OpenACC Julia 图形
▶ 书上的代码，逐步优化绘制 Julia 图形的代码
● 无并行优化（手动优化了变量等）
#include <stdio.h>
#include <stdlib.h>
#include <openacc.h> #define N (1024 * 8) int julia(const float cre, const float cim, float zre, float zim, const int maxIter)// 计算单点迭代次数
{
float zre2 = 0.0f, zim2 = 0.0f;
for (int iter = ; iter < maxIter; iter += ) // 一个迭代里计算两次
{
zre2 = zre * zre - zim * zim + cre, zim2 = * zre * zim + cim;
if (zre2 * zre2 + zim2 * zim2 > 4.0f)
return iter; zre = zre2 * zre2 - zim2 * zim2 + cre, zim = * zre2 * zim2 + cim;
if (zre * zre + zim * zim > 4.0f)
return iter;
}
return maxIter + + (maxIter % );
} int main()
{
const int maxIter = ; // 最大迭代次数
const float cre = -0.8350, cim = -0.2321, h = 4.0f / N; // 迭代常数和画幅步长
int *image = (int *)malloc(sizeof(int) * N * N);
FILE *pf = fopen("R:/output.txt", "w"); for (int i = ; i < N; i++)
{
for (int j = ; j < N; j++)
fprintf(pf, "%d ", julia(cre, cim, i * h - 2.0f, j * h - 2.0f, maxIter));
fprintf(pf, "\n");
} fclose(pf);
free(image);
//getchar();
return ;
}
● 输出结果（后面所有代码的输出都相同，不再写了）
● 改进 1，计算并行化
#include <stdio.h>
#include <stdlib.h>
#include <openacc.h> #define N (1024 * 8) #pragma acc routine seq
int julia(const float cre, const float cim, float zre, float zim, const int maxIter)
{
float zre2 = 0.0f, zim2 = 0.0f;
for (int iter = ; iter < maxIter; iter += )
{
zre2 = zre * zre - zim * zim + cre, zim2 = * zre * zim + cim;
if (zre2 * zre2 + zim2 * zim2 > 4.0f)
return iter; zre = zre2 * zre2 - zim2 * zim2 + cre, zim = * zre2 * zim2 + cim;
if (zre * zre + zim * zim > 4.0f)
return iter;
}
return maxIter + + (maxIter % );
} int main()
{
const int maxIter = ;
const float cre = -0.8350, cim = -0.2321, h = 4.0f / N;
int *image = (int *)malloc(sizeof(int) * N * N); #pragma acc data copyout(image[0:N * N]) // 数据域
{
#pragma acc kernels loop independent // loop 并行化，强制独立
for (int i = ; i < N; i++)
{
for (int j = ; j < N; j++)
image[i * N + j] = julia(cre, cim, i * h - 2.0f, j * h - 2.0f, maxIter);
}
}
/*// 注释掉写入文件的部分，防止 Nvvp 加入分析
FILE *pf = fopen("R:/output.txt", "w");
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
fprintf(pf, "%d ", image[i * N + j]);
fprintf(pf, "\n");
}
fclose(pf);
*/
free(image);
//getchar();
return ;
}
● 输出结果
D:\Code\OpenACC\OpenACCProject\OpenACCProject>pgcc -acc -Minfo main.c -o main_acc.exe
julia:
, Generating acc routine seq
Generating Tesla code
, FMA (fused multiply-add) instruction(s) generated
, FMA (fused multiply-add) instruction(s) generated
main:
, Generating copyout(image[:])
, Loop is parallelizable
FMA (fused multiply-add) instruction(s) generated
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, vector(4) /* blockIdx.y threadIdx.y */
, #pragma acc loop gang, vector(32) /* blockIdx.x threadIdx.x */
, FMA (fused multiply-add) instruction(s) generated D:\Code\OpenACC\OpenACCProject\OpenACCProject>main_acc.exe
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
PGI: "acc_shutdown" not detected, performance results might be incomplete.
Please add the call "acc_shutdown(acc_device_nvidia)" to the end of your application to ensure that the performance results are complete. Accelerator Kernel Timing data
D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c
main NVIDIA devicenum=
time(us): ,
: data region reached times
: data copyout transfers:
device time(us): total=, max=, min= avg=,
: compute region reached time
: kernel launched time
grid: [256x128] block: [32x4]
device time(us): total= max= min= avg=
● 改进 2，分块计算，没有明显性能提升，为异步做准备
#include <stdio.h>
#include <stdlib.h>
#include <openacc.h> #define N (1024 * 8) #pragma acc routine seq
int julia(const float cre, const float cim, float zre, float zim, const int maxIter)
{
float zre2 = 0.0f, zim2 = 0.0f;
for (int iter = ; iter < maxIter; iter += )
{
zre2 = zre * zre - zim * zim + cre, zim2 = * zre * zim + cim;
if (zre2 * zre2 + zim2 * zim2 > 4.0f)
return iter; zre = zre2 * zre2 - zim2 * zim2 + cre, zim = * zre2 * zim2 + cim;
if (zre * zre + zim * zim > 4.0f)
return iter;
}
return maxIter + + (maxIter % );
} int main()
{
const int maxIter = ;
const float cre = -0.8350, cim = -0.2321, h = 4.0f / N;
int *image = (int *)malloc(sizeof(int) * N * N); #pragma acc data copyout(image[0:N * N])
{
const int numblock = ; // 指定分块数量
for (int block = ; block < numblock; block++) // 每次计算一块
{
const int start = block * (N / numblock), end = start + N / numblock; // 每块的始末下标
#pragma acc kernels loop independent
for (int i = start; i < end; i++)
{
for (int j = ; j < N; j++)
image[i * N + j] = julia(cre, cim, i * h - 2.0f, j * h - 2.0f, maxIter);
}
}
}
/*
FILE *pf = fopen("R:/output.txt", "w");
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
fprintf(pf, "%d ", image[i * N + j]);
fprintf(pf, "\n");
}
fclose(pf);
*/
free(image);
//getchar();
return ;
}
● 输出结果
D:\Code\OpenACC\OpenACCProject\OpenACCProject>pgcc -acc -Minfo main.c -o main_acc.exe
julia:
, Generating acc routine seq
Generating Tesla code
, FMA (fused multiply-add) instruction(s) generated
, FMA (fused multiply-add) instruction(s) generated
main:
, Generating copyout(image[:])
, Loop is parallelizable
FMA (fused multiply-add) instruction(s) generated
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, vector(4) /* blockIdx.y threadIdx.y */
, #pragma acc loop gang, vector(32) /* blockIdx.x threadIdx.x */
, FMA (fused multiply-add) instruction(s) generated D:\Code\OpenACC\OpenACCProject\OpenACCProject>main_acc.exe
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
PGI: "acc_shutdown" not detected, performance results might be incomplete.
Please add the call "acc_shutdown(acc_device_nvidia)" to the end of your application to ensure that the performance results are complete. Accelerator Kernel Timing data
D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c
main NVIDIA devicenum=
time(us): ,
: data region reached times
: data copyout transfers:
device time(us): total=, max=, min= avg=,
: compute region reached times
: kernel launched times
grid: [256x128] block: [32x4]
device time(us): total= max= min= avg=
● 改进 3，分块传输，没有明显性能提升，为异步做准备
#include <stdio.h>
#include <stdlib.h>
#include <openacc.h> #define N (1024 * 8) #pragma acc routine seq
int julia(const float cre, const float cim, float zre, float zim, const int maxIter)
{
float zre2 = 0.0f, zim2 = 0.0f;
for (int iter = ; iter < maxIter; iter += )
{
zre2 = zre * zre - zim * zim + cre, zim2 = * zre * zim + cim;
if (zre2 * zre2 + zim2 * zim2 > 4.0f)
return iter; zre = zre2 * zre2 - zim2 * zim2 + cre, zim = * zre2 * zim2 + cim;
if (zre * zre + zim * zim > 4.0f)
return iter;
}
return maxIter + + (maxIter % );
} int main()
{
const int maxIter = ;
const float cre = -0.8350, cim = -0.2321, h = 4.0f / N;
int *image = (int *)malloc(sizeof(int) * N * N); #pragma acc data create(image[0:N * N]) // 改成 create，不需要从主机拷贝初始数据
{
const int numBlock = , blockSize = N * N / numBlock; // 仍然分块计算
for (int block = ; block < numBlock; block++)
{
const int start = block * (N / numBlock), end = start + N / numBlock;
#pragma acc kernels loop independent
for (int i = start; i < end; i++)
{
for (int j = ; j < N; j++)
image[i * N + j] = julia(cre, cim, i * h - 2.0f, j * h - 2.0f, maxIter);
}
#pragma acc update host(image[block * blockSize : blockSize]) // 每计算完一块就向主机回传数据
}
}
/*
FILE *pf = fopen("R:/output.txt", "w");
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
fprintf(pf, "%d ", image[i * N + j]);
fprintf(pf, "\n");
}
fclose(pf);
*/
free(image);
//getchar();
return ;
}
● 输出结果
D:\Code\OpenACC\OpenACCProject\OpenACCProject>pgcc -acc -Minfo main.c -o main_acc.exe
julia:
, Generating acc routine seq
Generating Tesla code
, FMA (fused multiply-add) instruction(s) generated
, FMA (fused multiply-add) instruction(s) generated
main:
, Generating create(image[:])
, Loop is parallelizable
FMA (fused multiply-add) instruction(s) generated
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, vector(4) /* blockIdx.y threadIdx.y */
, #pragma acc loop gang, vector(32) /* blockIdx.x threadIdx.x */
, FMA (fused multiply-add) instruction(s) generated
, Generating update self(image[block*blockSize:blockSize]) D:\Code\OpenACC\OpenACCProject\OpenACCProject>main_acc.exe
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
PGI: "acc_shutdown" not detected, performance results might be incomplete.
Please add the call "acc_shutdown(acc_device_nvidia)" to the end of your application to ensure that the performance results are complete. Accelerator Kernel Timing data
D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c
main NVIDIA devicenum=
time(us): ,
: data region reached times
: compute region reached times
: kernel launched times
grid: [256x128] block: [32x4]
elapsed time(us): total=, max=, min= avg=,
: update directive reached times
: data copyout transfers:
device time(us): total=, max=, min= avg=,
● 改进 4，异步计算 - 双向传输
#include <stdio.h>
#include <stdlib.h>
#include <openacc.h> #define N (1024 * 8) #pragma acc routine seq
int julia(const float cre, const float cim, float zre, float zim, const int maxIter)
{
float zre2 = 0.0f, zim2 = 0.0f;
for (int iter = ; iter < maxIter; iter += )
{
zre2 = zre * zre - zim * zim + cre, zim2 = * zre * zim + cim;
if (zre2 * zre2 + zim2 * zim2 > 4.0f)
return iter; zre = zre2 * zre2 - zim2 * zim2 + cre, zim = * zre2 * zim2 + cim;
if (zre * zre + zim * zim > 4.0f)
return iter;
}
return maxIter + + (maxIter % );
} int main()
{
const int maxIter = ;
const float cre = -0.8350, cim = -0.2321, h = 4.0f / N;
int *image = (int *)malloc(sizeof(int) * N * N); #pragma acc data create(image[0:N * N])
{
const int numBlock = , blockSize = N / numBlock * N;
for (int block = ; block < numBlock; block++)
{
const int start = block * (N / numBlock), end = start + N / numBlock;
#pragma acc kernels loop independent async(block + 1) // 异步计算，用块编号作标记
for (int i = start; i < end; i++)
{
for (int j = ; j < N; j++)
image[i * N + j] = julia(cre, cim, i * h - 2.0f, j * h - 2.0f, maxIter);
}
#pragma acc update host(image[block * blockSize : blockSize]) async(block + 1) // 计算完一块就异步传输
}
#pragma acc wait
}
/*
FILE *pf = fopen("R:/output.txt", "w");
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
fprintf(pf, "%d ", image[i * N + j]);
fprintf(pf, "\n");
}
fclose(pf);
*/
free(image);
//getchar();
return ;
}
● 输出结果
D:\Code\OpenACC\OpenACCProject\OpenACCProject>pgcc -acc -Minfo main.c -o main_acc.exe
julia:
, Generating acc routine seq
Generating Tesla code
, FMA (fused multiply-add) instruction(s) generated
, FMA (fused multiply-add) instruction(s) generated
main:
, Generating create(image[:])
, Loop is parallelizable
FMA (fused multiply-add) instruction(s) generated
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, vector(4) /* blockIdx.y threadIdx.y */
, #pragma acc loop gang, vector(32) /* blockIdx.x threadIdx.x */
, FMA (fused multiply-add) instruction(s) generated
, Generating update self(image[block*blockSize:blockSize]) D:\Code\OpenACC\OpenACCProject\OpenACCProject>main_acc.exe
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= queue= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= queue= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= queue= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= queue= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
PGI: "acc_shutdown" not detected, performance results might be incomplete.
Please add the call "acc_shutdown(acc_device_nvidia)" to the end of your application to ensure that the performance results are complete. Accelerator Kernel Timing data
Timing may be affected by asynchronous behavior
set PGI_ACC_SYNCHRONOUS to to disable async() clauses
D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c
main NVIDIA devicenum=
time(us): ,
: data region reached times
: compute region reached times
: kernel launched times
grid: [256x128] block: [32x4]
device time(us): total= max= min= avg=
: update directive reached times
: data copyout transfers:
device time(us): total=, max=, min= avg=,
● 使用统一内存访址（Ubuntu，win64 不支持）
● 改进 4，多设备版本 1，使用 OpenMP
#include <stdio.h>
#include <stdlib.h>
#include <omp.h>
#include <openacc.h> #define N (1024 * 8) #pragma acc routine seq
int julia(const float cre, const float cim, float zre, float zim, const int maxIter)
{
float zre2 = 0.0f, zim2 = 0.0f;
for (int iter = ; iter < maxIter; iter += )
{
zre2 = zre * zre - zim * zim + cre, zim2 = * zre * zim + cim;
if (zre2 * zre2 + zim2 * zim2 > 4.0f)
return iter; zre = zre2 * zre2 - zim2 * zim2 + cre, zim = * zre2 * zim2 + cim;
if (zre * zre + zim * zim > 4.0f)
return iter;
}
return maxIter + + (maxIter % );
} int main()
{
const int maxIter = ;
const int numBlock = acc_get_num_devices(acc_device_nvidia), blockSize = N / numBlock * N; // 使用 OpenMP 检测目标设备数量，以此作为分块数
const float cre = -0.8350, cim = -0.2321, h = 4.0f / N; int *image = (int *)malloc(sizeof(int) * N * N);
acc_init(acc_device_nvidia); // 一次性初始化全部目标设备 #pragma omp parallel num_threads(numBlock) // 使用多个线程，分别向目标设备发送任务
{
acc_set_device_num(omp_get_thread_num(), acc_device_nvidia);// 标记目标设备
#pragma omp for
for (int block = ; block < numBlock; block++)
{
const int start = block * (N / numBlock), end = start + N / numBlock;
#pragma acc data copyout(image[block * blockSize : blockSize])
{
#pragma acc kernels loop independent
for (int i = start; i < end; i++)
{
for (int j = ; j < N; j++)
image[i * N + j] = julia(cre, cim, i * h - 2.0f, j * h - 2.0f, maxIter);
}
}
}
}
/*
FILE *pf = fopen("R:/output.txt", "w");
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
fprintf(pf, "%d ", image[i * N + j]);
fprintf(pf, "\n");
}
fclose(pf);
*/
free(image);
//getchar();
return ;
}
● 输出结果
D:\Code\OpenACC\OpenACCProject\OpenACCProject>pgcc -acc -mp -Minfo main.c -o main_acc.exe
julia:
, Generating acc routine seq
Generating Tesla code
, FMA (fused multiply-add) instruction(s) generated
, FMA (fused multiply-add) instruction(s) generated
main:
, Parallel region activated
, Parallel loop activated with static block schedule
, Generating copyout(image[block*blockSize:blockSize])
, Loop is parallelizable
FMA (fused multiply-add) instruction(s) generated
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, vector(4) /* blockIdx.y threadIdx.y */
, #pragma acc loop gang, vector(32) /* blockIdx.x threadIdx.x */
, FMA (fused multiply-add) instruction(s) generated
, Barrier
, Parallel region terminated D:\Code\OpenACC\OpenACCProject\OpenACCProject>main_acc.exe
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
PGI: "acc_shutdown" not detected, performance results might be incomplete.
Please add the call "acc_shutdown(acc_device_nvidia)" to the end of your application to ensure that the performance results are complete. Accelerator Kernel Timing data
D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c
main NVIDIA devicenum=
time(us): ,
: data region reached times
: data copyout transfers:
device time(us): total=, max=, min= avg=,
: compute region reached time
: kernel launched time
grid: [256x128] block: [32x4]
device time(us): total= max= min= avg=
● 改进 5，多设备版本 2，调整 OpenMP
#include <stdio.h>
#include <stdlib.h>
#include <omp.h>
#include <openacc.h> #define N (1024 * 8) #pragma acc routine seq
int julia(const float cre, const float cim, float zre, float zim, const int maxIter)
{
float zre2 = 0.0f, zim2 = 0.0f;
for (int iter = ; iter < maxIter; iter += )
{
zre2 = zre * zre - zim * zim + cre, zim2 = * zre * zim + cim;
if (zre2 * zre2 + zim2 * zim2 > 4.0f)
return iter; zre = zre2 * zre2 - zim2 * zim2 + cre, zim = * zre2 * zim2 + cim;
if (zre * zre + zim * zim > 4.0f)
return iter;
}
return maxIter + + (maxIter % );
} int main()
{
const int maxIter = ;
const int numBlock = acc_get_num_devices(acc_device_nvidia), blockSize = N / numBlock * N;
const float cre = -0.8350, cim = -0.2321, h = 4.0f / N; int *image = (int *)malloc(sizeof(int) * N * N);
acc_init(acc_device_nvidia); #pragma omp parallel for num_threads(numBlock) // 把函数 acc_set_device_num 单独放在一起
for(int block = ;block<numBlock;block++)
acc_set_device_num(block, acc_device_nvidia); #pragma omp for num_threads(numBlock)
for (int block = ; block < numBlock; block++)
{
const int start = block * (N / numBlock), end = start + N / numBlock;
#pragma acc data copyout(image[block * blockSize : blockSize])
{
#pragma acc kernels loop independent
for (int i = start; i < end; i++)
{
for (int j = ; j < N; j++)
image[i * N + j] = julia(cre, cim, i * h - 2.0f, j * h - 2.0f, maxIter);
}
}
}
/*
FILE *pf = fopen("R:/output.txt", "w");
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
fprintf(pf, "%d ", image[i * N + j]);
fprintf(pf, "\n");
}
fclose(pf);
*/
free(image);
//getchar();
return ;
}
● 输出结果
D:\Code\OpenACC\OpenACCProject\OpenACCProject>pgcc main.c -acc -Minfo -o main_acc.exe
julia:
, Generating acc routine seq
Generating Tesla code
, FMA (fused multiply-add) instruction(s) generated
, FMA (fused multiply-add) instruction(s) generated
main:
, Generating copyout(image[block*blockSize:blockSize])
, Loop is parallelizable
FMA (fused multiply-add) instruction(s) generated
, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
, #pragma acc loop gang, vector(4) /* blockIdx.y threadIdx.y */
, #pragma acc loop gang, vector(32) /* blockIdx.x threadIdx.x */
, FMA (fused multiply-add) instruction(s) generated D:\Code\OpenACC\OpenACCProject\OpenACCProject>main_acc.exe
launch CUDA kernel file=D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c function=main
line= device= threadid= num_gangs= num_workers= vector_length= grid=256x128 block=32x4
PGI: "acc_shutdown" not detected, performance results might be incomplete.
Please add the call "acc_shutdown(acc_device_nvidia)" to the end of your application to ensure that the performance results are complete. Accelerator Kernel Timing data
D:\Code\OpenACC\OpenACCProject\OpenACCProject\main.c
main NVIDIA devicenum=
time(us): ,
: data region reached times
: data copyout transfers:
device time(us): total=, max=, min= avg=,
: compute region reached time
: kernel launched time
grid: [256x128] block: [32x4]
elapsed time(us): total=, max=, min=, avg=,
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