OpenMP を使用して GPU で実行するコードを取得しようとしていますが、うまくいきません。私のコードでは、forループを使用して行列乗算を実行しています。1 回は OpenMP プラグマ タグを使用し、もう 1 回は使用しません。(これは、実行時間を比較できるようにするためです。) 最初のループの後で呼び出しますomp_get_num_devices()(これは、実際に GPU に接続しているかどうかを確認するための主なテストです。) 何を試しても、omp_get_num_devices()常に 0 を返します。
私が使用しているコンピューターには、2 つのNVIDIA Tesla K40M GPUが搭載されています。CUDA 7.0 と CUDA 7.5 はコンピューターでモジュールとして利用でき、CUDA 7.5 モジュールは通常アクティブです。gcc 4.9.3、5.1.0、および 7.1.0 はすべてモジュールとして利用でき、通常は gcc 7.1.0 モジュールがアクティブです。でコードをコンパイルしています$ g++ -fopenmp -omptargets=nvptx64sm_35-nvidia-linux ParallelExperimenting.cpp -o ParallelExperimenting。CPU を使用して OpenMP コードを正常に並列化しましたが、GPU では並列化できませんでした。
ここでの主な目標はomp_get_num_devices()、OpenMP で GPU を検出して使用できることの証明として 2 を返すことです。ここで私が受け取った助けは大歓迎です。
GPUが正しく使用されているかどうかを確認するために使用しているコードは次のとおりです。
#include <omp.h>
#include <fstream>
#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <time.h>
#include <iomanip>
#include <cstdio>
#include <stdlib.h>
#include <iostream>
#include <time.h>
using namespace std;
double A [501][501];
double B [501][501];
double C [501][501][501];
double D [501][501];
double E [501][501];
double F [501][501][501];
double dummyvar;
int Mapped [501];
int main() {
int i, j, k, l, N, StallerGPU, StallerCPU;
//
N = 500;
// Variables merely uses to make the execution take longer and to
// exaggurate the difference in performance between first and second
// calculation
StallerGPU = 200;
StallerCPU = 200;
std::cout << " N = " << N << "\n";
// generate matrix to be used in first calculation
for (i=0; i<N; i++) {
for (k=0; k<N; k++) {
if (i == k) {
A[i][k] = i+1;
} else {
A[i][k] = i * k / N;
}
}
}
// generate other matrix to be used for the first calculation
for (k=0; k<N; k++) {
for (j=0; j<N; j++) {
B[k][j] = 2*(N-1)-k-j;
}
}
// Slightly adjusted matrices for second calculation
for (i=0; i<N; i++) {
for (k=0; k<N; k++) {
if (i == k) {
D[i][k] = i+2;
} else {
D[i][k] = i * k / N - 1;
}
}
}
for (k=0; k<N; k++) {
for (j=0; j<N; j++) {
E[k][j] = 2*(N+1)-k-j;
}
}
dummyvar = 0;
//Run the multiplication in parallel using GPUs
double diff;
time_t time1;
time1 = time( NULL ); // CPU time counter
cout << endl << " GPU section begins at " << ctime(&time1) << endl;
// This pragma is frequently changed to try different tags
#pragma omp for collapse(4) private(i, j, k, l)
for (i=0; i<N; i++) {
// Mapped[i] = omp_is_initial_device();
for (j=0; j<N; j++) {
for (k=0; k<N; k++) {
for(l = 0; l < StallerGPU; l++ ) {
C[i][j][k] = A[i][k] * B[k][j] ;
dummyvar += A[i][k] * B[k][j] * (l + 1);
}
}
// cout << " i " << i << endl;
}
}
//record the time it took to run the multiplication
time_t time2 = time( NULL );
cout << " number of devices: " << omp_get_num_devices() << endl;
cout << " dummy variable: " << dummyvar << endl;
float cpumin = difftime(time2,time1);
diff = difftime(time2,time1);
cout << " stopping at delta GPU time: " << cpumin << endl;
cout << " terminating at " << ctime(&time2) << endl;
cout << " GPU time elasped " << diff << " s" << endl;
cout << endl;
dummyvar = 0;
time_t time3 = time( NULL );
cout << endl << " CPU section begins at " << ctime(&time3) << endl;
// #pragma omp single
for (i=0; i<N; i++) {
for (j=0; j<N; j++) {
for (k=0; k<N; k++) {
for (int l=0; l<StallerCPU; l++) {
F[i][j][k] = D[i][k] * E[k][j];
dummyvar += D[i][k] * E[k][j] * (l - 1);
}
}
}
}
// the sum to complete the matrix calculation is left out here, but would
// only be used to check if the result of the calculation is correct
time_t time4 = time( NULL );
cpumin = difftime(time4,time3);
diff = difftime(time4,time3);
cout << " dummy variable: " << dummyvar << endl;
cout << " stopping at delta CPU time: " << cpumin << endl;
cout << " terminating at " << ctime(&time4) << endl;
cout << " CPU time elasped " << diff << " s" << endl;
//Compare the time it took to confirm that we actually used GPUs to parallelize.
}
これは、deviceQuery サンプル CUDA コードを実行した結果です。
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 2 CUDA Capable device(s)
Device 0: "Tesla K40m"
CUDA Driver Version / Runtime Version 7.5 / 7.5
CUDA Capability Major/Minor version number: 3.5
Total amount of global memory: 11520 MBytes (12079136768 bytes)
(15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores
GPU Max Clock rate: 745 MHz (0.75 GHz)
Memory Clock rate: 3004 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 1572864 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 130 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 1: "Tesla K40m"
CUDA Driver Version / Runtime Version 7.5 / 7.5
CUDA Capability Major/Minor version number: 3.5
Total amount of global memory: 11520 MBytes (12079136768 bytes)
(15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores
GPU Max Clock rate: 745 MHz (0.75 GHz)
Memory Clock rate: 3004 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 1572864 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 131 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
> Peer access from Tesla K40m (GPU0) -> Tesla K40m (GPU1) : Yes
> Peer access from Tesla K40m (GPU1) -> Tesla K40m (GPU0) : Yes
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 2, Device0 = Tesla K40m, Device1 = Tesla K40m
Result = PASS