num_rows);
allocate_vector(r, A.num_rows);
printf("Rows: %d, nnz: %d\n", A.num_rows, A.row_offsets[A.num_rows]); initialize_vector(x, );
initialize_vector(b, ); // 计算一个初始 r
waxpby(1.0, x, 0.0, x, p);
matvec(A, p, Ap);
waxpby(1.0, b, -1.0, Ap, r);
rtrans = dot(r, r);
normr = sqrt(rtrans); t1 = high_resolution_clock::now();
for(iter = ; iter < MAX_ITERS && normr > TOL; iter++)
{
// 更新 p 和 rtrans
if (iter == )
waxpby(1.0, r, 0.0, r, p);
else
{
oldtrans = rtrans;
rtrans = dot(r, r);
waxpby(1.0, r, rtrans / oldtrans, p, p);
} // 计算步长 alpha，用的是上一次的 rtran
matvec(A, p, Ap);
alpha = rtrans / dot(Ap, p);
normr = sqrt(rtrans); // 更新 x 和 r
waxpby(1.0, x, alpha, p, x);
waxpby(1.0, r, -alpha, Ap, r); if (iter % == )
printf("Iteration: %d, Tolerance: %.4e\n", iter, normr);
}
t2 = high_resolution_clock::now();
duration<double> time = duration_cast<duration<double>>(t2 - t1);
printf("Iterarion: %d, error: %e, time: %f s\n", iter, normr, time.count()); free_matrix(A);
free_vector(x);
free_vector(p);
free_vector(Ap);
free_vector(b);
free_vector(r);
//getchar();
return ;
}
● 输出结果，WSL
// WSL:
cuan@CUAN:/mnt/d/Code/ParallelProgrammingWithOpenACC-master/Chapter02/cpp$ pgc++ -std=c++ -acc -fast main.cpp -o acc.exe
cuan@CUAN:/mnt/d/Code/ParallelProgrammingWithOpenACC-master/Chapter02/cpp$ ./acc.exe
Rows: , nnz:
Iteration: , Tolerance: 4.0067e+08
Iteration: , Tolerance: 1.8772e+07
Iteration: , Tolerance: 6.4359e+05
Iteration: , Tolerance: 2.3202e+04
Iteration: , Tolerance: 8.3565e+02
Iteration: , Tolerance: 3.0039e+01
Iteration: , Tolerance: 1.0764e+00
Iteration: , Tolerance: 3.8360e-02
Iteration: , Tolerance: 1.3515e-03
Iteration: , Tolerance: 4.6209e-05
Iterarion: , error: 1.993399e-06, time: 17.065934 s // Ubuntu:
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/cpp$ pgc++ -std=c++ -acc -fast -Minfo -ta=tesla main.cpp -o no_acc.exe
initialize_vector(vector &, double):
, include "vector.h"
, Memory set idiom, loop replaced by call to __c_mset8
dot(const vector &, const vector &):
, include "vector_functions.h"
, Generated an alternate version of the loop
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
FMA (fused multiply-add) instruction(s) generated
waxpby(double, const vector &, double, const vector &, const vector &):
, include "vector_functions.h"
, Generated alternate versions of the loop
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated prefetch instructions for the loop
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated prefetch instructions for the loop
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated prefetch instructions for the loop
Loop unrolled times
FMA (fused multiply-add) instruction(s) generated
allocate_3d_poission_matrix(matrix &, int):
, include "matrix.h"
, Loop not fused: different loop trip count
, Loop not vectorized/parallelized: loop count too small
, Loop unrolled times (completely unrolled)
, Loop not vectorized: data dependency
matvec(const matrix &, const vector &, const vector &):
, include "matrix_functions.h"
, Generated an alternate version of the loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
FMA (fused multiply-add) instruction(s) generated
main:
, allocate_3d_poission_matrix(matrix &, int) inlined, size= (inline) file main.cpp ()
, Loop not fused: different loop trip count
, Loop not vectorized/parallelized: loop count too small
, Loop unrolled times (completely unrolled)
, Loop not fused: function call before adjacent loop
, Loop not vectorized: data dependency
, allocate_vector(vector &, unsigned int) inlined, size= (inline) file main.cpp ()
, allocate_vector(vector &, unsigned int) inlined, size= (inline) file main.cpp ()
, allocate_vector(vector &, unsigned int) inlined, size= (inline) file main.cpp ()
, allocate_vector(vector &, unsigned int) inlined, size= (inline) file main.cpp ()
, allocate_vector(vector &, unsigned int) inlined, size= (inline) file main.cpp ()
, initialize_vector(vector &, double) inlined, size= (inline) file main.cpp ()
, Memory set idiom, loop replaced by call to __c_mset8
, initialize_vector(vector &, double) inlined, size= (inline) file main.cpp ()
, Memory set idiom, loop replaced by call to __c_mset8
, waxpby(double, const vector &, double, const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Memory copy idiom, loop replaced by call to __c_mcopy8
, matvec(const matrix &, const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: dependence chain to sibling loop
, Generated an alternate version of the loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
FMA (fused multiply-add) instruction(s) generated
, waxpby(double, const vector &, double, const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: dependence chain to sibling loop
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated prefetch instructions for the loop
Loop unrolled times
Generated prefetches in scalar loop
FMA (fused multiply-add) instruction(s) generated
, dot(const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: function call before adjacent loop
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
FMA (fused multiply-add) instruction(s) generated
, Loop not vectorized/parallelized: potential early exits
, waxpby(double, const vector &, double, const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Memory copy idiom, loop replaced by call to __c_mcopy8
, dot(const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: dependence chain to sibling loop
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
FMA (fused multiply-add) instruction(s) generated
, waxpby(double, const vector &, double, const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: different controlling conditions
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated prefetch instructions for the loop
Loop unrolled times
Generated prefetches in scalar loop
FMA (fused multiply-add) instruction(s) generated
, matvec(const matrix &, const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: dependence chain to sibling loop
, Generated an alternate version of the loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
FMA (fused multiply-add) instruction(s) generated
, dot(const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: dependence chain to sibling loop
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
FMA (fused multiply-add) instruction(s) generated
, waxpby(double, const vector &, double, const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: dependence chain to sibling loop
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Loop not vectorized: data dependency
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated prefetch instructions for the loop
Loop unrolled times
Generated prefetches in scalar loop
FMA (fused multiply-add) instruction(s) generated
, waxpby(double, const vector &, double, const vector &, const vector &) inlined, size= (inline) file main.cpp ()
, Loop not fused: function call before adjacent loop
Generated vector and scalar versions of the loop; pointer conflict tests determine which is executed
Generated prefetch instructions for the loop
Loop unrolled times
Generated prefetches in scalar loop
FMA (fused multiply-add) instruction(s) generated
, free_matrix(matrix &) inlined, size= (inline) file main.cpp ()
, free_vector(vector &) inlined, size= (inline) file main.cpp ()
, free_vector(vector &) inlined, size= (inline) file main.cpp ()
, free_vector(vector &) inlined, size= (inline) file main.cpp ()
, free_vector(vector &) inlined, size= (inline) file main.cpp ()
, free_vector(vector &) inlined, size= (inline) file main.cpp ()
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/cpp$ ./no_acc.exe
Rows: , nnz:
Iteration: , Tolerance: 4.0067e+08
Iteration: , Tolerance: 1.8772e+07
Iteration: , Tolerance: 6.4359e+05
Iteration: , Tolerance: 2.3202e+04
Iteration: , Tolerance: 8.3565e+02
Iteration: , Tolerance: 3.0039e+01
Iteration: , Tolerance: 1.0764e+00
Iteration: , Tolerance: 3.8360e-02
Iteration: , Tolerance: 1.3515e-03
Iteration: , Tolerance: 4.6209e-05
Iterarion: , error: 1.993399e-06, time: 17182.560547 ms
● 优化后
// matrix.h
#pragma once
#include <cstdlib> struct matrix
{
unsigned int num_rows;
unsigned int nnz;
unsigned int *row_offsets;
unsigned int *cols;
double *coefs;
}; void allocate_3d_poission_matrix(matrix &A, int N)
{
const int num_rows = (N + ) * (N + ) * (N + );
A.num_rows = num_rows;
A.row_offsets = (unsigned int*)malloc((num_rows + ) * sizeof(unsigned int));
A.cols = (unsigned int*)malloc( * num_rows * sizeof(unsigned int));
A.coefs = (double*)malloc( * num_rows * sizeof(double)); const int ystride = N, zstride = N * N;
int i, j, n, nnz, offsets[];
double coefs[]; i = ;
for (int z = -; z <= ; z++)
{
for (int y = -; y <= ; y++)
{
for (int x = -; x <= ; x++)
{
offsets[i] = zstride * z + ystride * y + x;
if (x == && y == && z == )
coefs[i] = ;
else
coefs[i] = -;
i++;
}
}
}
nnz = ;
for (i = ; i < num_rows; i++)
{
A.row_offsets[i] = nnz;
for (j = ; j < ; j++)
{
n = i + offsets[j];
if (n >= && n<num_rows)
{
A.cols[nnz] = n;
A.coefs[nnz] = coefs[j];
nnz++;
}
}
}
A.row_offsets[num_rows] = nnz;
A.nnz = nnz;
#pragma acc enter data copyin(A)
#pragma acc enter data copyin(A.row_offsets[0:num_rows+1],A.cols[0:nnz],A.coefs[0:nnz])
} void free_matrix(matrix &A)
{
unsigned int *row_offsets = A.row_offsets, *cols = A.cols;
double *coefs = A.coefs;
#pragma acc exit data delete(A.row_offsets,A.cols,A.coefs)
#pragma acc exit data delete(A)
free(row_offsets);
free(cols);
free(coefs);
}
// matrix_function.h
#pragma once
#include "vector.h"
#include "matrix.h" void matvec(const matrix& A, const vector& x, const vector &y)
{
const unsigned int num_rows=A.num_rows;
unsigned int *restrict row_offsets=A.row_offsets, *restrict cols=A.cols;
double *restrict Acoefs=A.coefs, *restrict xcoefs=x.coefs, *restrict ycoefs=y.coefs; #pragma acc kernels copyin(cols[0:A.nnz],Acoefs[0:A.nnz],xcoefs[0:x.n])
#pragma acc loop device_type(nvidia) gang worker(8)
for(int i = ; i < num_rows; i++)
{
double sum = ;
const int row_start=row_offsets[i], row_end=row_offsets[i+];
#pragma acc loop device_type(nvidia) vector(32)
for(int j = row_start; j < row_end; j++)
sum += Acoefs[j] * xcoefs[cols[j]];
ycoefs[i] = sum;
}
}
// vector.h
#pragma once
#include<cstdlib>
#include<cmath> struct vector
{
unsigned int n;
double *coefs;
}; void allocate_vector(vector &v, const unsigned int n)
{
v.n=n;
v.coefs=(double*)malloc(n*sizeof(double));
#pragma acc enter data create(v)
#pragma acc enter data create(v.coefs[0:n])
} void free_vector(vector &v)
{
#pragma acc exit data delete(v.coefs)
#pragma acc exit data delete(v)
v.n = ;
free(v.coefs);
} void initialize_vector(vector &v, const double val)
{
for (int i = ; i < v.n; i++)
v.coefs[i] = val;
#pragma acc update device(v.coefs[0:v.n])
}
// vector_functions.h
#pragma once
#include<cstdlib>
#include "vector.h" double dot(const vector& x, const vector& y)
{
const unsigned int n = x.n;
double sum = , *xcoefs = x.coefs, *ycoefs = y.coefs; #pragma acc kernels
for (int i = ; i < n; i++)
sum += xcoefs[i] * ycoefs[i];
return sum;
} void waxpby(double alpha, const vector &x, double beta, const vector &y, const vector& w) const unsigned int n = x.n;
double *restrict xcoefs = x.coefs, *ycoefs = y.coefs, *wcoefs = w.coefs; #pragma acc kernels copy(wcoefs[:w.n],ycoefs[0:y.n]) copyin(xcoefs[0:x.n])
{
#pragma acc loop independent
for (int i = ; i<n; i++)
wcoefs[i] = alpha * xcoefs[i] + beta * ycoefs[i];
}
}
● 输出结果
// Ubuntu:
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/cpp$ pgc++ -fast -acc -Minfo main.cpp -ta=tesla
allocate_vector(vector &, unsigned int):
, include "vector.h"
, Accelerator clause: upper bound for dimension of array 'v' is unknown
Generating enter data create(v[:],v->coefs[:n])
free_vector(vector &):
, include "vector.h"
, Generating exit data delete(v[:],v->coefs[:])
initialize_vector(vector &, double):
, include "vector.h"
, Memory set idiom, loop replaced by call to __c_mset8
, Generating update device(v->coefs[:v->n])
dot(const vector &, const vector &):
, include "vector_functions.h"
, Generating implicit copyin(ycoefs[:n],xcoefs[:n])
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, vector(128) /* blockIdx.x threadIdx.x */
, Generating implicit reduction(+:sum)
waxpby(double, const vector &, double, const vector &, const vector &):
, include "vector_functions.h"
, Generating copyin(xcoefs[:x->n])
Generating copy(ycoefs[:y->n],wcoefs[:w->n])
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, vector(128) /* blockIdx.x threadIdx.x */
allocate_3d_poission_matrix(matrix &, int):
, include "matrix.h"
, Loop not fused: different loop trip count
, Loop not vectorized/parallelized: loop count too small
, Loop not fused: function call before adjacent loop
, Loop not vectorized: data dependency
, Accelerator clause: upper bound for dimension of array 'A' is unknown
Generating enter data copyin(A[:],A->row_offsets[:num_rows+],A->coefs[:nnz],A->cols[:nnz])
free_matrix(matrix &):
, include "matrix.h"
, Generating exit data delete(A->coefs[:],A->cols[:],A[:],A->row_offsets[:])
matvec(const matrix &, const vector &, const vector &):
, include "matrix_functions.h"
, Generating copyin(Acoefs[:A->nnz])
Generating implicit copyin(row_offsets[:num_rows+])
Generating copyin(xcoefs[:x->n])
Generating implicit copyout(ycoefs[:num_rows])
Generating copyin(cols[:A->nnz])
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, worker(8) /* blockIdx.x threadIdx.y */
, #pragma acc loop vector(32) /* threadIdx.x */
, Generating implicit reduction(+:sum)
, Loop is parallelizable
main:
, Loop not vectorized/parallelized: potential early exits
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/cpp$ ./a.out
Rows: , nnz:
Iteration: , Tolerance: 4.0067e+08
Iteration: , Tolerance: 1.8772e+07
Iteration: , Tolerance: 6.4359e+05
Iteration: , Tolerance: 2.3202e+04
Iteration: , Tolerance: 8.3565e+02
Iteration: , Tolerance: 3.0039e+01
Iteration: , Tolerance: 1.0764e+00
Iteration: , Tolerance: 3.8360e-02
Iteration: , Tolerance: 1.3515e-03
Iteration: , Tolerance: 4.6209e-05
Iterarion: , error: 1.993399e-06, time: 3249.523926 ms
● 对应的 fortran 代码优化前后的结果
// Ubuntu 优化前:
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90/-multicore$ pgf90 -c matrix.F90 vector.F90
matrix.F90:
vector.F90:
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90/-multicore$ pgf90 main.F90 matrix.o vector.o -fast -mp -Minfo -o no_acc.exe
main.F90:
main:
, Loop not vectorized/parallelized: potential early exits
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90/-multicore$ ./no_acc.exe
Rows: nnz:
Iteration: Tolerance: 4.006700E+08
Iteration: Tolerance: 1.877230E+07
^C
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90/-multicore$ cd ..
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90$ pgf90 -c matrix.F90 vector.F90 -fast -Minfo
matrix.F90:
allocate_3d_poisson_matrix:
, Loop not fused: different loop trip count
, Loop not vectorized/parallelized: loop count too small
, Loop unrolled times (completely unrolled)
, Loop not vectorized: data dependency
matvec:
, Loop unrolled times
FMA (fused multiply-add) instruction(s) generated
vector.F90:
initialize_vector:
, Memory set idiom, loop replaced by call to __c_mset8
dot:
, Generated an alternate version of the loop
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
FMA (fused multiply-add) instruction(s) generated
waxpby:
, Generated alternate versions of the loop
Generated vector simd code for the loop
Generated prefetch instructions for the loop
Generated vector simd code for the loop
Generated prefetch instructions for the loop
Generated vector simd code for the loop
Generated prefetch instructions for the loop
FMA (fused multiply-add) instruction(s) generated
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90$ pgf90 main.F90 matrix.o vector.o -fast -Minfo -mp -o no_acc.exe
main.F90:
main:
, Loop not vectorized/parallelized: potential early exits
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90$ ./no_acc.exe
Rows: nnz:
Iteration: Tolerance: 4.006700E+08
Iteration: Tolerance: 1.877230E+07
Iteration: Tolerance: 6.435887E+05
Iteration: Tolerance: 2.320219E+04
Iteration: Tolerance: 8.356487E+02
Iteration: Tolerance: 3.003893E+01
Iteration: Tolerance: 1.076441E+00
Iteration: Tolerance: 3.836034E-02
Iteration: Tolerance: 1.351503E-03
Iteration: Tolerance: 4.620883E-05
Total Iterations: Time (s): 23.30484 s // Ubuntu 优化后：
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90/-multicore$ pgf90 -c matrix.F90 vector.F90 -acc -fast -Minfo
matrix.F90:
allocate_3d_poisson_matrix:
, Loop not fused: different loop trip count
, Loop not vectorized/parallelized: loop count too small
, Loop unrolled times (completely unrolled)
, Loop not fused: function call before adjacent loop
, Loop not vectorized: data dependency
, Generating enter data copyin(a)
, Generating enter data copyin(a%coefs(:),a%row_offsets(:),a%cols(:))
free_matrix:
, Generating exit data delete(a%coefs(:),a%cols(:),a%row_offsets(:))
, Generating exit data delete(a)
matvec:
, Generating implicit copyin(arow_offsets(:a%num_rows+),acols(:),acoefs(:))
Generating implicit copyout(y(:a%num_rows))
Generating implicit copyin(x(:))
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, !$acc loop gang, worker() ! blockidx%x threadidx%y
, !$acc loop vector() ! threadidx%x
, Generating implicit reduction(+:tmpsum)
, Loop not fused: no successor loop
, Loop is parallelizable
Loop unrolled times
FMA (fused multiply-add) instruction(s) generated
vector.F90:
initialize_vector:
, Generating present(vector(:))
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, !$acc loop gang, vector() ! blockidx%x threadidx%x
, Memory set idiom, loop replaced by call to __c_mset8
allocate_vector:
, Generating enter data create(vector(:))
free_vector:
, Generating exit data delete(vector(:))
dot:
, Generating implicit copyin(y(:length),x(:length))
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, !$acc loop gang, vector() ! blockidx%x threadidx%x
, Generating implicit reduction(+:tmpsum)
, Loop not fused: no successor loop
Generated an alternate version of the loop
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
Generated vector simd code for the loop containing reductions
Generated prefetch instructions for the loop
FMA (fused multiply-add) instruction(s) generated
waxpby:
, Generating implicit copyin(x(:length),y(:length))
Generating implicit copyout(w(:length))
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, !$acc loop gang, vector() ! blockidx%x threadidx%x
, Loop not fused: no successor loop
Generated alternate versions of the loop
Generated vector simd code for the loop
Generated prefetch instructions for the loop
Generated vector simd code for the loop
Generated prefetch instructions for the loop
Generated vector simd code for the loop
Generated prefetch instructions for the loop
FMA (fused multiply-add) instruction(s) generated
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90/-multicore$ pgf90 main.F90 matrix.o vector.o -acc -fast -Minfo -mp -o acc.exe
main.F90:
main:
, Loop not vectorized/parallelized: potential early exits
cuan@CUAN:/media/cuan/02FCDA52FCDA4019/Code/ParallelProgrammingWithOpenACC-master/Chapter02/f90/-multicore$ ./acc.exe
Rows: nnz:
Iteration: Tolerance: 4.006700E+08
Iteration: Tolerance: 1.877230E+07
Iteration: Tolerance: 6.435887E+05
Iteration: Tolerance: 2.320219E+04
Iteration: Tolerance: 8.356487E+02
Iteration: Tolerance: 3.003893E+01
Iteration: Tolerance: 1.076441E+00
Iteration: Tolerance: 3.836034E-02
Iteration: Tolerance: 1.351503E-03
Iteration: Tolerance: 4.620883E-05
Total Iterations: Time (s): 3.533652 s
● C++ 优化结果在 nvprof 中的表现
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