116 lines
4.2 KiB
C++
116 lines
4.2 KiB
C++
#include <fmt/format.h>
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#include <vector>
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#include <fstream>
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#include <string>
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#include <chrono>
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#include <cmath>
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#include <mergesort_mt.h>
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/*
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Create a simple sorting application that uses the mergesort algorithm to sort a
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large collection (e.g., 10^7 ) of 32-bit integers. The input data and output results
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should be stored in files, and the I/O operations should be considered a
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sequential part of the application. Mergesort is an algorithm that is considered
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appropriate for parallel execution, although it cannot be equally divided between
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an arbitrary number of processors, as Amdahl’s and Gustafson-Barsis’ laws
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require.
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Assuming that this equal division is possible, estimate α, i.e., the part of the
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program that can be parallelized, by using a profiler like gprof or valgrind to
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measure the duration of sort’s execution relative to the overall execution
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time. Use this number to estimate the predicted speedup for your program.
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Does α depend on the size of the input? If it does, how should you modify
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your predictions and their graphical illustration?
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*/
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template<typename T>
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auto parse_file(std::ifstream &stream, std::vector<T> &vec) -> void {
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std::string buf;
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T convbuf;
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while (std::getline(stream, buf)) {
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convbuf = static_cast<T>(std::stoul(buf));
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vec.emplace_back(std::move(convbuf));
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}
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}
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auto main(int argc, char *argv[]) -> int {
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try {
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const auto path = "dataset.dat";
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std::ifstream file(path, std::ios_base::in);
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if (!file.is_open()) {
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fmt::print("\nError opening file");
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return -1;
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}
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fmt::print("\nOpened file {} sucessfully", path);
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std::vector<int32_t> dataset;
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parse_file(file, dataset);
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fmt::print("\nRead {} values from {}", dataset.size(), path);
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auto dataset_par = dataset;
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auto dataset_seq = dataset;
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auto t1 = std::chrono::high_resolution_clock::now();
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MergeSorterMT<int32_t> msst([](int32_t a, int32_t b) {
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return (a > b);
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}, 0);
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msst.sort(dataset_seq);
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auto t2 = std::chrono::high_resolution_clock::now();
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auto t_seq = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
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fmt::print("\nSorted {} entries within {} ms in sequential", dataset_seq.size(), t_seq.count());
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const int threads = std::thread::hardware_concurrency();
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const int max_depth = std::sqrt(threads);
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t1 = std::chrono::high_resolution_clock::now();
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MergeSorterMT<int32_t> msmt([](int32_t a, int32_t b) {
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return (a > b);
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}, max_depth);
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msmt.sort(dataset_par);
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t2 = std::chrono::high_resolution_clock::now();
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auto t_par = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
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fmt::print("\nSorted {} entries within {} ms in parallel on a system having {} threads and a recursion depth of {}"
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"\nresulting in a total count of {} threads",
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dataset_seq.size(), t_par.count(), threads, max_depth, std::pow(2, max_depth));
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auto eq = (dataset_seq == dataset_par);
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fmt::print("\nCheck whether sorted arrays are equal: {}", (eq) ? "Equal" : "not equal");
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fmt::print("\n\n------------Summary------------");
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fmt::print("\nt_seq = {: > 5.2f} ms", static_cast<float>(t_seq.count()));
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fmt::print("\nt_par = {: > 5.2f} ms", static_cast<float>(t_par.count()));
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fmt::print("\nspeedup = {: > 5.2f}", (1.0 * t_seq / t_par));
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fmt::print("\nDelta_t = {: > 5.2f} ms", static_cast<float>(t_seq.count() - t_par.count()));
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fmt::print("\n-------------------------------");
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std::ofstream ofile("dataset.out.dat", std::ios_base::out);
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if (!ofile.is_open()) {
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fmt::print("\nError writing to file");
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return -1;
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}
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for (auto &element: dataset_seq) {
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ofile << std::to_string(element) << '\n';
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}
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file.close();
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ofile.flush();
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ofile.close();
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fmt::print("\nWritten to output file");
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return 0;
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} catch (std::exception &e) {
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fmt::print("\nError occured: {}", e.what());
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return -1;
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}
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}
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