【HPC】 OpenACC介绍

References

介绍

Open Accelerators，OpenACC
编译器：PGI，nvc或nvc++，linux下PGI编译器安装
针对GPU，OpenMP模型在GPU上的扩展，支持AMD GPU

入门例程

示例代码

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// 代码 1：test.cpp，使用命令编译：nvc++ -o test -gpu=mem:managed test.cpp
#include <iostream>
#include <vector>
#include <openacc.h>

// Function to initialize the vectors with values
void initialize(std::vector<double>& a, std::vector<double>& b, int n) {
	for(int i = 0; i < n; ++i) {
		a[i] = static_cast<double>(i);
		b[i] = static_cast<double>(2 * i);
	}
}

// detect if GPU is actually running
void detect_gpu()
{
	double a[100], b[100];
	#pragma acc parallel loop
	for (int i = 0; i < 100; ++i) {
		if (i == 10) {
			if (acc_on_device(acc_device_not_host))
				printf("Executing on GPU.\n");
			else
				printf("Not executing on GPU.\n");
		}
		a[i] += b[i];
	}
}

int main() {
	const int n = 1000000; // Size of the vectors
	std::vector<double> a(n), b(n), c(n);
	double *pa = a.data(), *pb = b.data(), *pc = c.data();

	// Initialize vectors a and b
	initialize(a, b, n);

	detect_gpu();

	// Using OpenACC to offload the following computation to an accelerator
	// and explicitly handle data movement
#pragma acc data copyin(pa[0:n], pb[0:n]) copyout(pc[0:n])
	{
#pragma acc parallel loop
		for(int i = 0; i < n; ++i)
			pc[i] = pa[i] + pb[i];
	}

	// Display the first 10 results
	for(int i = 0; i < 10; ++i) {
		std::cout << "c[" << i << "] = " << c[i] << std::endl;
	}
}

编译器选项

-ta=tesla: Compiler option to target NVIDIA Tesla GPUs.
-Minfo=accel: Provides feedback about the code generated by the compiler.

常用命令

循环

#pragma acc parallel: GPU 并行运算
#pragma acc kernels: Identifies a code block for parallelization, allowing the compiler to automatically manage parallelism.
#pragma acc loop: Used within parallel or kernels regions to indicate loops that should be parallelized. 函数和变量
#pragma acc routine: 让一个函数可以在 GPU 代码中被调用（也可以在 CPU 代码调用）。
#pragma acc declare: Used for declaring variables or creating a data region. 数据传输
#pragma acc data: Manages data movement to and from the GPU.
#pragma acc enter data: Specifies data that should be moved to the GPU.
#pragma acc exit data: Specifies data to be moved back from the GPU.
#pragma acc update: Synchronizes data between the host and the GPU.
copy, copyin, copyout, create, present: Clauses for data construct to define how data is handled (e.g., whether it’s copied to/from the GPU or just created there). 线程精细控制
gang, worker, vector: Used with loop directive to control how loop iterations are distributed over parallel execution units.
collapse(n): Collapses nested loops to enhance parallelism.
reduction(operator:list): Performs a reduction operation (like sum, max) across parallel elements.