[aws] Add support for benchmarks in cpp

benchmarks/200.multimedia/210.thumbnailer/config.json

@@ -1,6 +1,7 @@
 {
   "timeout": 60,
   "memory": 256,
-  "languages": ["python", "nodejs"],
-  "modules": ["storage"]
+  "languages": ["python", "nodejs", "cpp"],
+  "modules": ["storage"],
+  "cpp_dependencies": ["opencv", "boost"]
 }

benchmarks/200.multimedia/210.thumbnailer/cpp/function.hpp

@@ -0,0 +1,27 @@
+
+#include <opencv2/opencv.hpp>
+#include <opencv2/imgcodecs.hpp>
+
+void thumbnailer(cv::Mat &in, int64_t width, int64_t height, cv::Mat &out)
+{
+  try
+  {
+    // Calculate thumbnail size while maintaining aspect ratio
+    int orig_width = in.cols;
+    int orig_height = in.rows;
+
+    double scale_w = static_cast<double>(width) / orig_width;
+    double scale_h = static_cast<double>(height) / orig_height;
+    double scale = std::min(scale_w, scale_h); // Use smaller scale to fit within bounds
+
+    int new_width = static_cast<int>(orig_width * scale);
+    int new_height = static_cast<int>(orig_height * scale);
+
+    // Resize image (equivalent to PIL's thumbnail method)
+    cv::resize(in, out, cv::Size(new_width, new_height), cv::INTER_LINEAR);
+  }
+  catch (const cv::Exception &e)
+  {
+    std::cerr << "OpenCV error: " << e.what() << std::endl;
+  }
+}

benchmarks/200.multimedia/210.thumbnailer/cpp/main.cpp

@@ -0,0 +1,117 @@
+
+#include <aws/core/Aws.h>
+#include <aws/core/utils/json/JsonSerializer.h>
+
+#include <boost/uuid/uuid_generators.hpp>
+#include <boost/uuid/uuid.hpp>
+#include <boost/uuid/uuid_io.hpp>
+
+#include <iostream>
+#include <vector>
+
+#include "function.hpp"
+#include "storage.hpp"
+#include "utils.hpp"
+
+Aws::Utils::Json::JsonValue function(Aws::Utils::Json::JsonView request)
+{
+  sebs::Storage client_ = sebs::Storage::get_client();
+
+  auto bucket_obj = request.GetObject("bucket");
+  if (!bucket_obj.IsObject())
+  {
+    Aws::Utils::Json::JsonValue error;
+    error.WithString("error", "Bucket object is not valid.");
+    return error;
+  }
+  auto bucket_name = bucket_obj.GetString("bucket");
+  auto input_key_prefix = bucket_obj.GetString("input");
+  auto output_key_prefix = bucket_obj.GetString("output");
+
+  auto image_name = request.GetObject("object").GetString("key");
+  auto width = request.GetObject("object").GetInteger("width");
+  auto height = request.GetObject("object").GetInteger("height");
+
+  std::string body_str;
+  uint64_t download_time;
+  {
+    std::string input_key = input_key_prefix + "/" + image_name;
+    auto ans = client_.download_file(bucket_name, input_key);
+    body_str = std::get<0>(ans);
+    download_time = std::get<1>(ans);
+
+    if (body_str.empty())
+    {
+      Aws::Utils::Json::JsonValue error;
+      error.WithString("error", "Failed to download object from S3: " + input_key);
+      return error;
+    }
+  }
+
+  std::vector<char> vectordata(body_str.begin(), body_str.end());
+  cv::Mat image = imdecode(cv::Mat(vectordata), 1);
+
+  // Apply the thumbnailer function and measure the computing time
+  cv::Mat image2;
+  uint64_t computing_time;
+  {
+    auto start_time = timeSinceEpochMicrosec();
+    thumbnailer(image, width, height, image2);
+    computing_time = timeSinceEpochMicrosec() - start_time;
+  }
+
+  std::vector<unsigned char> out_buffer;
+  cv::imencode(".jpg", image2, out_buffer);
+
+  // Create a unique key name for the output image
+  std::string key_name;
+  {
+    std::string output_key = output_key_prefix + "/" + image_name;
+    std::string name, extension;
+    if (output_key.find_last_of('.') != std::string::npos)
+    {
+      name = output_key.substr(0, output_key.find_last_of('.'));
+      extension = output_key.substr(output_key.find_last_of('.'));
+    }
+    else
+    {
+      name = output_key;
+      extension = "";
+    }
+    key_name = name + "."
+        + boost::uuids::to_string(boost::uuids::random_generator()())
+        + extension;
+  }
+
+  // Upload the resulting image to S3
+  // If the upload fails, return an error
+  Aws::String upload_data(out_buffer.begin(), out_buffer.end());
+
+  uint64_t upload_time = client_.upload_random_file(
+      bucket_name, key_name, true,
+      reinterpret_cast<char *>(out_buffer.data()), out_buffer.size());
+
+  if (upload_time == 0)
+  {
+    Aws::Utils::Json::JsonValue error;
+    error.WithString("error", "Failed to upload object to S3: " + key_name);
+    return error;
+  }
+
+
+  Aws::Utils::Json::JsonValue val;
+  Aws::Utils::Json::JsonValue result;
+  Aws::Utils::Json::JsonValue measurements;
+
+  result.WithString("bucket", bucket_name);
+  result.WithString("key", key_name);
+  val.WithObject("result", result);
+
+  measurements.WithInteger("download_time", download_time);
+  measurements.WithInteger("upload_time", upload_time);
+  measurements.WithInteger("compute_time", computing_time);
+  measurements.WithInteger("download_size", vectordata.size());
+  measurements.WithInteger("upload_size", out_buffer.size());
+  val.WithObject("measurements", measurements);
+  return val;
+}

benchmarks/400.inference/411.image-recognition/config.json

@@ -1,6 +1,7 @@
 {
   "timeout": 60,
   "memory": 512,
-  "languages": ["python"],
-  "modules": ["storage"]
+  "languages": ["python", "cpp"],
+  "modules": ["storage"],
+  "cpp_dependencies": ["torch", "opencv"]
 }

benchmarks/400.inference/411.image-recognition/cpp/main.cpp

@@ -0,0 +1,122 @@
+#include <aws/core/Aws.h>
+#include <aws/core/utils/json/JsonSerializer.h>
+
+#include <boost/uuid/uuid_generators.hpp>
+#include <boost/uuid/uuid.hpp>
+#include <boost/uuid/uuid_io.hpp>
+
+#include <iostream>
+#include <vector>
+
+#include "utils.hpp"
+#include "storage.hpp"
+
+#include <torch/torch.h>
+#include <torch/script.h>
+
+#include <torchvision/vision.h>
+#include <torchvision/models/resnet.h>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/opencv.hpp>
+
+
+#define kIMAGE_SIZE 224
+#define kCHANNELS 3
+#define kTOP_K 3
+
+
+bool load_image(cv::Mat &image)
+{
+
+    // image = cv::imread(file_name);  // CV_8UC3
+    if (image.empty() || !image.data)
+    {
+        return false;
+    }
+    cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
+
+    int w = image.size().width, h = image.size().height;
+    cv::Size scale((int)256 * ((float)w) / h, 256);
+    cv::resize(image, image, scale);
+    w = image.size().width, h = image.size().height;
+    image = image(cv::Range(16, 240), cv::Range(80, 304));
+    image.convertTo(image, CV_32FC3, 1.0f / 255.0f);
+
+    return true;
+}
+
+int recognition(cv::Mat &image)
+{
+
+    static bool initialized = false;
+    static torch::jit::script::Module module;
+    if (!initialized)
+    {
+        try
+        {
+            std::cout << "Initialize ResNet50 model" << std::endl;
+            module = torch::jit::load("./resnet50.pt");
+        }
+        catch (const c10::Error &e)
+        {
+            std::cerr << "error loading the model\n";
+            return -1;
+        }
+        initialized = true;
+    }
+
+    if (load_image(image))
+    {
+
+        auto input_tensor = torch::from_blob(
+            image.data, {1, kIMAGE_SIZE, kIMAGE_SIZE, kCHANNELS});
+        input_tensor = input_tensor.permute({0, 3, 1, 2});
+        // input_tensor = input_tensor.permute({0, 1, 2, 3});
+        input_tensor[0][0] = input_tensor[0][0].sub_(0.485).div_(0.229);
+        input_tensor[0][1] = input_tensor[0][1].sub_(0.456).div_(0.224);
+        input_tensor[0][2] = input_tensor[0][2].sub_(0.406).div_(0.225);
+
+        torch::Tensor out_tensor = module.forward({input_tensor}).toTensor();
+        auto results = out_tensor.sort(-1, true);
+        auto softmaxs = std::get<0>(results)[0].softmax(0);
+        auto indexs = std::get<1>(results)[0];
+
+        std::cout << indexs[0].item<int>() << " " << softmaxs[0].item<double>() << std::endl;
+        return indexs[0].item<int>();
+        // for(int i = 0; i < 100; ++i)
+        //	std::cout << softmaxs[i].item<double>() << ' ';
+        // std::cout << '\n';
+        // for(int i = 0; i < 100; ++i)
+        //	std::cout << indexs[i].item<int>() << ' ';
+        // std::cout << '\n';
+    }
+
+    return -1;
+}
+
+Aws::Utils::Json::JsonValue function(Aws::Utils::Json::JsonView request)
+{
+    sebs::Storage client_ = sebs::Storage::get_client();
+
+    auto bucket_obj = request.GetObject("bucket");
+    if (!bucket_obj.IsObject())
+    {
+        Aws::Utils::Json::JsonValue error;
+        error.WithString("error", "Bucket object is not valid.");
+        return error;
+    }
+
+    auto bucket_name = bucket_obj.GetString("bucket");
+    auto input_prefix = bucket_obj.GetString("input");
+    auto model_prefix = bucket_obj.GetString("model");
+    auto key = request.GetObject("object").GetString("input");
+    auto model_key = request.GetObject("object").GetString("model");
+
+
+
+    Aws::Utils::Json::JsonValue val;
+    Aws::Utils::Json::JsonValue result;
+    Aws::Utils::Json::JsonValue measurements;
+
+    return val;
+}

benchmarks/500.scientific/501.graph-pagerank/config.json

@@ -1,6 +1,7 @@
 {
   "timeout": 120,
   "memory": 512,
-  "languages": ["python"],
-  "modules": []
+  "languages": ["python", "cpp"],
+  "modules": [],
+  "cpp_dependencies": ["igraph"]
 }

benchmarks/500.scientific/501.graph-pagerank/cpp/function.hpp

@@ -0,0 +1,56 @@
+
+#include <igraph.h>
+
+#include <cfloat>
+
+#include "utils.hpp"
+
+igraph_real_t graph_pagerank
+(int size, uint64_t seed, uint64_t &graph_generation_time_ms, uint64_t &compute_pr_time_ms)
+{
+  igraph_t graph;
+  igraph_vector_t pagerank;
+  igraph_real_t value;
+
+  igraph_rng_seed(igraph_rng_default(), seed);
+  {
+    uint64_t start_time = timeSinceEpochMicrosec();
+    igraph_barabasi_game(
+      /* graph=    */ &graph,
+      /* n=        */ size,
+      /* power=    */ 1,
+      /* m=        */ 10,
+      /* outseq=   */ NULL,
+      /* outpref=  */ 0,
+      /* A=        */ 1.0,
+      /* directed= */ 0,
+      /* algo=     */ IGRAPH_BARABASI_PSUMTREE_MULTIPLE,
+      /* start_from= */ 0
+    );
+    graph_generation_time_ms = (timeSinceEpochMicrosec() - start_time);
+  }
+
+  igraph_vector_init(&pagerank, 0);
+  {
+    uint64_t start_time = timeSinceEpochMicrosec();
+    igraph_pagerank(&graph, IGRAPH_PAGERANK_ALGO_PRPACK,
+                    &pagerank, &value,
+                    igraph_vss_all(), IGRAPH_DIRECTED,
+                    /* damping */ 0.85, /* weights */ NULL,
+                    NULL /* not needed with PRPACK method */);
+    compute_pr_time_ms = (timeSinceEpochMicrosec() - start_time);
+  }
+  /* Check that the eigenvalue is 1, as expected. */
+  if (fabs(value - 1.0) > 32*DBL_EPSILON) {
+      fprintf(stderr, "PageRank failed to converge.\n");
+      return 1;
+  }
+
+  igraph_real_t result = VECTOR(pagerank)[0];
+
+  igraph_vector_destroy(&pagerank);
+  igraph_destroy(&graph);
+
+  return result;
+}
+