{"id":22668992,"url":"https://github.com/kavindujayarathne/high-performance-computing-scripts","last_synced_at":"2025-03-29T10:44:03.181Z","repository":{"id":250580465,"uuid":"834685318","full_name":"kavindujayarathne/high-performance-computing-scripts","owner":"kavindujayarathne","description":"These scripts showcase the use of multithreading and GPU acceleration for various computational tasks.","archived":false,"fork":false,"pushed_at":"2024-10-27T18:58:24.000Z","size":294,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-04T09:33:19.614Z","etag":null,"topics":["hashing-algorithms","hashing-passwords","high-performance-computing","multithreading","passwordcracking"],"latest_commit_sha":null,"homepage":"https://github.com/kavindujayarathne/high-performance-computing-scripts.git","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kavindujayarathne.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-28T03:37:05.000Z","updated_at":"2024-10-27T18:58:27.000Z","dependencies_parsed_at":null,"dependency_job_id":"f05fefbd-3659-4155-ac57-e4f0cfa366a1","html_url":"https://github.com/kavindujayarathne/high-performance-computing-scripts","commit_stats":null,"previous_names":["kavindujayarathne/high-performance-computing-scripts"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kavindujayarathne%2Fhigh-performance-computing-scripts","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kavindujayarathne%2Fhigh-performance-computing-scripts/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kavindujayarathne%2Fhigh-performance-computing-scripts/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kavindujayarathne%2Fhigh-performance-computing-scripts/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kavindujayarathne","download_url":"https://codeload.github.com/kavindujayarathne/high-performance-computing-scripts/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246174468,"owners_count":20735409,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["hashing-algorithms","hashing-passwords","high-performance-computing","multithreading","passwordcracking"],"created_at":"2024-12-09T15:17:53.540Z","updated_at":"2025-03-29T10:44:03.155Z","avatar_url":"https://github.com/kavindujayarathne.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# High Performance Computing Scripts\n\nThis repository contains a collection of high performance computing scripts I have written in C. These scripts showcase the use of multithreading and GPU acceleration for various computational tasks.\n\n## Scripts Included\n\n\u003cul\u003e\n  \u003cli\u003eMatrix Multiplication with Multithreading\u003c/li\u003e\n  \u003cli\u003ePassword Cracking using Multithreading\u003c/li\u003e\n  \u003cli\u003ePassword Cracking using CUDA\u003c/li\u003e\n  \u003cli\u003eBox Blur using CUDA\u003c/li\u003e\n\u003c/ul\u003e\n\n## Getting Started\n\n### Using Google Colab\n\nYou can run these scripts directly in Google Colab, which provides a convenient environment with necessary libraries pre-installed.\n\nTo run the GPU-related tasks on Google Colab, change the runtime type to use a T4 GPU. For other tasks, the CPU runtime type is sufficient.\n\n### Using Visual Studio Code\n\nTo run these scripts in Visual Studio Code, follow these steps to set up the environment:\n\n\u003cstrong\u003eOpen the Script:\u003c/strong\u003e Open the script in Visual Studio Code.\n\n\u003cstrong\u003eSelect a Kernel:\u003c/strong\u003e A kernel is a computational engine that executes the code contained in the notebook cells. When you open a notebook in VS Code, you need to select a kernel to run the code.\n\n\u003cul\u003e\n  \u003cli\u003eIn the upper right corner of VS Code, click on the option called 'Select Kernel'.\u003c/li\u003e\n\n  \u003cli\u003eIf you haven't already installed the relevant Jupyter extensions, VS Code will suggest you install them first.\u003c/li\u003e\n  \u003cbr\u003e\n\n![demo-img 1](https://raw.githubusercontent.com/kavindujayarathne/high-performance-computing-scripts/main/demo-img1.png)\n\n  \u003cli\u003eOnce the Jupyter extensions are installed, VS Code will usually handle the next steps automatically.\u003c/li\u003e\n\n  \u003cli\u003eIf you have already installed Python, VS Code will automatically set that environment as the kernel.\u003c/li\u003e\n\n  \u003cli\u003eIf it is not selected automatically, the 'Select Kernel' option will still be available. Click on it.\u003c/li\u003e\n\n  \u003cli\u003eYou will see two options: Python Environments and Existing Jupyter Server.\u003c/li\u003e\n  \u003cbr\u003e\n\n![demo-img 2](https://raw.githubusercontent.com/kavindujayarathne/high-performance-computing-scripts/main/demo-img2.png)\n\n  \u003cul\u003e\n    \u003cli\u003eIf you have Python installed on your system, choose Python Environments and select the relevant Python environment.\u003c/li\u003e\n    \u003cli\u003eIf you have a Jupyter server on the cloud or elsewhere, choose Existing Jupyter Server and connect to it.\u003c/li\u003e\n  \u003c/ul\u003e\n\u003c/ul\u003e\n\n\u003cstrong\u003eInstall 'ipykernel':\u003c/strong\u003e\n\n```\npip install ipykernel\n```\n\n\u003cul\u003e\n  \u003cli\u003eEnsure the 'ipykernel' package is installed in your environment to act as a Jupyter kernel, enabling you to run cells within your notebook.\u003c/li\u003e\n\n  \u003cli\u003eIf connecting to an existing Jupyter server, ensure the server has the ipykernel package installed\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cstrong\u003eSet Up the C Environment:\u003c/strong\u003e Install the necessary extensions and C compilers to setup the C Environment, as these scripts are written in C.\n\n\u003cstrong\u003eInstall CUDA Toolkit:\u003c/strong\u003e When using VS Code, to run GPU-accelerated tasks, install the CUDA toolkit on your machine.\n\n\u003cstrong\u003eCompile and Execute in the Terminal:\u003c/strong\u003e\n\nWhen you use VS Code, It't better to use the integrated terminal rather than running them inside a cell or using magic commands (!).\n\n\u003cmark\u003e\u003cstrong\u003eExamples\u003c/strong\u003e\u003c/mark\u003e\n\nYou should navigate to the correct directory where your scripts are located before running the commands.\n\n\u003cstrong\u003eFor C scripts:\u003c/strong\u003e\n\n```\ngcc -pthread -o matrix_multiplication matrix_multiplication.c\n./matrix_multiplication input.txt 6\n```\n\n\u003cstrong\u003eFor CUDA scripts:\u003c/strong\u003e\n\n```\nnvcc -o enc encrypt.cu\n./enc\n```\n\n\u003cbr\u003e\n\n\u003e **Note:**\n\u003e\n\u003e These scripts were developed and tested on Google Colab. If you are running them on VS Code, you should setup the environment accordingly.\n\u003e\n\u003e The scripts utilizing GPU acceleration require a compatible NVIDIA GPU and the CUDA toolkit installed on your machine if you are running GPU-related scripts on VS Code. For Google Colab, GPU support is provided.\n\n## Legal and Ethical Considerations\n\n\u003e **Disclaimer:** These scripts are provided for educational purposes only.\n\n## Licensing\n\nThis repository is licensed under the MIT License. See the [`LICENSE`](./LICENSE) file for more details.\n\nSome scripts include third-party code with its own licenses. Please see the [`THIRD_PARTY_LICENSES.md`](./THIRD_PARTY_LICENSES.md) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkavindujayarathne%2Fhigh-performance-computing-scripts","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkavindujayarathne%2Fhigh-performance-computing-scripts","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkavindujayarathne%2Fhigh-performance-computing-scripts/lists"}