{"id":108413,"date":"2023-05-08T12:48:42","date_gmt":"2023-05-08T10:48:43","guid":{"rendered":"https:\/\/prohoster.info\/?p=108413"},"modified":"2023-05-08T16:10:08","modified_gmt":"2023-05-08T14:10:08","slug":"sozdatel-llvm-razvivaet-novyj-yazyk-programmirovaniya-mojo","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sl\/blog\/news\/sozdatel-llvm-razvivaet-novyj-yazyk-programmirovaniya-mojo","title":{"rendered":"Kreator LLVM razvija novi programski jezik Mojo","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Chris Lattner, the founder and chief architect of LLVM, as well as the creator of the Swift programming language, and Tim Davis, the former head of AI projects at Google such as TensorFlow and JAX, have introduced a new programming language called Mojo. This language combines ease of use for research and rapid prototyping with the capability to create high-performance end products. The first aspect is achieved through the familiar syntax of Python, while the second is enabled by the ability to compile to machine code, safe memory handling mechanisms, and the use of hardware acceleration for computations. <\/p>\n

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Currently, the language is in an intensive development phase, and only an online interface is available for testing. Standalone builds for running on local systems are promised to be released later, after feedback is received on the performance of the interactive web environment. The source code of the compiler, JIT, and other related project developments is planned to be made available after the internal architecture design is completed (the model for developing a working prototype behind closed doors resembles the early stages of LLVM, Clang, and Swift development). Since Mojo's syntax is based on Python and its type system is close to C\/C++, there are plans to develop tools to simplify the translation of existing projects written in C\/C++ and Python into Mojo, as well as to create hybrid projects that combine code in Python and Mojo. <\/p>\n

Projekt je osmi\u0161ljen za anga\u017eovanje dostupnih hardverskih resursa u heterogenim sistemima za izvr\u0161avanje ra\u010dunarskih operacija. Na primer, za pokretanje aplikacija napisane u jeziku Mojo i paralelno obra\u010dunavanje, mogu se koristiti GPU, specijalizovani akceleratori za ma\u0161insko u\u010denje i vektorske procesorske instrukcije (SIMD). Razlog za razvoj posebnog podskupa programskog jezika Python umesto optimizacije postoje\u0107eg CPython-a je fokus na kompajlaciju, integraciju mogu\u0107nosti sistemskog programiranja i primenu fundamentalno druga\u010dije unutra\u0161nje arhitekture koja omogu\u0107ava izvr\u0161avanje koda na GPU-ima i raznim hardverskim akceleratorima. Pri tome, developeri jezika Mojo nastoje da se, koliko je to mogu\u0107e, pridr\u017eavaju kompatibilnosti sa CPython-om. <\/p>\n

Mojo se mo\u017ee koristiti i u re\u017eimu interpretacije uz JIT, kao i za kompajlaciju u izvr\u0161ne datoteke (AOT, anticipativno). Kompajler uklju\u010duje moderne tehnologije automatske optimizacije, ke\u0161iranja i distribuirane kompajlacije. Izvorni kodovi napisani u jeziku Mojo se transformi\u0161u u niskonivo izme\u0111u-reprezentacije MLIR (Multi-Level Intermediate Representation), koju razvija projekat LLVM i koja pru\u017ea dodatne mogu\u0107nosti za optimizaciju obrade grafova podataka. Kompajler omogu\u0107ava kori\u0161\u0107enje razli\u010ditih backend-a koji podr\u017eavaju MLIR za generisanje ma\u0161inskog koda. <\/p>\n

Kori\u0161\u0107enje dodatnih hardverskih mehanizama za ubrzanje prora\u010duna omogu\u0107ava postizanje performansi koje, u slu\u010daju intenzivnog ra\u010dunanja, prevazilaze aplikacije napisane u C\/C++. Na primer, tokom testiranja aplikacije za generisanje Mandelbrot skupa, aplikacija kompajlirana na jeziku Mojo, kada se izvodi u oblaku AWS (r7iz.metal-16xl), bila je 6 puta br\u017ea od implementacije na C++ (0.03 sekundi naspram 0.20 sekundi), kao i 35.000 puta br\u017ea od aplikacije na Pythone sa standardnim CPython 3.10.9 (0.03 sekundi naspram 1027 sekundi) i 1500 puta br\u017ea u pore\u0111enju sa PYPY (0.03 sekundi naspram 46.1 sekundi). <\/p>\n

\u041f\u0440\u0438 \u043e\u0446\u0435\u043d\u043a\u0435 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0432 \u043e\u0431\u043b\u0430\u0441\u0442\u0438 \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u0437\u0430\u0434\u0430\u0447 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, AI-\u0441\u0442\u0435\u043a Modular Inference Engine, \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0439 \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Mojo, \u043f\u043e \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044e \u0441 \u0440\u0435\u0448\u0435\u043d\u0438\u0435\u043c \u043d\u0430 \u0431\u0430\u0437\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 TensorFlow \u043e\u043a\u0430\u0437\u0430\u043b\u0441\u044f \u043d\u0430 \u0441\u0438\u0441\u0442\u0435\u043c\u0435 \u0441 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u043c Intel \u0432 3 \u0440\u0430\u0437\u0430 \u0431\u044b\u0441\u0442\u0440\u0435\u0435 \u043f\u0440\u0438 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0435 \u044f\u0437\u044b\u043a\u043e\u0432\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438, \u0432 6.4 \u0440\u0430\u0437\u0430 \u0431\u044b\u0441\u0442\u0440\u0435\u0435 \u043f\u0440\u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0438 \u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u0439 \u0438 \u0432 2.1 \u0440\u0430\u0437\u0430 \u0431\u044b\u0441\u0442\u0440\u0435\u0435 \u043f\u0440\u0438 \u0440\u0430\u0431\u043e\u0442\u0435 \u0441 \u043c\u043e\u0434\u0435\u043b\u044f\u043c\u0438 \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0432\u0438\u0437\u0443\u0430\u043b\u044c\u043d\u043e\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438. \u041f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u0432 AMD \u0432\u044b\u0438\u0433\u0440\u044b\u0448 \u043f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 Mojo \u0441\u043e\u0441\u0442\u0430\u0432\u0438\u043b 3.2, 5 \u0438 2.2 \u0440\u0430\u0437\u0430, \u0430 \u043f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u0432 ARM — 5.3, 7.5 \u0438 1.7 \u0440\u0430\u0437, \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0435\u043d\u043d\u043e. \u0420\u0435\u0448\u0435\u043d\u0438\u0435 \u043d\u0430 \u0431\u0430\u0437\u0435 PyTorch \u043e\u0442\u0441\u0442\u0430\u043b\u043e \u043e\u0442 Mojo \u0432 1.4, 1.1 \u0438 1.5 \u0440\u0430\u0437 \u043d\u0430 CPU Intel, \u0432 2.1, 1.2 \u0438 1.5 \u0440\u0430\u0437 \u043d\u0430 CPU AMD \u0438 \u0432 4, 4.3 \u0438 1.3 \u0440\u0430\u0437\u0430 \u043d\u0430 CPU ARM.

\"Kreator<\/center> <\/p>\n

Jezik podpira stati\u010dno tipizacijo in sredstva za varno nizkonivojsko delo z spominom, ki spominja na zmo\u017enosti jezika Rust, kot so sledenje \u017eivljenjski dobi povezav in preverjanje izposoje spremenljivk (borrow checker). Poleg sredstev za varno delo z kazalci so v jeziku na voljo tudi mo\u017enosti za nizkonivojsko delo, na primer, neposreden dostop do pomnilnika v na\u010dinu unsafe z uporabo tipa Pointer, klic posameznih SIMD-navodil ali dostop do strojnih raz\u0161iritev, kot so TensorCores in AMX.

\"Kreator<\/center> <\/p>\n

\u0414\u043b\u044f \u0443\u043f\u0440\u043e\u0449\u0435\u043d\u0438\u044f \u0440\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0438 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0433\u043e Python-\u043a\u043e\u0434\u0430 \u0434\u043b\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0441 \u044f\u0432\u043d\u044b\u043c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435\u043c \u0442\u0438\u043f\u043e\u0432 \u0434\u043b\u044f \u0432\u0441\u0435\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e\u0435 \u043a\u043b\u044e\u0447\u0435\u0432\u043e\u0435 \u0441\u043b\u043e\u0432\u043e «fn» \u0432\u043c\u0435\u0441\u0442\u043e «def». \u0410\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u043e \u0434\u043b\u044f \u043a\u043b\u0430\u0441\u0441\u043e\u0432, \u043f\u0440\u0438 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u0438 \u0441\u0442\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0443\u043f\u0430\u043a\u043e\u0432\u043a\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u043f\u0430\u043c\u044f\u0442\u0438 \u0432\u043e \u0432\u0440\u0435\u043c\u044f \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0446\u0438\u0438 (\u043a\u0430\u043a \u0432 \u0421\u0438) \u0432\u043c\u0435\u0441\u0442\u043e «class» \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0442\u0438\u043f «struct». \u0422\u0430\u043a\u0436\u0435 \u0432\u043e\u0437\u043c\u043e\u0436\u0435\u043d \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u0438\u043c\u043f\u043e\u0440\u0442 \u043c\u043e\u0434\u0443\u043b\u0435\u0439 \u043d\u0430 \u044f\u0437\u044b\u043a\u0430\u0445 C\/C++, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0434\u043b\u044f \u0438\u043c\u043f\u043e\u0440\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 cos \u0438\u0437 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 math \u043c\u043e\u0436\u043d\u043e \u0443\u043a\u0430\u0437\u0430\u0442\u044c «from «math.h» import cos».
\n
Izvor: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041a\u0440\u0438\u0441 \u041b\u0430\u0442\u043d\u0435\u0440 (Chris Lattner), \u043e\u0441\u043d\u043e\u0432\u0430\u0442\u0435\u043b\u044c \u0438 \u0433\u043b\u0430\u0432\u043d\u044b\u0439 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u043e\u0440 LLVM, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0441\u043e\u0437\u0434\u0430\u0442\u0435\u043b\u044c \u044f\u0437\u044b\u043a\u0430 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f Swift, \u0438 \u0422\u0438\u043c \u0414\u044d\u0432\u0438\u0441 (Tim Davis), \u0431\u044b\u0432\u0448\u0438\u0439 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c AI-\u043f\u0440\u043e\u0435\u043a\u0442\u043e\u0432 Google, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a Tensorflow \u0438 JAX, \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0438 \u043d\u043e\u0432\u044b\u0439 \u044f\u0437\u044b\u043a \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f Mojo, \u0441\u043e\u0447\u0435\u0442\u0430\u044e\u0449\u0438\u0439 \u043f\u0440\u043e\u0441\u0442\u043e\u0442\u0443 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u0441\u043a\u0438\u0445 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043e\u043a \u0438 \u0431\u044b\u0441\u0442\u0440\u043e\u0433\u043e \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043f\u0440\u043e\u0442\u043e\u0442\u0438\u043f\u043e\u0432 \u0441 \u043f\u0440\u0438\u0433\u043e\u0434\u043d\u043e\u0441\u0442\u044c\u044e \u0434\u043b\u044f \u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0432\u044b\u0441\u043e\u043a\u043e\u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u043a\u043e\u043d\u0435\u0447\u043d\u044b\u0445 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