如何使用OpenMP提供的GPU?
我想获得一些代码,使用OpenMP在GPU上运行,但我没有成功。在我的代码中,我使用for
循环执行矩阵乘法:一次使用OpenMP pragma标记,一次没有。 (这样我就可以比较执行时间了。)在第一个循环之后,我调用omp_get_num_devices()
(这是我的主要测试,看看我是否实际连接到GPU)。无论我尝试什么,omp_get_num_devices()
始终返回0如何使用OpenMP提供的GPU?
我正在使用的计算机有两个NVIDIA Tesla K40M GPU。 CUDA 7.0和CUDA 7.5作为模块在计算机上提供,并且CUDA 7.5模块通常处于活动状态。 gcc 4.9.3,5.1.0和7.1.0都可以作为模块使用,gcc 7.1.0模块通常处于活动状态。我正在编写我的代码$ g++ -fopenmp -omptargets=nvptx64sm_35-nvidia-linux ParallelExperimenting.cpp -o ParallelExperimenting
。我已经成功使用CPU并行处理了OpenMP代码,但没有使用GPU。
我的主要目标是让omp_get_num_devices()
返回2,以证明我可以在OpenMP中检测和使用GPU。我在这里接受任何帮助将不胜感激。
这里是我使用的检查,如果被正确或不使用的GPU代码:
#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.
}
这里是运行DEVICEQUERY样本CUDA代码的结果。
./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
GCC 4.9.3和5.1.0绝对不支持OpenMP卸载到GPU。 GCC 7.1.0确实支持它,但它应该使用特殊配置选项as described here来构建。
这解决了我的问题!非常感谢!!! – Josiah
也许我在一个错误的方向。但我想帮助,因为我曾经在使用GPU的奇怪的情况下,
。
您需要位于linux的“视频”组,因此您可以使用GPU。
或全部结果从GPU返回将是0
所以我会建议你运行示例代码CUDA来检查,如果你是在我以前被卡住的情况。
这很奇怪。我不确定我是否正确描述了它。 希望它有帮助。
根据本:https://wiki.gentoo.org/wiki/NVidia/nvidia-drivers
无需访问视频卡用户(S)将需要添加到 视频组
我可能是错的,但我认为你需要对发布的代码进行一些更正(也许你已经知道了)。要真正在使用OpenMP的GPU目标运行,你需要更换:
#pragma omp for collapse(4) private(i, j, k, l)
与
#pragma omp target teams distribute parallel for collapse(4) private(i, j, k, l)
您可以验证如果内核实际上是在GPU上用“nvprof”剖析你的可执行文件运行。它应该显示在GPU上执行的内核。您还可以使用'num_teams'和'thread_limit'子句更改目标区域中的团队和线程数量,并且您应该在您的配置文件中看到相应的更改。
要以编程方式实际检查目标区域是否在目标设备上运行,我使用'omp_is_initial_device()'调用,该调用在从加速器调用时返回0。下面是一个例子:
int A[1] = {-1};
#pragma omp target
{
A[0] = omp_is_initial_device();
}
if (!A[0]) {
printf("Able to use offloading!\n");
}
我试图按照你的建议用'nvprof'来描述它。程序完成其执行后,我收到一个错误'========警告:没有CUDA应用程序分析,退出'。当我添加'omp_is_initial_device()'时,它每次都返回1。 – Josiah
这似乎强烈表明你的内核正在CPU上运行。正如Ilya提到的,你可能需要编译gcc以支持gpu。 –
为什么你需要使用一个元素的数组而不仅仅是一个简单的整数?我试过你的代码,它只适用于一个数组,但我不明白为什么。 –
你可以上传一个最低工作示例,显示你正在尝试做什么? – Richard
欢迎来到Stack Overflow!你的帖子不幸遗失了[mcve]。请访问[帮助中心](http://*.com/help)并阅读[如何提出一个好问题]部分(http://*.com/help/how-to-ask)。 –
我添加了我的测试代码。 – Josiah