{"id":19129322,"url":"https://github.com/tonitaga/parallel-algorithms-cpp","last_synced_at":"2025-02-22T16:17:15.811Z","repository":{"id":199139588,"uuid":"702215251","full_name":"tonitaga/Parallel-Algorithms-CPP","owner":"tonitaga","description":"Implementation of parallel algorithms and comparison from speed of operation with sequential implementation. 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Clone or download the matrix library repository.\n\n```shell\ngit clone https://github.com/tonitaga/Parallel-Algorithms-CPP.git\n```\n\n2. Ensure you have the g++ compiler installed on your system.\n3. Build all targets\n\n```shell\nmake all\n```\n\nOr build them separately\n\n```shell\nmake ant\n```\n\n```shell\nmake gauss\n```\n\n```shell\nmake winograd\n```\n\n4. Run need target\n\n```shell\nmake run_ant\n```\n\n```shell\nmake run_gauss\n```\n\n```shell\nmake run_winograd\n```\n\n## Ant colony optimization\n\n---\n\n[Ant Colony Optimization article \u003c- click](https://habr.com/ru/companies/timeweb/articles/754462/)\n\nThe Ant Colony Optimization algorithm is a algorithm\ninspired by the foraging behavior of ants.\n\nIt uses a probabilistic approach to find optimal solutions \nby simulating the pheromone trail communication and collective\ndecision-making of ants, effectively exploring and exploiting solution spaces.\n\nIn the parallel version, we employed two approaches within\nthe same algorithm based on the size of the input data:\n1) If the graph size exceeds 50, additional computation threads are utilized.\n2) If the graph size is less than 50, we employ parallel computation using the `\u003cexecution\u003e -\u003e std::execution::par` library.\nIrrespective of the graph size, ants are created in parallel\nin the parallel algorithm to expedite their creation time.\n\n## Gauss elimination\n\n---\n\nThe Gauss Algorithm, also known as the Gauss Method, is an alternative way\nto solve a system of linear algebraic equations.\n\nThe advantage of this method is its simplicity,\nas it only requires a piece of paper and knowledge of addition and\nsubtraction.\n\nTo implement parallel calculations, we have used OpenMP\ndirectives, specifically parallelization of the main for loop using the\n'#pragma omp parallel for' directive.\n\nIt is well-known that parallel computation can be costly,\nso sequential computation will be faster for small data sizes.\nTo observe the performance, create a matrix with sizes starting\nfrom 250.\n\n## Winograd algorithm\n\n---\n\nThe Winograd algorithm is an optimized matrix multiplication \nmethod that was proposed by Winograd\n\nThis algorithm is based on the observation that some calculations\nin a standard matrix multiplication algorithm can be performed\nin advance and their results can be reused to reduce the number of\n\"operations.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftonitaga%2Fparallel-algorithms-cpp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftonitaga%2Fparallel-algorithms-cpp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftonitaga%2Fparallel-algorithms-cpp/lists"}