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。

Execution ..... 1278 Parsing ..... 1279 Macro Expansion ..... 1280 Type Inference ..... 1280 JIT Code Generation ..... 1281 System Image ..... 1281 96.6 Calling Conventions ..... 1281 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 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

- 先补了补GPU和Cuda的知识 · 几个可以选择的方案： 1. 买更多更贵的机器——fp16、v100、cpu化 2. 优化算法——知识蒸馏 3. 优化实现——jit/TensorRT ## PyTorch jit ## · 原理介绍 • 转化为graph截图 ![Image](/uploads/documents/7/1/6/5/71656c39f0055537d7f9feafcf0f03f1/p31_2 计时代码只包含GPU时间，排除掉前后处理时间，另包含数据在CPU和GPU之间copy的时间 |bs * seqlen|tensorrt c++|tensorrt py|tensorflow|pytorch|pytorch jit| |---|---|---|---|---|---| |1 * 328|9.9|9.9|17|16.3|14.8| |32 * 328|7.3||11.6|9.9|8.6| ## 异步执行 • CPU与GPU异构，所以可以异步

functions of 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 sure 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 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

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 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 3}((500,500,500)); julia> u = SharedArray{Float64,3}((500,500,500)); Run the functions once to JIT-compile and @time them on the second run: julia> @time advection_serial!(q, u); (irange, jrange

AKA "THE NORM": IT'S LIKE THE APPLICATION (/LIBRARY) IS THE COMPILER... - JUST-IN-TIME (JIT) IS COMPILATION AT THE POINT OF NEED ## WHAT IS JITTING? - INSTEAD OF AHEAD-OF-TIME (AOT), AKA "THE JUST-IN-TIME (JIT) IS COMPILATION AT THE POINT OF NEED - IT'S LIKE THE APPLICATION (/LIBRARY) IS THE COMPILER... ## WHY JIT-慈PILE? ## WHY JIT-慈PILE? • AOT IS NOT POSSIBLE ## WHY JIT-慈PILE? ## • AOT DESIRABLE ## WHY JIT-慈PILE? • AOT IS NOT POSSIBLE • AOT IS NOT DESIRABLE • JIT GIVES PERF BENEFITS ## WHY JIT-慈PILE? • AOT IS NOT POSSIBLE • AOT IS NOT DESIRABLE • JIT GIVES PERF BENEFITS - JIT GIVES PRODUCTIVITY

x86_64. The last number stands for the features: • 0: No anti-debug, JIT, advanced mode features, high speed • 7: Include anti-debug, JIT, advanced mode features, high security. It’s possible to obfuscate --list pyarmor download --list windows pyarmor download --list windows.x86_64 pyarmor download --list JIT pyarmor download --list armv7 After pyarmor is upgraded, however these downloaded dynamic libraries feature has its own bit • 1: Anti-Debug • 2: JIT • 4: ADV, advanced mode • 8: SUPER, super mode For example, windows.x86_64.7 means anti-debug(1), JIT(2) and advanced mode(4) supported, windows.x86_64

[Image](/uploads/documents/d/0/a/d/d0ad97dd1fc1c252d99c48a4d1048dc1/p2_1.jpg) CPPCON Slack channel #SIG JIT I’ll cover 20-some papers in this talk, and have collected them on a GitHub repo. I’ll be going through We’ve also got a CppCon Slack channel: SIG_JIT (Special Interest Group). In a way this talk isn't my usual talk because it's more of a lecture on JiT compilers, where I'll outline the papers d/d0ad97dd1fc1c252d99c48a4d1048dc1/p3_1.jpg) With some artistic liberty, folks usually think of JIT as: The executable code changes after the program is loaded into memory and the linker/loader are

ts/1/0/1/8/10184ce64b2b42c1c174eb48b746ae74/p8_1.jpg) ## JUST-IN-TIME COMPILER BRAND NEW WAY · JIT For PHP7 · Require Gcc 4.8 +(Global Register) · X86-32/64 Supported • Using Dynasm To Emitting Machine Opcode JIT Mcode SHM Execute Liveness Analysis Register Allocation ## JUST-IN-TIME COMPILER CONFIGURATION(TEMPORARY) JIT Strategy Use AVX ### opcache.jit=1 2 0 5 Register alloc JIT Level 0: 0: JIT functions on load 1: JIT functions on execute 2: JIT most frequently called functions on first request 3: JIT functions after N calls or loop iterations 4: JIT functions with @jit annotation