Initial Commit

.gitignore

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+cmake-build-debug
+.idea
+task1/dataset.dat

CMakeLists.txt

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+cmake_minimum_required(VERSION 3.0)
+project(task1)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_AUTOMOC ON)
+set(CMAKE_AUTORCC ON)
+set(CMAKE_AUTOUIC ON)
+set(CMAKE_INCLUDE_CURRENT_DIR ON)
+
+
+find_package(Qt6 COMPONENTS
+        Core
+        REQUIRED)
+
+find_package(fmt)
+
+
+add_executable(task1_7 task1/main.cpp)
+add_custom_command(OUTPUT dataset.dat
+        DEPENDS task1/dataset.dat
+        COMMAND cmake -E copy_if_different ${CMAKE_SOURCE_DIR}/task1/dataset.dat ${CMAKE_BINARY_DIR}/dataset.dat
+        COMMENT "Copying dataset")
+
+add_custom_target(task1_7_dataset DEPENDS dataset.dat)
+add_dependencies(task1_7 task1_7_dataset)
+
+
+target_link_libraries(task1_7
+        fmt::fmt
+        Qt::Core)

docker/Dockerfile

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+ARG UBUNTU_VERSION="23.04"
+FROM ubuntu:${UBUNTU_VERSION}
+
+# Build environment
+# Profiling and analyzer tools
+RUN --mount=type=bind,target=/var/lib/apt \
+    apt-get -y update \
+    && DEBIAN_FRONTEND=noninteractive TZ=Europe/Berlin apt-get -y install tzdata apt-utils \
+    && apt-get -y upgrade \
+    && apt-get -y install python3 python3-pip lsb-release software-properties-common \
+       rename wget git ninja-build make gdb gdbserver pkg-config \
+       gperf linux-tools-generic linux-tools-common valgrind \
+    && apt-get clean
+
+# CMake
+ENV CMAKE_VERSION="3.28.0-rc2"
+RUN wget -P /opt https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-x86_64.sh \
+    && bash /opt/cmake-${CMAKE_VERSION}-linux-x86_64.sh --skip-license --prefix=/usr \
+    && rm /opt/cmake-${CMAKE_VERSION}-linux-x86_64.sh
+
+# Conan
+ENV CONAN_VERSION="1.61.0"
+ARG UBUNTU_VERSION
+SHELL ["/bin/bash", "-c"]
+RUN env; if [[ ${UBUNTU_VERSION} == "23.04" || ${UBUNTU_VERSION} == "23.10" ]]; then \
+    pip3 install --break-system-packages conan==${CONAN_VERSION}; else \
+    pip3 install conan==${CONAN_VERSION}; fi
+
+ARG GCC_VERSION="13"
+RUN apt-get -y install gcc-${GCC_VERSION} g++-${GCC_VERSION}
+

task1/dataset-gen.py

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+import numpy as np
+import os
+
+randomizer = np.random.default_rng(42)
+randvec = randomizer.integers(low=0, high=pow(2, 32), size=pow(10, 7))
+
+print("Generated random vector with {} entries".format(randvec.size))
+
+with open("dataset.dat", "w") as file:
+    for value in randvec:
+        file.write("{}\n".format(value))
+
+fsize = os.path.getsize("dataset.dat")
+print("File written down with Size {} MB".format(fsize/1000/1000))

task1/main.cpp

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+#include <fmt/format.h>
+#include <QCoreApplication>
+#include <QFile>
+#include <vector>
+#include <printf.h>
+
+/*
+Create a simple sorting application that uses the mergesort algorithm to sort a
+        large collection (e.g., 10^7 ) of 32-bit integers. The input data and output results
+        should be stored in files, and the I/O operations should be considered a
+sequential part of the application. Mergesort is an algorithm that is considered
+        appropriate for parallel execution, although it cannot be equally divided between
+an arbitrary number of processors, as Amdahl’s and Gustafson-Barsis’ laws
+        require.
+Assuming that this equal division is possible, estimate α, i.e., the part of the
+program that can be parallelized, by using a profiler like gprof or valgrind to
+measure the duration of mergesort’s execution relative to the overall execution
+        time. Use this number to estimate the predicted speedup for your program.
+Does α depend on the size of the input? If it does, how should you modify
+your predictions and their graphical illustration?
+*/
+
+int main(int argc, char *argv[]) {
+    QFile file("dataset.dat");
+    if(!file.open(QIODevice::ReadOnly | QIODevice::Text)) {
+        //qDebug() << "Could not open file";
+       return 0;
+    }
+
+    fmt::print("Opened file {} sucessfully!\n", file.fileName().toStdString());
+
+
+    std::vector<int32_t> dataset;
+    int counter = 0;
+
+    while(!file.atEnd()) {
+        dataset.emplace_back(file.readLine().toInt());
+    }
+
+    fmt::print("Read {} values from {}\n", dataset.size(), file.fileName().toStdString());
+
+    return 0;
+}