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xocc – C compiler based on BSD license

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xocc – C compiler based on BSD license

Hello everybody, I'm glad to announce the major release of xocc C compiler, 1.2.1, based on BSD license. xocc is a C compiler that based on XOC infrastructure, XGEN code generator and XOCFE C frontend. Both xocc, XOC, XGEN, and XOCFE are based on BSD license. XOC provides multi-level IRs, flexibility, and the capability of representing almost all popular languages. There are mainly two level IRs used throughout all phases of the compilation. In addition to basic IR, XOC also provides IR builder, BB manipulating api, Control Flow Graph(CFG), IR lowering, Control Dependent Graph, Dominator Tree, Register SSA, Memory SSA, Agressive Flow Sensitive Alias Analysis, Flow Insensitive Alias Analysis, Agressive Field Sensitive Alias Analysis, Interprocedual Analysis Framework, Inliner, Dead Code Elimination, Copy Propagation, GCSE, GVN, and a lot of sophisticated control flow and peephole optimizations. Moveover, XOC does not use any std library, it provides all basic algorithm library such Vector, List, Map, Dense Bitset, Sparse Bitset, Graph, Mempool, Big-Integer, Linear Algebraic library, Linear Programming Solver, just read Manual.txt of XOC. XGEN provides a retargetable machine code generator. For now, it could generate ARM, x86 code. XGEN provides IR2OR translator, Instruction Scheduling, Register Allocation, Instruction Packager, Assembly. XOCFE is C frontend, it prints AST as output. XOCC is a driver that composes XOCFE, AST2IR, XOC and XGEN into complete C compiler. All features provided by XOCC, XOC, XGEN, XOCFE empower you developing any compiler and analysis tools. Any feedback on the compiler and its components would be much appreciated! To build xocc, see https://github.com/stevenknown/xgen for details. Get them here: https://github.com/stevenknown/xgen https://github.com/stevenknown/xoc https://github.com/stevenknown/xocfe https://github.com/stevenknown/xpoly Contact: steven.known@gmail.com

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50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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