Showing 5 open source projects for "code blocks plugins"

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  • 1
    Code::Blocks

    Code::Blocks

    A free C, C++ and Fortran IDE

    ...Any kind of functionality can be added by installing/coding a plugin. For instance, compiling and debugging functionality is already provided by plugins! We hope you enjoy using Code::Blocks! The Code::Blocks Team
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    Downloads: 56,792 This Week
    Last Update:
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  • 2
    Dragme

    Dragme

    A visual-based IDE for C language uses block drag and drop technology

    ...It is popular among teenagers who want to learn Coding. It is not created only for Students. It can be used by professionals too. It has Playground where Student can learn coding with playing with blocks. It also has Codeground, which is a source code editor, where industry professionals can write and run their own limitless source code. For Windows Only ============== Dragme is only for WINDOWS computer.
    Downloads: 0 This Week
    Last Update:
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  • 3

    HScc2c

    workaround for "Code::Blocks" to mix c and cc sources with precompiled

    workaround for "Code::Blocks" to mix c and cc sources. In CC precompiled Header very faster, but standard MinGW in c not works. Just get a message like: warning: ./wx_pch.h.gch/Debug_HSsysInfo_wx_pch_h_gch: not for GNU C
    Downloads: 0 This Week
    Last Update:
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  • 4
    Intermediate Language Distributed Just-In-Time (ILDJIT) is a unified compilation framework for CIL bytecode. It is a parallel and easily extensible framework thanks to its internal structure composed by plugins.
    Downloads: 0 This Week
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  • 5

    Alchemist plugin

    Alchemist GCC/LLVM plugin for code analysis and tuning

    News: since 2015 we continue all related developments within Collective Knowledge Framework: http://github.com/ctuning/ck/wiki Alchemist plugin is a collection of plugins for GCC/LLVM for external and fine-grain code analysis and tuning. It is intended to to extract program properties for machine learning based optimization (see MILEPOST GCC); optimize programs at fine-grain level (such as unrolling, tiling, prefetching, etc); tune default optimization heuristic; gradually decompose program and detect performance or other anomalies; generate benchmarks particularly useful to train ML-based compilers. ...
    Downloads: 0 This Week
    Last Update:
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