diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 55bf264..da1270e 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -33,7 +33,8 @@ "ms-vscode.cmake-tools", "ms-azuretools.vscode-docker", "ms-vscode.cpptools-themes", - "ms-iot.vscode-ros" + "ms-iot.vscode-ros", + "GitHub.github-vscode-theme" ] } } diff --git a/analysis/analysis.ipynb b/analysis/analysis.ipynb index 5c492a7..9fe4dbf 100644 --- a/analysis/analysis.ipynb +++ b/analysis/analysis.ipynb @@ -58,9 +58,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of records: 11236\n", - "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99799}, 'time': 0.0001}\n", - "Operation types: ['start_work', 'get_next_executable', 'next_deadline', 'end_work', 'wait_for_work']\n" + "Number of records: 11755\n", + "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99884}, 'time': 0.0001}\n", + "Operation types: ['wait_for_work', 'next_deadline', 'end_work', 'get_next_executable', 'start_work']\n" ] } ], @@ -164,18 +164,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9f7b2b16a21a439c849b44f675f73fbc", + "model_id": "f4ac2c553514434b9f1980bd9bc391d7", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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"get_next_executable", "duration": 3, "result": 1}, "time": 67270355}, +{"entry": {"operation": "wait_for_work", "wait_duration": 25}, "time": 67270356}, +{"entry": {"operation": "get_next_executable", "duration": 0, "result": 0}, "time": 67270356}, +{"entry": {"operation": "wait_for_work", "wait_duration": 62}, "time": 67270356}, +{"entry": {"operation": "get_next_executable", "duration": 4, "result": 1}, "time": 67270356}, +{"entry": {"operation": "wait_for_work", "wait_duration": 14}, "time": 67270376}, +{"entry": {"operation": "get_next_executable", "duration": 0, "result": 0}, "time": 67270376}, +{"entry": {"operation": "wait_for_work", "wait_duration": 717}, "time": 67270377}, +{"entry": {"operation": "get_next_executable", "duration": 1, "result": 0}, "time": 67270377}, +{"entry": {"operation": "wait_for_work", "wait_duration": 54755}, "time": 67270432}, +{"entry": {"operation": "get_next_executable", "duration": 8, "result": 1}, "time": 67270432}, +{"entry": {"operation": "wait_for_work", "wait_duration": 41}, "time": 67270432}, +{"entry": {"operation": "get_next_executable", "duration": 4, "result": 1}, "time": 67270432}, +{"entry": {"operation": "next_deadline", "chain_id": 0, "deadline": 67220632}, "time": 67220432}, +{"entry": {"operation": "next_deadline", "chain_id": 1, "deadline": 67220632}, "time": 67220432}] diff --git a/src/casestudy/src/casestudy_2023customfile_singlethread.cpp b/src/casestudy/src/casestudy_2023customfile_singlethread.cpp index 1b6d1a9..70c4ced 100755 --- a/src/casestudy/src/casestudy_2023customfile_singlethread.cpp +++ b/src/casestudy/src/casestudy_2023customfile_singlethread.cpp @@ -100,28 +100,9 @@ int main(int argc, char *argv[]) { std::cout << "ros_experiment: sched_setscheduler failed: " << result << ": " << strerror(errno) << std::endl; } - // calibrate the dummy load - // Naive way to calibrate dummy workload for current system - int dummy_load_calib = 1; - while (1) - { - timeval ctime, ftime; - int duration_us; - gettimeofday(&ctime, NULL); - dummy_load(100); // 100ms - gettimeofday(&ftime, NULL); - duration_us = (ftime.tv_sec - ctime.tv_sec) * 1000000 + (ftime.tv_usec - ctime.tv_usec); - RCLCPP_INFO(rclcpp::get_logger("rclcpp"), "dummy_load_calib: %d (duration_us: %d ns)", dummy_load_calib, duration_us); - if (abs(duration_us - 100 * 1000) < 500) - { // error margin: 500us - break; - } - dummy_load_calib = 100 * 1000 * dummy_load_calib / duration_us; - if (dummy_load_calib <= 0) - dummy_load_calib = 1; - DummyLoadCalibration::setCalibration(dummy_load_calib); - } - // DummyLoadCalibration::setCalibration(2900); + // calibrate the dummy load for the current system + calibrate_dummy_load(); + ExperimentConfig config; // config.chain_lengths = {4, 3, 4}; diff --git a/src/casestudy/src/casestudy_2024ours_executor2executor.cpp b/src/casestudy/src/casestudy_2024ours_executor2executor.cpp index 63f35a9..55720cc 100755 --- a/src/casestudy/src/casestudy_2024ours_executor2executor.cpp +++ b/src/casestudy/src/casestudy_2024ours_executor2executor.cpp @@ -112,29 +112,10 @@ int main(int argc, char *argv[]) { std::cout << "ros_experiment: sched_setscheduler failed: " << result << ": " << strerror(errno) << std::endl; } - // calibrate the dummy load - // Naive way to calibrate dummy workload for current system - int dummy_load_calib = 1; - while (1) { - timeval ctime, ftime; - int duration_us; - gettimeofday(&ctime, NULL); - dummy_load(100); // 100ms - gettimeofday(&ftime, NULL); - duration_us = (ftime.tv_sec - ctime.tv_sec) * 1000000 + - (ftime.tv_usec - ctime.tv_usec); - RCLCPP_INFO(rclcpp::get_logger("rclcpp"), - "dummy_load_calib: %d (duration_us: %d ns)", dummy_load_calib, - duration_us); - if (abs(duration_us - 100 * 1000) < 500) { // error margin: 500us - break; - } - dummy_load_calib = 100 * 1000 * dummy_load_calib / duration_us; - if (dummy_load_calib <= 0) - dummy_load_calib = 1; - DummyLoadCalibration::setCalibration(dummy_load_calib); - } - // DummyLoadCalibration::setCalibration(2900); + + // calibrate the dummy load for the current system + calibrate_dummy_load(); + ExperimentConfig config; // runtimes and timers subject to change diff --git a/src/casestudy/src/casestudy_2024ours_latency.cpp b/src/casestudy/src/casestudy_2024ours_latency.cpp index fdccb61..a1b9d63 100755 --- a/src/casestudy/src/casestudy_2024ours_latency.cpp +++ b/src/casestudy/src/casestudy_2024ours_latency.cpp @@ -113,29 +113,9 @@ int main(int argc, char *argv[]) { std::cout << "ros_experiment: sched_setscheduler failed: " << result << ": " << strerror(errno) << std::endl; } - // calibrate the dummy load - // Naive way to calibrate dummy workload for current system - int dummy_load_calib = 1; - while (1) { - timeval ctime, ftime; - int duration_us; - gettimeofday(&ctime, NULL); - dummy_load(100); // 100ms - gettimeofday(&ftime, NULL); - duration_us = (ftime.tv_sec - ctime.tv_sec) * 1000000 + - (ftime.tv_usec - ctime.tv_usec); - RCLCPP_INFO(rclcpp::get_logger("rclcpp"), - "dummy_load_calib: %d (duration_us: %d ns)", dummy_load_calib, - duration_us); - if (abs(duration_us - 100 * 1000) < 500) { // error margin: 500us - break; - } - dummy_load_calib = 100 * 1000 * dummy_load_calib / duration_us; - if (dummy_load_calib <= 0) - dummy_load_calib = 1; - DummyLoadCalibration::setCalibration(dummy_load_calib); - } - // DummyLoadCalibration::setCalibration(2900); + // calibrate the dummy load for the current system + calibrate_dummy_load(); + ExperimentConfig config; // runtimes and timers subject to change diff --git a/src/casestudy/src/casestudy_example.cpp b/src/casestudy/src/casestudy_example.cpp index 543e217..a944f9b 100644 --- a/src/casestudy/src/casestudy_example.cpp +++ b/src/casestudy/src/casestudy_example.cpp @@ -1,15 +1,20 @@ -#include "rclcpp/rclcpp.hpp" -#include "priority_executor/priority_executor.hpp" -#include "priority_executor/priority_memory_strategy.hpp" -#include "casestudy_tools/test_nodes.hpp" +#include +#include #include #include +#include + #include -#include "std_msgs/msg/string.hpp" -#include "casestudy_tools/primes_workload.hpp" #include -#include "casestudy_tools/primes_workload.hpp" + +#include +#include + +#include "casestudy_tools/test_nodes.hpp" #include "casestudy_tools/experiment.hpp" +#include "casestudy_tools/primes_workload.hpp" +#include "priority_executor/priority_executor.hpp" +#include "priority_executor/priority_memory_strategy.hpp" // bool should_do_task = true; std::atomic should_do_task(true); @@ -36,10 +41,11 @@ void run_one_executor(std::function &)> exec_fun, int idx int result = sched_setscheduler(0, SCHED_FIFO, ¶m); if (result != 0) { - std::cout << "ros_experiment: sched_setscheduler failed: " << result << ": " << strerror(errno) << std::endl; + std::cerr << "ros_experiment: sched_setscheduler failed: " << result << ": " << strerror(errno) << std::endl; + std::cerr << "This operation requires root privileges. Please run the program with sufficient permissions." << std::endl; + exit(EXIT_FAILURE); } - - // set process name + // Set the process name to "ros_experiment" for easier identification during debugging or system monitoring. prctl(PR_SET_NAME, "ros_experiment", 0, 0, 0); exec_fun(should_do_task); @@ -59,12 +65,15 @@ int ros_experiment(int argc, char **arg, std::string file_name, ExperimentConfig // TODO: move experiment configuration to main config.executor_type = executor_type; + // Enable parallel mode for the experiment configuration. + // When set to true, this allows multiple executors to run concurrently, + // leveraging multi-threading to improve performance on multi-core systems. config.parallel_mode = true; config.nodes.push_back(node); Experiment experiment(config); // std::string result_log = experiment.run(should_do_task); - std::vector &)>> exec_funs = experiment.getRunFunctions(); + std::vector &)> > exec_funs = experiment.getRunFunctions(); std::cout << "got " << exec_funs.size() << " executors" << std::endl; std::vector exec_threads; int i = 0; @@ -87,30 +96,9 @@ int ros_experiment(int argc, char **arg, std::string file_name, ExperimentConfig int main(int argc, char *argv[]) { - // calibrate the dummy load - // Naive way to calibrate dummy workload for current system - int dummy_load_calib = 1; - while (1) - { - timeval ctime, ftime; - int duration_us; - gettimeofday(&ctime, NULL); - dummy_load(100); // 100ms - gettimeofday(&ftime, NULL); - duration_us = (ftime.tv_sec - ctime.tv_sec) * 1000000 + (ftime.tv_usec - ctime.tv_usec); - RCLCPP_INFO(rclcpp::get_logger("rclcpp"), "dummy_load_calib: %d (duration_us: %d ns)", dummy_load_calib, duration_us); - if (abs(duration_us - 100 * 1000) < 500) - { // error margin: 500us - break; - } - dummy_load_calib = 100 * 1000 * dummy_load_calib / duration_us; - if (dummy_load_calib <= 0) - { - dummy_load_calib = 1; - } - DummyLoadCalibration::setCalibration(dummy_load_calib); - } - // DummyLoadCalibration::setCalibration(2900); + // calibrate the dummy load for the current system + calibrate_dummy_load(); + ExperimentConfig config; config.chain_lengths = {2, 2}; config.node_ids = {{0, 1}, {2, 3}}; @@ -126,7 +114,20 @@ int main(int argc, char *argv[]) sanity_check_config(config); - std::thread ros_task(ros_experiment, argc, argv, "cs_example", config); + std::thread ros_task([&]() { + try + { + ros_experiment(argc, argv, "cs_example", config); + } + catch (const std::exception &e) + { + std::cerr << "Exception in ros_experiment: " << e.what() << std::endl; + } + catch (...) + { + std::cerr << "Unknown exception in ros_experiment." << std::endl; + } + }); std::cout << "tasks started" << std::endl; ros_task.join(); std::cout << "tasks joined" << std::endl; diff --git a/src/casestudy_tools/include/casestudy_tools/primes_workload.hpp b/src/casestudy_tools/include/casestudy_tools/primes_workload.hpp index a7d2373..d6c1dc8 100755 --- a/src/casestudy_tools/include/casestudy_tools/primes_workload.hpp +++ b/src/casestudy_tools/include/casestudy_tools/primes_workload.hpp @@ -1,22 +1,44 @@ #ifndef RTIS_PRIMES_WORKLOAD #define RTIS_PRIMES_WORKLOAD -#include -#include + #include #include -typedef double ktimeunit; -ktimeunit nth_prime_silly(double millis = 100); -void dummy_load(int load_ms); -// create a global variable to store the calibration value -class DummyLoadCalibration + +#include +#include + +// Parameters for the dummy load calibration +constexpr int TARGET_DURATION_MS = 100; +constexpr int MAX_ITERATIONS = 100; +constexpr int ACCEPTABLE_MARGIN_US = 500; + +/** + * @brief Dummy load calibration namespace. + * This namespace contains functions and variables for calibrating the dummy load. + * It uses an atomic integer to store the calibration factor. + * The calibration factor is used to adjust the workload of the dummy load function. + */ +namespace DummyLoadCalibration { -public: - static int getCalibration(); - static void setCalibration(int calib); + inline std::atomic calibration{1}; -private: - static int calibration; -}; + inline void setCalibration(int calib); + + inline int getCalibration(); +} + +/** + * @brief Dummy load function to simulate a workload. + * This function performs a busy wait for a specified duration. + * It is used to calibrate the dummy load for the current system. + */ +void calibrate_dummy_load(); + +/** + * @brief Dummy load function to simulate a workload. + * This function performs a busy wait for a specified duration. + * It is used to calibrate the dummy load for the current system. + */ +void dummy_load(int load_ms); -ktimeunit get_thread_time(struct timespec *currTime); #endif \ No newline at end of file diff --git a/src/casestudy_tools/src/primes_workload.cpp b/src/casestudy_tools/src/primes_workload.cpp index 7b8a915..048048f 100755 --- a/src/casestudy_tools/src/primes_workload.cpp +++ b/src/casestudy_tools/src/primes_workload.cpp @@ -1,67 +1,71 @@ -#include "casestudy_tools/primes_workload.hpp" +#include +#include +#include #include -ktimeunit nth_prime_silly(double millis) -{ - // struct tms this_thread_times; - struct timespec currTime; - int sum = 0; - int i; - int j; - ktimeunit const start_cpu_time = get_thread_time(&currTime); - ktimeunit last_iter_time = 0; - ktimeunit last_iter_start_time = start_cpu_time; - for (i = 2; i < 4294967296 - 1; i++) - { - // times(&this_thread_times); - ktimeunit cum_time = get_thread_time(&currTime); - last_iter_time = cum_time - last_iter_start_time; - last_iter_start_time = cum_time; - if ((cum_time - start_cpu_time + last_iter_time*2) > millis) - { - break; - } - for (j = 2; j < i; j++) - { - sum += j; - } - if (cum_time - start_cpu_time > millis) - { - std::cout << "Warning: Time limit exceeded" << std::endl; - } - } - // std::cout<< "took " << (get_thread_time(&currTime) - start_cpu_time) << "ms, allowed " << millis << "ms" << std::endl; - return get_thread_time(&currTime) - start_cpu_time; -} -ktimeunit get_thread_time(struct timespec *currTime) -{ - // clockid_t threadClockId; - // pthread_getcpuclockid(pthread_self(), &threadClockId); - // clock_gettime(threadClockId, currTime); - // return currTime->tv_nsec / 1000000.0 + currTime->tv_sec * 1000.0; - // use the wall clock instead - clock_gettime(CLOCK_MONOTONIC_RAW, currTime); - return currTime->tv_nsec / 1000000.0 + currTime->tv_sec * 1000.0; -} +#include "casestudy_tools/primes_workload.hpp" -#define DUMMY_LOAD_ITER 100000 - -void dummy_load(int load_ms) { - int i, j; - int dummy_load_calib = DummyLoadCalibration::getCalibration(); - // dummy_load_calib is for 100ms - dummy_load_calib = dummy_load_calib * load_ms / 100; - for (j = 0; j < dummy_load_calib; j++) - for (i = 0 ; i < DUMMY_LOAD_ITER; i++) - __asm__ volatile ("nop"); -} void DummyLoadCalibration::setCalibration(int calib) { - DummyLoadCalibration::calibration = calib; + calibration.store(calib, std::memory_order_relaxed); } int DummyLoadCalibration::getCalibration() { - return DummyLoadCalibration::calibration; + return calibration.load(std::memory_order_relaxed); } -int DummyLoadCalibration::calibration = 1; \ No newline at end of file +void calibrate_dummy_load() +{ + int calibration_factor = 1; + for (int iteration = 0; iteration < MAX_ITERATIONS; ++iteration) + { + // Measure the time taken for the dummy load + auto start_time = std::chrono::high_resolution_clock::now(); + dummy_load(TARGET_DURATION_MS); // intended workload + auto end_time = std::chrono::high_resolution_clock::now(); + + // Calculate duration in microseconds + auto duration_us = + std::chrono::duration_cast(end_time - start_time).count(); + + std::cout << "[Calibration] Iteration: " << iteration + << ", Calibration Factor: " << calibration_factor + << ", Measured Duration: " << duration_us << " µs" << std::endl; + + // Check if within acceptable margin + if (std::abs(duration_us - TARGET_DURATION_MS * 1000) < ACCEPTABLE_MARGIN_US) + { + std::cout << "[Calibration] Converged successfully." << std::endl; + break; + } + + // Adjust calibration factor proportionally + calibration_factor = (TARGET_DURATION_MS * 1000 * calibration_factor) / duration_us; + + if (calibration_factor <= 0) + { + calibration_factor = 1; + } + + DummyLoadCalibration::setCalibration(calibration_factor); + } +} + +void dummy_load(int load_ms) +{ + const int calib_factor = DummyLoadCalibration::getCalibration(); + + // calibration factor corresponds to 100ms load, scale it + const int scaled_factor = calib_factor * load_ms / 100; + + volatile int dummy_var = 0; + constexpr int DUMMY_LOAD_ITER = 100'000; + + for (int j = 0; j < scaled_factor; ++j) + { + for (int i = 0; i < DUMMY_LOAD_ITER; ++i) + { + dummy_var += i; // clearer dummy operation (optimizations prevented by volatile) + } + } +}