▶ ▶ ▶ ▶ ## Tackling Technical Debt: Hello Junior Developers! CppCon 2021 Lightning Talks Tulio Paschoalin Leao – Software Engineering Manager October 28^{th}, 2021 cadence®  Photo by Robert Bye on Unsplash ## Key Concepts Technical Debt Resource Allocation “Free time” Junior Devs Senior Devs Knowledge ![Image](/uploads

# openEuler OS Technical Whitepaper Innovation Projects (June, 2023) Powered by TCPDF (www.tcpdf.org) ## CONTENTS 1 Introduction 001 Development Roadmap 002 2 Technology Ecosystem 003 Innovative pkgship 105 QuickIssue 106 Compatibility and Technical Assessment 107 OSV Technical Assessment 107 openEuler Compatibility List 108 openEuler Technical Assessment 110 Acknowledgment 111 ## 01 Introduction A new openEuler innovative version is released every 6 months to quickly integrate the latest technical achievements of openEuler and other communities. The innovative tech is first verified in the openEuler

PyArmor are written by c in the dynamic library _pytransform. _pytransform protects itself by JIT technical, and the obfuscated scripts is protected by _pytransform. On the other hand, the dynamic library it's not changed. This is called Cross Protection. The dynamic library _pytransform.so uses JIT technical to achieve two tasks: • Keep the des key used to encrypt python scripts from tracing by any c instruction JZ to JNZ, so that _pytrans-form.so can execute even if checking license failed ## How JIT works? First PyArmor defines an instruction set based on GNU lightning. Then write some core functions

7e6351e02e278d623b7b26d/p1_2.jpg) ## The "Restrain" Python JIT thautwarm 目录 CONTENTS >> Preview >> 和其他JIT的比较 >> 实现原理 >> 如何参与开发 ) ## JIT version: 局部函数无开销 ## ●●● from restrain_jit.bejulia.functional import foreach, select, simd_select, J, out xs = np.arange(20000) @jit def all_add2(lst): @select(lst) @select(lst) def ret(elt): return ett + 2 return ret all_add2(xs) ## JIT using SIMD: ☀️ ☀️ ☁️ @jit def all_add2_�(lst, out): @simd_select(lst, out) def ret(elt): return ett + 2 return ret ret

a4/p1_2.jpg) ## 解放Python的 表达力，性能和安全性 Thautwarm 目录 CONTENTS >> 语法和语义扩展 >> JIT >> 静态类型  /home/$USER/.ipython/profile_default/startup/moshmosh_ipy.py ## Just In Time The Restrain Python JIT  为什么编译器从字节码开始着手？ machine)语义的优化问题? Julia后端和Cython后端的差别？ 栈机到基于寄存器(register based)的语义，控制流分析，SSA 和 $ \Phi $ 节点和栈机语义消除。 Cython JIT 基础架构。 ## 为什么编译器从字节码开始着手？ 因为运行时一旦开始你是拿不到源代码的。 如果你拿到了，那么你做了“脏”的操作(inspect库)。 我个人不能接受编译好的程序在运行时还要求源代码存在。

2015Mike BallouNotes from Velocity1Chris KrullTechnical Humility1Jul 2015Jason WohlgemuthAgile software for Agile1Aug 2015Scott GrimesTechnical Debt2Michelle Bauer"Rapid Problem Solving with Post-It Notes for Defense|2| |Jonathan Mowers|The Testing Pyramid with a focus on Unit Testing|2| |Scott Grimes|Technical Debt|2| |Nick Wenner|My Experience in Effective Retrospectives|2| |Sean Lomax|Scrum: The Art of

optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation, implemented using LLVM. It is multi-paradigm, combining features of imperative, functional sqrt 3-element Vector{Float64}: 1.0 2.0 3.0 ### 8.18 Dot Syntax for Vectorizing Functions In technical-computing languages, it is common to have "vectorized" versions of functions, which simply is key to Julia's ability to statically infer and compile code to run fast, without the usual JIT tricks and overhead. Indeed, any new method definition won't be visible to the current runtime

Execution ..... 1141 Parsing ..... 1142 Macro Expansion ..... 1142 Type Inference ..... 1143 JIT Code Generation ..... 1143 System Image ..... 1144 101.6 Calling Conventions ..... 1144 Julia optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation, implemented using LLVM. It is multi-paradigm, combining features of imperative, functional challenges as discussed in performance tips. ### 15.15 Dot Syntax for Vectorizing Functions In technical-computing languages, it is common to have "vectorized" versions of functions, which simply

optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation (and optional ahead-of-time compilation), implemented using LLVM. It is multi-paradigm sqrt 3-element Vector{Float64}: 1.0 2.0 3.0 ### 8.18 Dot Syntax for Vectorizing Functions In technical-computing languages, it is common to have "vectorized" versions of functions, which simply is key to Julia's ability to statically infer and compile code to run fast, without the usual JIT tricks and overhead. Indeed, any new method definition won't be visible to the current runtime