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我正在尝试运行一个简单的测试用例，其中动态分配的数组A被定义为外部并使用 OpenACC 上传到 GPU。全部使用 PGI 编译器。

我的 header.h文件：

     extern int *A;
     #pragma acc declare create(A)

然后，我的header.c实现：

    int *A;
    #pragma acc declare copyin(A)

然后，在main.c我有

#include "header.h"
int main(int argc, char* argv[]){
        printf("main() start\n");
        int sum=0;
        int N=0;
        if(argc==1){ 
          printf("usage: ./main.exe N");
        }else{
          N=atoi(argv[1]);  
        }
        printf("N =%d\n", N);
        A=(int*)malloc(N*sizeof(int));
        for(int i=0;i<N;i++){A[i]=i;}
        printf("almost data region\n");
        #pragma acc data copy(sum)
        {
             printf("inside data region\n");
             #pragma acc update device(A[0:N])
             #pragma acc parallel loop reduction(+:sum)
             for(int i=0;i<N;i++){
                sum+=A[i];
             }
        }
        printf("sum = %d\n",sum);
    }

我使用以下命令编译代码：

$ cc -g -lnvToolsExt -O2 -acc -ta=tesla:cc60 -c11 -mp -Minfo -Mlarge_arrays   -c  -o header.o header.c
$ cc -g -lnvToolsExt -O2 -acc -ta=tesla:cc60 -c11 -mp -Minfo -Mlarge_arrays   -c  -o main.o main.c
PGC-W-0155-Pointer value created from a nonlong integral type  (main.c: 12)
main:
     13, Generated 2 alternate versions of the loop
         Generated vector simd code for the loop
     17, Generating copy(sum)
     21, Generating update device(A[:N])
         Accelerator kernel generated
         Generating Tesla code
         21, Generating reduction(+:sum)
         22, #pragma acc loop gang, vector(128) /* blockIdx.x threadIdx.x */
PGC/x86-64 Linux 17.5-0: compilation completed with warnings
$ cc -g -lnvToolsExt -O2 -acc -ta=tesla:cc60 -c11 -mp -Minfo -Mlarge_arrays   header.o main.o -o main.exe

我的PGI编译器版本是：

$ cc -v
Export PGI=/opt/pgi/17.5.0

要执行代码：

$ ACC_NOTIFY=3 srun cuda-memcheck --show-backtrace yes main.exe 10000
upload CUDA data  file=/scratch/snx3000/ragagnin/2017/prova/main.c function=main line=17 device=0 threadid=1 variable=A bytes=8
upload CUDA data  file=/scratch/snx3000/ragagnin/2017/prova/main.c function=main line=17 device=0 threadid=1 variable=sum bytes=4
Present table dump for device[1]: NVIDIA Tesla GPU 0, compute capability 6.0, threadid=1
host:0x606780 device:0x10216200000 size:8 presentcount:0+1 line:-1 name:A
host:0x7fffffff67ac device:0x1021a400000 size:4 presentcount:1+0 line:17 name:sum
allocated block device:0x1021a400000 size:512 thread:1
FATAL ERROR: data in update device clause was not found on device 1: name=A
 file:/scratch/snx3000/ragagnin/2017/prova/main.c main line:21
main() start
N =10000
almost data region
inside data region
========= CUDA-MEMCHECK
========= Program hit CUDA_ERROR_INVALID_DEVICE (error 101) due to "invalid device ordinal" on CUDA API call to cuDevicePrimaryCtxRetain. 
=========     Saved host backtrace up to driver entry point at error
=========     Host Frame:/opt/cray/nvidia/default/lib64/libcuda.so (cuDevicePrimaryCtxRetain + 0x15c) [0x1e497c]
=========     Host Frame:/opt/pgi/17.5.0/linux86-64/17.5/lib/libaccnmp.so (__pgi_uacc_cuda_initdev + 0x962) [0x140e1]
=========     Host Frame:/opt/pgi/17.5.0/linux86-64/17.5/lib/libaccgmp.so (__pgi_uacc_enumerate + 0x173) [0x12e31]
=========     Host Frame:/opt/pgi/17.5.0/linux86-64/17.5/lib/libaccgmp.so (__pgi_uacc_initialize + 0x9b) [0x1340d]
=========     Host Frame:/opt/pgi/17.5.0/linux86-64/17.5/lib/libaccgmp.so (__pgi_uacc_dataenterstart + 0x50) [0x9de1]
=========     Host Frame:main.exe [0x16a5]
=========     Host Frame:/lib64/libc.so.6 (__libc_start_main + 0xf5) [0x206e5]
=========     Host Frame:main.exe [0x11c9]
=========
========= ERROR SUMMARY: 1 error
srun: error: nid03948: task 0: Exited with exit code 1
srun: Terminating job step 4066800.15

我认为问题在于 PGI 编译器发送variable=A bytes=8，因此忽略了我的发送请求A[0:N]。

那么，如何用 C/OpenACC 和 PGI 编译器声明一个全局动态数组呢？

4

1 回答 1

4

当您将“声明”与指针一起使用时，您正在创建一个全局设备指针，而不是指针指向的数组。因此，当您尝试更新数组时，它不存在以及运行时错误的原因。

要解决此问题，您还需要将数组添加到数据区域，例如“输入数据”指令，如下所示。当您将数组放入数据区域时，除了为数组创建空间外，运行时将返回并将其“附加”到“A”，即用正确的设备指针值填充“A”的设备副本。

您还需要通过在计算区域上放置“present(A)”来告诉编译器“A”已经存在于设备上。

请注意，不需要第二个“声明 copyin”。此外，使用“create”，设备数据未初始化，而“copyin”将使用主机值初始化变量。但是由于主机值是主机指针，所以它在设备上仍然是垃圾。所以不一定是错的，只是不需要。

% cat header.h

#include <stdio.h>
#include <stdlib.h>

extern int *A;
#pragma acc declare create(A)

% cat header.c
#include <header.h>
int *A;

% cat test.c
#include "header.h"
int main(int argc, char* argv[]){
        printf("main() start\n");
        int sum=0;
        int N=0;
        if(argc==1){
          printf("usage: ./main.exe N");
        }else{
          N=atoi(argv[1]);
        }
        printf("N =%d\n", N);
        A=(int*)malloc(N*sizeof(int));
        #pragma acc enter data create(A[0:N])

        for(int i=0;i<N;i++){A[i]=i;}
        printf("almost data region\n");
        #pragma acc data copy(sum)
        {
             printf("inside data region\n");
             #pragma acc update device(A[0:N])
             #pragma acc parallel loop present(A) reduction(+:sum)
             for(int i=0;i<N;i++){
                sum+=A[i];
             }
        }
        printf("sum = %d\n",sum);
        #pragma acc exit data delete(A)
        free(A);
        exit(0);
    }
% pgcc -I./ test.c header.c -ta=tesla:cc60 -Minfo=accel
test.c:
main:
     13, Generating enter data create(A[:N])
     17, Generating copy(sum)
     21, Generating update device(A[:N])
         Accelerator kernel generated
         Generating Tesla code
         21, Generating reduction(+:sum)
         22, #pragma acc loop gang, vector(128) /* blockIdx.x threadIdx.x */
     27, Generating exit data delete(A[:1])
header.c:
% setenv PGI_ACC_TIME 1
% a.out 1024
main() start
N =1024
almost data region
inside data region
sum = 523776

Accelerator Kernel Timing data
test.c
  main  NVIDIA  devicenum=0
    time(us): 124
    13: upload reached 1 time
        13: data copyin transfers: 1
             device time(us): total=33 max=33 min=33 avg=33
    13: data region reached 1 time
        13: data copyin transfers: 1
             device time(us): total=9 max=9 min=9 avg=9
    17: data region reached 2 times
        17: data copyin transfers: 1
             device time(us): total=33 max=33 min=33 avg=33
        26: data copyout transfers: 1
             device time(us): total=22 max=22 min=22 avg=22
    21: update directive reached 1 time
        21: data copyin transfers: 1
             device time(us): total=10 max=10 min=10 avg=10
    21: compute region reached 1 time
        21: kernel launched 1 time
            grid: [8]  block: [128]
             device time(us): total=4 max=4 min=4 avg=4
            elapsed time(us): total=589 max=589 min=589 avg=589
        21: reduction kernel launched 1 time
            grid: [1]  block: [256]
             device time(us): total=4 max=4 min=4 avg=4
            elapsed time(us): total=27 max=27 min=27 avg=27
    27: data region reached 1 time
        27: data copyin transfers: 1
             device time(us): total=9 max=9 min=9 avg=9
于 2017-10-20T17:07:58.413 回答