C++与并行计算：利用并行计算加速程序运行

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#include <iostream>
#include <omp.h>
int main() {
    #pragma omp parallel for
    for (int i = 0; i < 10; i++) {
        std::cout << "Thread " << omp_get_thread_num() << ": Iteration " << i << std::endl;
    }
    return 0;
}

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#include <iostream>
#include <mpi.h>
int main(int argc, char** argv) {
    MPI_Init(&argc, &argv);
    int rank, size;
    MPI_Comm_rank(MPI_COMM_WORLD, &rank);
    MPI_Comm_size(MPI_COMM_WORLD, &size);
    std::cout << "Hello from process " << rank << " out of " << size << std::endl;
    MPI_Finalize();
    return 0;
}

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#include <iostream>
#include <tbb/tbb.h>
int main() {
    tbb::parallel_for(0, 10, [](int i) {
        std::cout << "Iteration " << i << " executed by thread " << tbb::this_task_arena::current_thread_index() << std::endl;
    });
    return 0;
}

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from transformers import pipeline
nlp = pipeline("sentiment-analysis")
text = "这部电影真令人惊叹！"
result = nlp(text)
print(result[0]['label'])  # 输出情感类别（积极/消极）

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from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
text = "自然语言处理是一门研究如何让计算机理解和处理人类语言的学科。"
tokens = word_tokenize(text)
freq_dist = FreqDist(tokens)
print(freq_dist.most_common(5))  # 输出频率最高的5个词语

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import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image
# 加载预训练的模型
resnet = models.resnet50(pretrained=True)
# 设置模型为评估模式
resnet.eval()
# 图像预处理
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# 读取图像
image = Image.open("image.jpg")
# 图像预处理
input_tensor = preprocess(image)
# 添加一个维度作为batch_size
input_batch = input_tensor.unsqueeze(0)
# 模型推理
with torch.no_grad():
    output = resnet(input_batch)
# 加载标签
with open("imagenet_classes.txt") as f:
    labels = [line.strip() for line in f.readlines()]
# 获取预测结果
_, predicted_idx = torch.max(output, 1)
predicted_label = labels[predicted_idx.item()]
print(predicted_label)  # 输出预测标签

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#include <iostream>
#include <omp.h>
void processImage(int* image, int width, int height) {
    #pragma omp parallel for
    for (int i = 0; i < height; i++) {
        for (int j = 0; j < width; j++) {
            // 并行处理每个像素
            int index = i * width + j;
            // 图像处理逻辑
            // ...
        }
    }
}
int main() {
    int width = 640;
    int height = 480;
    int* image = new int[width * height];
    // 填充图像数据
    for (int i = 0; i < height; i++) {
        for (int j = 0; j < width; j++) {
            int index = i * width + j;
            // 填充图像像素
            image[index] = index % 256;
        }
    }
    // 处理图像
    processImage(image, width, height);
    // 输出部分处理后的图像数据
    for (int i = 0; i < 10; i++) {
        for (int j = 0; j < 10; j++) {
            int index = i * width + j;
            std::cout << image[index] << " ";
        }
        std::cout << std::endl;
    }
    delete[] image;
    return 0;
}