{"id":415021,"date":"2024-06-30T00:06:24","date_gmt":"2024-06-30T00:06:24","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=415021"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=415021","title":{"rendered":"\u0421\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u0435\u043c Huawei ExaGear \u0441 Apple Rosetta 2 \u0438 \u0440\u0435\u0448\u0435\u043d\u0438\u0435\u043c \u043e\u0442 Microsoft<\/span>"},"content":{"rendered":"
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10 \u043d\u043e\u044f\u0431\u0440\u044f 2020 \u0433\u043e\u0434\u0430 \u043f\u0440\u043e\u0438\u0437\u043e\u0448\u043b\u043e \u0432\u043e \u043c\u043d\u043e\u0433\u043e\u043c \u044d\u043f\u043e\u0445\u0430\u043b\u044c\u043d\u043e\u0435 \u0441\u043e\u0431\u044b\u0442\u0438\u0435 \u0432 \u0438\u043d\u0434\u0443\u0441\u0442\u0440\u0438\u0438 \u043c\u0438\u043a\u0440\u043e\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u0432 — \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Apple \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u043e\u0432\u0430\u043b\u0430 \u043d\u043e\u0432\u044b\u0439 Mac Mini, \u0433\u043b\u0430\u0432\u043d\u043e\u0439 \u0444\u0438\u0448\u043a\u043e\u0439 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u044f\u0432\u043b\u044f\u043b\u0441\u044f \u0447\u0438\u043f \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0439 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 Apple M1. \u0414\u0430\u043d\u043d\u044b\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440, \u0431\u0435\u0437 \u043f\u0440\u0435\u0443\u0432\u0435\u043b\u0438\u0447\u0435\u043d\u0438\u044f, \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0437\u043d\u0430\u043a\u043e\u0432\u044b\u043c \u0434\u043e\u0441\u0442\u0438\u0436\u0435\u043d\u0438\u0435\u043c \u0434\u043b\u044f \u044d\u043a\u043e\u0441\u0438\u0441\u0442\u0435\u043c\u044b ARM — \u043d\u0430\u043a\u043e\u043d\u0435\u0446-\u0442\u043e \u0431\u044b\u043b \u0441\u0434\u0435\u043b\u0430\u043d \u0447\u0438\u043f, \u043e\u0431\u043e\u0448\u0435\u0434\u0448\u0438\u0439 \u043a\u043e\u043d\u043a\u0443\u0440\u0435\u043d\u0442\u043e\u0432 \u0438\u0437 Intel \u0432 \u0442\u043e\u0439 \u043d\u0438\u0448\u0435, \u0433\u0434\u0435 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430 x86 \u0434\u043e\u043c\u0438\u043d\u0438\u0440\u043e\u0432\u0430\u043b\u0430 \u0434\u0435\u0441\u044f\u0442\u0438\u043b\u0435\u0442\u0438\u044f.<\/p>\n

\u041d\u043e \u0434\u043b\u044f \u043d\u0430\u0441 \u0433\u043b\u0430\u0432\u043d\u044b\u043c \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043e\u043c \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043d\u0435 \u0441\u0430\u043c \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440 M1, \u0430 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u044f \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438 Rosetta 2, \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u043d\u0430\u044f \u0441 \u0446\u0435\u043b\u044c\u044e \u0437\u0430\u043f\u0443\u0441\u043a\u0430 legacy x86 \u043a\u043e\u0434\u0430, \u043d\u0435 \u0443\u0441\u043f\u0435\u0432\u0448\u0435\u0433\u043e \u043f\u0435\u0440\u0435\u0435\u0445\u0430\u0442\u044c \u043d\u0430 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0443 ARM.\u00a0 \u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Apple \u0438\u043c\u0435\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u043e\u043f\u044b\u0442 \u0432 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0439 \u043f\u043e \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438 \u0438 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043f\u0440\u0438\u0437\u043d\u0430\u043d\u043d\u044b\u043c \u043b\u0438\u0434\u0435\u0440\u043e\u043c \u0432 \u0434\u0430\u043d\u043d\u043e\u0439 \u043e\u0431\u043b\u0430\u0441\u0442\u0438. \u041f\u0435\u0440\u0432\u0430\u044f \u0432\u0435\u0440\u0441\u0438\u044f \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0433\u043e \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u0430 Rosetta \u043f\u043e\u044f\u0432\u0438\u043b\u0430\u0441\u044c \u0432 2006-\u043e\u043c \u0433\u043e\u0434\u0443, \u043a\u043e\u0433\u0434\u0430 \u043f\u043e\u043c\u043e\u0433\u043b\u0430 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 Apple \u0441\u043e\u0432\u0435\u0440\u0448\u0438\u0442\u044c \u043f\u0435\u0440\u0435\u0445\u043e\u0434 \u0441 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b PowerPC \u043d\u0430 x86. \u0418 \u0445\u043e\u0442\u044f \u0432 \u044d\u0442\u043e\u0442 \u0440\u0430\u0437 \u0433\u043e\u0441\u0442\u0435\u0432\u0430\u044f \u0438 \u0446\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b \u043e\u0442\u043b\u0438\u0447\u0430\u043b\u0438\u0441\u044c \u043e\u0442 \u0442\u0435\u0445, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0431\u044b\u043b\u0438 \u0432 2006-\u043e\u043c, \u043e\u0447\u0435\u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e \u0432\u0435\u0441\u044c \u043e\u043f\u044b\u0442, \u043d\u0430\u043a\u043e\u043f\u043b\u0435\u043d\u043d\u044b\u0439 \u0438\u043d\u0436\u0435\u043d\u0435\u0440\u0430\u043c\u0438 Apple \u0437\u0430 \u044d\u0442\u0438 \u0433\u043e\u0434\u044b, \u0431\u044b\u043b \u0443\u0447\u0442\u0451\u043d \u0438 \u0432\u043b\u043e\u0436\u0435\u043d \u0432 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0443\u044e \u0432\u0435\u0440\u0441\u0438\u044e Rosetta 2. \u0422\u0435\u043c \u0438\u043d\u0442\u0440\u0435\u0441\u043d\u0435\u0435 \u0431\u044b\u043b\u043e \u0441\u0440\u0430\u0432\u043d\u0438\u0442\u044c \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u043e\u0442 Apple \u0441 \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u044b\u043c \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u043c Huawei ExaGear (\u0432\u0435\u0434\u0443\u0449\u0435\u0433\u043e \u0441\u0432\u043e\u044e \u0440\u043e\u0434\u043e\u0441\u043b\u043e\u0432\u043d\u0443\u044e \u043e\u0442 Eltechs ExaGear), \u0440\u0430\u0437\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0435\u043c\u043e\u0433\u043e \u043d\u0430\u0448\u0435\u0439 \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439. \u0417\u0430\u043e\u0434\u043d\u043e, \u043c\u044b \u043e\u0446\u0435\u043d\u0438\u043b\u0438 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438 \u0438\u0437 x86 \u0432 ARM \u043e\u0442 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 Microsoft (\u0432\u0445\u043e\u0434\u044f\u0449\u0435\u0439 \u0432 \u0441\u043e\u0441\u0442\u0430\u0432 MS Windows 10 \u0434\u043b\u044f Arm \u0443\u0441\u0442\u0440\u043e\u0439\u0441\u0442\u0432) \u043d\u0430 \u043b\u044d\u043f\u0442\u043e\u043f\u0435 Huawei MateBook E. \u041d\u0430 \u0442\u0435\u043a\u0443\u0449\u0438\u0439 \u043c\u043e\u043c\u0435\u043d\u0442, \u044d\u0442\u043e \u0432\u0441\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u044b\u0435 \u043d\u0430\u043c \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u043f\u043e \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438 \u0438\u0437 X86 \u0432 ARM, \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b\u0435 \u043d\u0430 \u0448\u0438\u0440\u043e\u043a\u043e\u043c \u0440\u044b\u043d\u043a\u0435.<\/p>\n

\u0422.\u043a. \u0432\u0441\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u0438\u0437\u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e \u0441\u043e\u0437\u0434\u0430\u043d\u044b \u043f\u043e\u0434 \u0440\u0430\u0437\u043d\u044b\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 \u0441\u0438\u0441\u0442\u0435\u043c\u044b (Huawei Exagear \u2013 \u043f\u043e\u0434 Linux, Apple Rosetta 2 — \u043f\u043e\u0434 MacOS, Microsoft binary translator \u2013 \u043f\u043e\u0434 MS Windows), \u0432\u043e\u0437\u043d\u0438\u043a\u043b\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u044c\u00a0 \u043d\u0430\u0439\u0442\u0438 \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u044b\u0439 \u043c\u0435\u0442\u043e\u0434 \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f, \u0442.\u043a. \u043d\u0430\u043f\u0440\u044f\u043c\u0443\u044e \u043f\u0440\u043e\u0432\u0435\u0441\u0442\u0438 \u00a0\u0437\u0430\u043f\u0443\u0441\u043a\u0438 \u0431\u044b\u043b\u043e \u043d\u0435\u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e. \u041c\u044b \u043f\u0440\u0438\u043d\u044f\u043b\u0438 \u0437\u0430 \u043e\u0441\u043d\u043e\u0432\u0443 \u043c\u0435\u0442\u0440\u0438\u043a\u0443 \u00ab\u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0438 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438\u00bb — \u044d\u0442\u043e \u043e\u0442\u043d\u043e\u0448\u0435\u043d\u0438\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044f \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0438\u0441\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0430 \u0432 \u043d\u0430\u0442\u0438\u0432\u043d\u043e\u043c \u0434\u043b\u044f \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b \u0440\u0435\u0436\u0438\u043c\u0435 (ARM \u0432 \u043d\u0430\u0448\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435) \u043a\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0437\u0430\u043f\u0443\u0441\u043a\u0430 \u044d\u0442\u043e\u0433\u043e \u0436\u0435 \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0430 \u0432 x86-\u043a\u043e\u0434\u0430\u0445 \u0437\u0430\u043f\u0443\u0449\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u043e\u0434 \u0434\u0432\u043e\u0438\u0447\u043d\u044b\u043c \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u043e\u043c(\u0438\u043b\u0438 \u0436\u0435 \u043e\u0431\u0440\u0430\u0442\u043d\u0430\u044f \u0432\u0435\u043b\u0438\u0447\u0438\u043d\u0430 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u0438\u0437\u043c\u0435\u0440\u0435\u043d\u0438\u044f \u043f\u043e\u043f\u0443\u0433\u0430\u0435\u0432 \u00ab\u0431\u043e\u043b\u044c\u0448\u0435-\u043b\u0443\u0447\u0448\u0435\u00bb). \u0418\u043d\u044b\u043c\u0438 \u0441\u043b\u043e\u0432\u0430\u043c\u0438, \u043c\u044b \u0441\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u043b\u0438 \u0442\u043e, \u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u043e\u0432 \u043e\u0442 \u043d\u0430\u0442\u0438\u0432\u043d\u043e\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0434\u043e\u0441\u0442\u0438\u0433\u0430\u0435\u0442 \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a \u0432 \u0440\u0435\u0436\u0438\u043c\u0435 \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438. \u0422\u0430\u043a\u043e\u0439 \u0436\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u043e\u0439 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b\u0438\u0441\u044c \u044d\u043a\u0441\u043f\u0435\u0440\u0442\u044b \u0441 \u0441\u0430\u0439\u0442\u0430 Anandtech, \u0432\u044b\u043f\u0443\u0441\u0442\u0438\u0432\u0448\u0438\u0435 \u043e\u0431\u0437\u043e\u0440\u043d\u0443\u044e \u0441\u0442\u0430\u0442\u044c\u044e \u043f\u0440\u043e Apple M1, \u0433\u0434\u0435 \u0442\u0430\u043a\u0436\u0435 \u0431\u044b\u043b\u0438 \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u044b \u0446\u0438\u0444\u0440\u044b \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438<\/a> Rosetta 2.<\/p>\n

\u0412\u0441\u0435 \u0437\u0430\u043c\u0435\u0440\u044b \u043f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u044b \u0432 \u043e\u0434\u043d\u043e\u043f\u043e\u0442\u043e\u0447\u043d\u043e\u043c \u0440\u0435\u0436\u0438\u043c\u0435, \u0442.\u043a. \u0434\u043b\u044f \u043d\u0430\u0441 \u0431\u044b\u043b\u043e \u0432\u0430\u0436\u043d\u043e \u0437\u0430\u043c\u0435\u0440\u0438\u0442\u044c \u0438\u043c\u0435\u043d\u043d\u043e\u00a0 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u043e\u0442\u0442\u0440\u0430\u043d\u0441\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0433\u043e \u043a\u043e\u0434\u0430, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043e\u043d \u0443\u0441\u0442\u0443\u043f\u0430\u0435\u0442 \u043d\u0430\u0442\u0438\u0432\u043d\u043e\u043c\u0443.<\/p>\n

Huawei ExaGear vs Apple Rosetta 2<\/h2>\n

\u0418\u0442\u0430\u043a, \u043d\u0430\u0447\u043d\u0451\u043c \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 Huawei ExaGear \u0441 Apple Rosetta 2 \u043d\u0430 Apple MacBook Pro (M1). \u0422\u0435\u0441\u0442\u044b \u043f\u043e\u0434 Rosetta 2 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u044e\u0442\u0441\u044f \u0432 \u0440\u043e\u0434\u043d\u043e\u0439 MacOS BigSur 11.1, \u0442\u0435\u0441\u0442\u044b \u043f\u043e\u0434 ExaGear\u00a0 — \u0432 \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u043a\u0435 \u0441 Linux kernel 5.4.1.<\/p>\n

Geekbench 5.4.1<\/strong> (\u0447\u0435\u043c \u0432\u044b\u0448\u0435 \u2013 \u0442\u0435\u043c \u043b\u0443\u0447\u0448\u0435):<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Bench name<\/p>\n<\/td>\n

\n

ARM64 MacOS<\/p>\n<\/td>\n

\n

Rosetta 2 (x86)<\/p>\n<\/td>\n

\n

Rosetta 2 efficiency<\/p>\n<\/td>\n

\n

ARM64 Linux\u00a0(VM)<\/p>\n<\/td>\n

\n

Exagear (x86)<\/p>\n<\/td>\n

\n

Exagear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

AES-XTS<\/p>\n<\/td>\n

\n

2769<\/p>\n<\/td>\n

\n

1720<\/p>\n<\/td>\n

\n

62.1%<\/p>\n<\/td>\n

\n

2703<\/p>\n<\/td>\n

\n

1823<\/p>\n<\/td>\n

\n

67.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Text Compression<\/p>\n<\/td>\n

\n

1502<\/p>\n<\/td>\n

\n

1319<\/p>\n<\/td>\n

\n

87.8%<\/p>\n<\/td>\n

\n

1438<\/p>\n<\/td>\n

\n

1349<\/p>\n<\/td>\n

\n

93.8%<\/p>\n<\/td>\n<\/tr>\n

\n

Image Compression<\/p>\n<\/td>\n

\n

1356<\/p>\n<\/td>\n

\n

1056<\/p>\n<\/td>\n

\n

77.9%<\/p>\n<\/td>\n

\n

1335<\/p>\n<\/td>\n

\n

1230<\/p>\n<\/td>\n

\n

92.1%<\/p>\n<\/td>\n<\/tr>\n

\n

Navigation<\/p>\n<\/td>\n

\n

1716<\/p>\n<\/td>\n

\n

1678<\/p>\n<\/td>\n

\n

97.8%<\/p>\n<\/td>\n

\n

1717<\/p>\n<\/td>\n

\n

1605<\/p>\n<\/td>\n

\n

93.5%<\/p>\n<\/td>\n<\/tr>\n

\n

HTML5<\/p>\n<\/td>\n

\n

1642<\/p>\n<\/td>\n

\n

1066<\/p>\n<\/td>\n

\n

64.9%<\/p>\n<\/td>\n

\n

1717<\/p>\n<\/td>\n

\n

1302<\/p>\n<\/td>\n

\n

75.8%<\/p>\n<\/td>\n<\/tr>\n

\n

SQLite<\/p>\n<\/td>\n

\n

1400<\/p>\n<\/td>\n

\n

1000<\/p>\n<\/td>\n

\n

71.4%<\/p>\n<\/td>\n

\n

1328<\/p>\n<\/td>\n

\n

1229<\/p>\n<\/td>\n

\n

92.6%<\/p>\n<\/td>\n<\/tr>\n

\n

PDF Rendering<\/p>\n<\/td>\n

\n

1600<\/p>\n<\/td>\n

\n

1324<\/p>\n<\/td>\n

\n

82.8%<\/p>\n<\/td>\n

\n

1738<\/p>\n<\/td>\n

\n

1464<\/p>\n<\/td>\n

\n

84.2%<\/p>\n<\/td>\n<\/tr>\n

\n

Text Rendering<\/p>\n<\/td>\n

\n

1778<\/p>\n<\/td>\n

\n

1290<\/p>\n<\/td>\n

\n

72.6%<\/p>\n<\/td>\n

\n

1726<\/p>\n<\/td>\n

\n

1480<\/p>\n<\/td>\n

\n

85.8%<\/p>\n<\/td>\n<\/tr>\n

\n

Clang<\/p>\n<\/td>\n

\n

1661<\/p>\n<\/td>\n

\n

1244<\/p>\n<\/td>\n

\n

74.9%<\/p>\n<\/td>\n

\n

1738<\/p>\n<\/td>\n

\n

1335<\/p>\n<\/td>\n

\n

76.8%<\/p>\n<\/td>\n<\/tr>\n

\n

Camera<\/p>\n<\/td>\n

\n

1612<\/p>\n<\/td>\n

\n

1278<\/p>\n<\/td>\n

\n

79.3%<\/p>\n<\/td>\n

\n

1464<\/p>\n<\/td>\n

\n

1148<\/p>\n<\/td>\n

\n

78.4%<\/p>\n<\/td>\n<\/tr>\n

\n

N-Body Physics<\/p>\n<\/td>\n

\n

1789<\/p>\n<\/td>\n

\n

1503<\/p>\n<\/td>\n

\n

84.0%<\/p>\n<\/td>\n

\n

1830<\/p>\n<\/td>\n

\n

1616<\/p>\n<\/td>\n

\n

88.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Rigid Body Physics<\/p>\n<\/td>\n

\n

1772<\/p>\n<\/td>\n

\n

1352<\/p>\n<\/td>\n

\n

76.3%<\/p>\n<\/td>\n

\n

1690<\/p>\n<\/td>\n

\n

1176<\/p>\n<\/td>\n

\n

69.6%<\/p>\n<\/td>\n<\/tr>\n

\n

Gaussian Blur<\/p>\n<\/td>\n

\n

1407<\/p>\n<\/td>\n

\n

1251<\/p>\n<\/td>\n

\n

88.9%<\/p>\n<\/td>\n

\n

1435<\/p>\n<\/td>\n

\n

1297<\/p>\n<\/td>\n

\n

90.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Face Detection<\/p>\n<\/td>\n

\n

2215<\/p>\n<\/td>\n

\n

1500<\/p>\n<\/td>\n

\n

67.7%<\/p>\n<\/td>\n

\n

2216<\/p>\n<\/td>\n

\n

1632<\/p>\n<\/td>\n

\n

73.7%<\/p>\n<\/td>\n<\/tr>\n

\n

Horizon Detection<\/p>\n<\/td>\n

\n

1964<\/p>\n<\/td>\n

\n

1268<\/p>\n<\/td>\n

\n

64.6%<\/p>\n<\/td>\n

\n

1879<\/p>\n<\/td>\n

\n

1386<\/p>\n<\/td>\n

\n

73.8%<\/p>\n<\/td>\n<\/tr>\n

\n

Image Inpainting<\/p>\n<\/td>\n

\n

3214<\/p>\n<\/td>\n

\n

2893<\/p>\n<\/td>\n

\n

90.0%<\/p>\n<\/td>\n

\n

3345<\/p>\n<\/td>\n

\n

2903<\/p>\n<\/td>\n

\n

86.8%<\/p>\n<\/td>\n<\/tr>\n

\n

HDR<\/p>\n<\/td>\n

\n

2486<\/p>\n<\/td>\n

\n

2250<\/p>\n<\/td>\n

\n

90.5%<\/p>\n<\/td>\n

\n

2761<\/p>\n<\/td>\n

\n

2690<\/p>\n<\/td>\n

\n

97.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Ray Tracing<\/p>\n<\/td>\n

\n

2553<\/p>\n<\/td>\n

\n

1970<\/p>\n<\/td>\n

\n

77.2%<\/p>\n<\/td>\n

\n

2055<\/p>\n<\/td>\n

\n

1992<\/p>\n<\/td>\n

\n

96.9%<\/p>\n<\/td>\n<\/tr>\n

\n

Structure from Motion<\/p>\n<\/td>\n

\n

1406<\/p>\n<\/td>\n

\n

1068<\/p>\n<\/td>\n

\n

76.0%<\/p>\n<\/td>\n

\n

1507<\/p>\n<\/td>\n

\n

1150<\/p>\n<\/td>\n

\n

76.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Speech Recognition<\/p>\n<\/td>\n

\n

1601<\/p>\n<\/td>\n

\n

1371<\/p>\n<\/td>\n

\n

85.6%<\/p>\n<\/td>\n

\n

1485<\/p>\n<\/td>\n

\n

1355<\/p>\n<\/td>\n

\n

91.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Machine Learning<\/p>\n<\/td>\n

\n

1243<\/p>\n<\/td>\n

\n

713<\/p>\n<\/td>\n

\n

57.4%<\/p>\n<\/td>\n

\n

1229<\/p>\n<\/td>\n

\n

727<\/p>\n<\/td>\n

\n

59.2%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean<\/strong><\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n

76.9%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

82.4%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

\u041a\u0430\u043a \u0432\u0438\u0434\u043d\u043e, \u0441\u0440\u0435\u0434\u043d\u044f\u044f \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c Huawei ExaGear ( 82.4%<\/strong>) \u043e\u0431\u0433\u043e\u043d\u044f\u0435\u0442 Apple Rosetta 2 (76.9%<\/strong>), \u043f\u0440\u0438\u0447\u0451\u043c \u043f\u0440\u043e\u0438\u0433\u0440\u044b\u0432\u0430\u0435\u0442 ExaGear \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u0430 4-\u0445 \u0442\u0435\u0441\u0442\u0430\u0445 \u0438\u0437 21-\u0433\u043e \u2013 Navigation, Camera, Rigid Body Physics \u0438 Image Inpainting. <\/p>\n

\u0422\u0443\u0442 \u043d\u0430\u0434\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043d\u0435\u0431\u043e\u043b\u044c\u0448\u043e\u0435, \u043d\u043e \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u043e\u0435 \u043e\u0442\u0441\u0442\u0443\u043f\u043b\u0435\u043d\u0438\u0435. \u0414\u0435\u043b\u043e \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e \u043f\u0440\u0438 \u0431\u043e\u043b\u0435\u0435 \u0434\u0435\u0442\u0430\u043b\u044c\u043d\u043e\u043c \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u0438 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0430 M1 \u043c\u043e\u0436\u043d\u043e \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u0442\u044c, \u0447\u0442\u043e \u043d\u0435\u0441\u043c\u043e\u0442\u0440\u044f \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u043e\u043d \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u0432\u044b\u0448\u0435\u043b \u0442\u043e\u043b\u044c\u043a\u043e \u0432 \u043d\u043e\u044f\u0431\u0440\u0435 2020-\u0433\u043e \u0433\u043e\u0434\u0430 (\u0430 \u0437\u043d\u0430\u0447\u0438\u0442 \u0435\u0433\u043e \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u043a\u043e\u043c\u0430\u043d\u0434 \u0431\u044b\u043b\u0430 \u0437\u0430\u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u0430 \u0437\u0430\u0434\u043e\u043b\u0433\u043e \u0434\u043e \u044d\u0442\u043e\u0433\u043e \u043c\u043e\u043c\u0435\u043d\u0442\u0430, \u044f\u0432\u043d\u043e \u043d\u0435 \u043f\u043e\u0437\u0434\u043d\u0435\u0435 2019-\u0433\u043e \u0433\u043e\u0434\u0430), \u043e\u043d \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0432 \u0441\u0435\u0431\u0435 \u043d\u0430\u0431\u043e\u0440 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u0439 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b ArmV8.7, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u0431\u044b\u043b\u0430 \u043f\u0440\u0438\u043d\u044f\u0442\u0430 \u0438 \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d\u0430 \u0442\u043e\u043b\u044c\u043a\u043e \u043e\u0441\u0435\u043d\u044c\u044e 2020-\u0433\u043e \u0433\u043e\u0434\u0430. \u041f\u0440\u0438\u0447\u0451\u043c \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u0430\u044f \u0447\u0430\u0441\u0442\u044c \u044d\u0442\u043e\u0433\u043e \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u044f \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043d\u0438\u0447\u0435\u043c \u0438\u043d\u044b\u043c, \u043a\u0430\u043a \u0430\u043f\u043f\u0430\u0440\u0430\u0442\u043d\u043e\u0439 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438, \u043f\u0440\u0438\u0437\u0432\u0430\u043d\u043d\u043e\u0439 \u0443\u043f\u0440\u043e\u0441\u0442\u0438\u0442\u044c \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u044e \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u0438\u0437 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b x86. \u0422.\u0435. \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Apple \u0440\u0430\u0437\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u043b\u0430 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440 \u0441 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u0435\u043c, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u043d\u0430 \u0442\u043e\u0442 \u043c\u043e\u043c\u0435\u043d\u0442 \u043d\u0435 \u0431\u044b\u043b\u043e \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u043c. \u0411\u043e\u043b\u0435\u0435 \u0442\u043e\u0433\u043e, \u043f\u0440\u0438 \u0432\u043d\u0438\u043c\u0430\u0442\u0435\u043b\u044c\u043d\u043e \u0440\u0435\u0442\u0440\u043e\u0441\u043f\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u043c \u0432\u0437\u0433\u043b\u044f\u0434\u0435 \u043d\u0430 \u0431\u043e\u043b\u0435\u0435 \u0440\u0430\u043d\u043d\u0438\u0435 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u044f \u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e ArmV8.5 \u0438 ArmV8.4 \u0442\u0430\u043a\u0436\u0435 \u0432\u043a\u043b\u044e\u0447\u0430\u043b\u0438 \u0432 \u0441\u0435\u0431\u044f \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0434\u043b\u044f \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0438 \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438. \u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u043e\u0447\u0435\u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Apple \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0434\u0430\u0432\u043d\u043e \u0432 \u043a\u043e\u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0441 Arm Ltd. \u0440\u0430\u0431\u043e\u0442\u0430\u043b\u043e \u043d\u0430\u0434 \u0430\u043f\u043f\u0430\u0440\u0442\u043d\u043e\u0439 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0441\u0432\u043e\u0435\u0433\u043e \u0440\u0435\u0448\u0435\u043d\u0438\u044f. Apple Rosetta 2 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0432\u0441\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0438 \u0438 \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0438\u043c\u0435\u0435\u0442 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u043e\u0435 \u043f\u0440\u0435\u0438\u043c\u0443\u0449\u0435\u0441\u0442\u0432\u043e, \u043f\u043e \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044e \u0441 Huawei Exagear, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0434\u0430\u043d\u043d\u044b\u0435 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u044f \u043d\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442. <\/p>\n

\u0418\u043c\u0435\u043d\u043d\u043e \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0434\u0430\u043d\u043d\u044b\u0435 4 \u0442\u0435\u0441\u0442\u0430 Apple Rosetta 2 \u0438\u0441\u043f\u043e\u043b\u043d\u044f\u0435\u0442 \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u0435\u0435. \u041d\u043e \u0442\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435, \u043d\u0430 \u043e\u0441\u0442\u0430\u043b\u044c\u043d\u044b\u0445 \u0442\u0435\u0441\u0442\u0430\u0445, \u0434\u0430\u0436\u0435 \u0441 \u0443\u0447\u0451\u0442\u043e\u043c \u0438\u043c\u0435\u044e\u0449\u0435\u0433\u043e\u0441\u044f \u0433\u0430\u043d\u0434\u0438\u043a\u0430\u043f\u0430 \u0443 \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u043e\u0442 Apple, Huawei ExaGear \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u0431\u043e\u043b\u0435\u0435 \u0432\u044b\u0441\u043e\u043a\u0438\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b.<\/p>\n

\u0422\u0430\u043a\u0436\u0435 \u0441\u0442\u043e\u0438\u0442 \u043e\u0442\u043c\u0435\u0442\u0438\u0442\u044c, \u0447\u0442\u043e \u0446\u0438\u0444\u0440\u044b \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0432 \u043d\u0430\u0442\u0438\u0432\u043d\u043e\u043c Arm-\u0440\u0435\u0436\u0438\u043c\u0435 \u043d\u0430 MacOS \u0438 Linux \u0432 \u0446\u0435\u043b\u043e\u043c \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0431\u043b\u0438\u0437\u043a\u0438, \u0447\u0442\u043e \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0430\u0435\u0442 \u043e\u0431\u0449\u0443\u044e \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e\u0441\u0442\u044c \u043d\u0430\u0448\u0435\u0433\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u0430 \u043f\u043e \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044e \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0434\u0432\u043e\u0438\u0447\u043d\u044b\u0445 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u043e\u0432.<\/p>\n

\u0414\u0430\u043b\u0435\u0435, \u0441\u0440\u0430\u0432\u043d\u0438\u043c \u0437\u0430\u043f\u0443\u0441\u043a\u0438 \u043d\u0430 SpecCPU2006 \u0438 SpecCPU2017. \u0418\u0437\u043c\u0435\u0440\u044f\u0435\u0442\u0441\u044f \u0432\u0440\u0435\u043c\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043f\u043e\u0434\u0442\u0435\u0441\u0442\u0430 \u0432 \u0441\u0435\u043a\u0443\u043d\u0434\u0430\u0445. \u0418\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u044b \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0438, \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0435 \u043d\u0430 Fortran.<\/p>\n

SpecCPU2006 <\/strong>(\u0441\u0435\u043a\u0443\u043d\u0434\u044b)<\/p>\n

\u041a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440: clang 11.0\u00a0 -O3 \u2013flto<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

INT tests<\/p>\n<\/td>\n

\n

ARM64 MacOS<\/p>\n<\/td>\n

\n

Rosetta 2 (x86)<\/p>\n<\/td>\n

\n

Rosetta 2 efficiency<\/p>\n<\/td>\n

\n

ARM64 Linux (VM)<\/p>\n<\/td>\n

\n

ExaGear (x86)<\/p>\n<\/td>\n

\n

ExaGear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

400.perlbench<\/p>\n<\/td>\n

\n

157<\/p>\n<\/td>\n

\n

218<\/p>\n<\/td>\n

\n

72.0%<\/p>\n<\/td>\n

\n

145<\/p>\n<\/td>\n

\n

166<\/p>\n<\/td>\n

\n

87.3%<\/p>\n<\/td>\n<\/tr>\n

\n

401.bzip2<\/p>\n<\/td>\n

\n

248<\/p>\n<\/td>\n

\n

326<\/p>\n<\/td>\n

\n

76.1%<\/p>\n<\/td>\n

\n

247<\/p>\n<\/td>\n

\n

282<\/p>\n<\/td>\n

\n

87.6%<\/p>\n<\/td>\n<\/tr>\n

\n

429.mcf<\/p>\n<\/td>\n

\n

106<\/p>\n<\/td>\n

\n

118<\/p>\n<\/td>\n

\n

89.8%<\/p>\n<\/td>\n

\n

129<\/p>\n<\/td>\n

\n

123<\/p>\n<\/td>\n

\n

104.9%<\/p>\n<\/td>\n<\/tr>\n

\n

445.gobmk<\/p>\n<\/td>\n

\n

178<\/p>\n<\/td>\n

\n

198<\/p>\n<\/td>\n

\n

89.9%<\/p>\n<\/td>\n

\n

183<\/p>\n<\/td>\n

\n

193<\/p>\n<\/td>\n

\n

94.8%<\/p>\n<\/td>\n<\/tr>\n

\n

456.hmmer<\/p>\n<\/td>\n

\n

159<\/p>\n<\/td>\n

\n

170<\/p>\n<\/td>\n

\n

93.5%<\/p>\n<\/td>\n

\n

164<\/p>\n<\/td>\n

\n

142<\/p>\n<\/td>\n

\n

115.5%<\/p>\n<\/td>\n<\/tr>\n

\n

458.sjeng<\/p>\n<\/td>\n

\n

246<\/p>\n<\/td>\n

\n

300<\/p>\n<\/td>\n

\n

82.0%<\/p>\n<\/td>\n

\n

253<\/p>\n<\/td>\n

\n

281<\/p>\n<\/td>\n

\n

90.0%<\/p>\n<\/td>\n<\/tr>\n

\n

462.libquantum<\/p>\n<\/td>\n

\n

94<\/p>\n<\/td>\n

\n

107<\/p>\n<\/td>\n

\n

87.9%<\/p>\n<\/td>\n

\n

101<\/p>\n<\/td>\n

\n

128<\/p>\n<\/td>\n

\n

78.9%<\/p>\n<\/td>\n<\/tr>\n

\n

464.h264ref<\/p>\n<\/td>\n

\n

203<\/p>\n<\/td>\n

\n

328<\/p>\n<\/td>\n

\n

61.9%<\/p>\n<\/td>\n

\n

201<\/p>\n<\/td>\n

\n

271<\/p>\n<\/td>\n

\n

74.2%<\/p>\n<\/td>\n<\/tr>\n

\n

471.omnetpp<\/p>\n<\/td>\n

\n

146<\/p>\n<\/td>\n

\n

179<\/p>\n<\/td>\n

\n

81.6%<\/p>\n<\/td>\n

\n

173<\/p>\n<\/td>\n

\n

193<\/p>\n<\/td>\n

\n

89.6%<\/p>\n<\/td>\n<\/tr>\n

\n

473.astar<\/p>\n<\/td>\n

\n

184<\/p>\n<\/td>\n

\n

203<\/p>\n<\/td>\n

\n

90.6%<\/p>\n<\/td>\n

\n

196<\/p>\n<\/td>\n

\n

201<\/p>\n<\/td>\n

\n

97.5%<\/p>\n<\/td>\n<\/tr>\n

\n

483.xalancbmk<\/p>\n<\/td>\n

\n

82<\/p>\n<\/td>\n

\n

104<\/p>\n<\/td>\n

\n

78.8%<\/p>\n<\/td>\n

\n

96<\/p>\n<\/td>\n

\n

112<\/p>\n<\/td>\n

\n

85.7%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean Int<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

81.7%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

90.8%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n<\/tr>\n
\n

FP tests<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n<\/tr>\n
\n

433.milc<\/p>\n<\/td>\n

\n

95<\/p>\n<\/td>\n

\n

130<\/p>\n<\/td>\n

\n

73.1%<\/p>\n<\/td>\n

\n

98<\/p>\n<\/td>\n

\n

127<\/p>\n<\/td>\n

\n

77.2%<\/p>\n<\/td>\n<\/tr>\n

\n

444.namd<\/p>\n<\/td>\n

\n

139<\/p>\n<\/td>\n

\n

172<\/p>\n<\/td>\n

\n

80.8%<\/p>\n<\/td>\n

\n

139<\/p>\n<\/td>\n

\n

164<\/p>\n<\/td>\n

\n

84.8%<\/p>\n<\/td>\n<\/tr>\n

\n

447.dealII<\/p>\n<\/td>\n

\n

108<\/p>\n<\/td>\n

\n

117<\/p>\n<\/td>\n

\n

92.3%<\/p>\n<\/td>\n

\n

115<\/p>\n<\/td>\n

\n

151<\/p>\n<\/td>\n

\n

76.2%<\/p>\n<\/td>\n<\/tr>\n

\n

450.soplex<\/p>\n<\/td>\n

\n

91<\/p>\n<\/td>\n

\n

104<\/p>\n<\/td>\n

\n

87.5%<\/p>\n<\/td>\n

\n

95<\/p>\n<\/td>\n

\n

107<\/p>\n<\/td>\n

\n

88.8%<\/p>\n<\/td>\n<\/tr>\n

\n

453.povray<\/p>\n<\/td>\n

\n

60<\/p>\n<\/td>\n

\n

78<\/p>\n<\/td>\n

\n

76.9%<\/p>\n<\/td>\n

\n

52<\/p>\n<\/td>\n

\n

65<\/p>\n<\/td>\n

\n

80.0%<\/p>\n<\/td>\n<\/tr>\n

\n

470.lbm<\/p>\n<\/td>\n

\n

113<\/p>\n<\/td>\n

\n

119<\/p>\n<\/td>\n

\n

95.0%<\/p>\n<\/td>\n

\n

114<\/p>\n<\/td>\n

\n

99<\/p>\n<\/td>\n

\n

115.2%<\/p>\n<\/td>\n<\/tr>\n

\n

482.sphinx3<\/p>\n<\/td>\n

\n

194<\/p>\n<\/td>\n

\n

207<\/p>\n<\/td>\n

\n

93.7%<\/p>\n<\/td>\n

\n

195<\/p>\n<\/td>\n

\n

215<\/p>\n<\/td>\n

\n

90.7%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean FP<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

85.2%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

86.7%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

SpecCPU2017 <\/strong>(\u0441\u0435\u043a\u0443\u043d\u0434\u044b)<\/p>\n

\u041a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440: clang 11.0\u00a0 -O3 \u2013flto<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

INT tests<\/p>\n<\/td>\n

\n

ARM64 MacOS<\/p>\n<\/td>\n

\n

Rosetta 2 (x86)<\/p>\n<\/td>\n

\n

Rosetta 2 efficiency<\/p>\n<\/td>\n

\n

ARM64 Linux(VM)<\/p>\n<\/td>\n

\n

ExaGear (x86)<\/p>\n<\/td>\n

\n

ExaGear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

500.perlbench_r<\/p>\n<\/td>\n

\n

216<\/p>\n<\/td>\n

\n

282<\/p>\n<\/td>\n

\n

76.6%<\/p>\n<\/td>\n

\n

210<\/p>\n<\/td>\n

\n

241<\/p>\n<\/td>\n

\n

87.1%<\/p>\n<\/td>\n<\/tr>\n

\n

502.gcc_r <\/p>\n<\/td>\n

\n

125<\/p>\n<\/td>\n

\n

164<\/p>\n<\/td>\n

\n

76.2%<\/p>\n<\/td>\n

\n

125<\/p>\n<\/td>\n

\n

153<\/p>\n<\/td>\n

\n

81.7%<\/p>\n<\/td>\n<\/tr>\n

\n

505.mcf_r<\/p>\n<\/td>\n

\n

202<\/p>\n<\/td>\n

\n

235<\/p>\n<\/td>\n

\n

86.0%<\/p>\n<\/td>\n

\n

217<\/p>\n<\/td>\n

\n

231<\/p>\n<\/td>\n

\n

93.9%<\/p>\n<\/td>\n<\/tr>\n

\n

520.omnetpp_r<\/p>\n<\/td>\n

\n

277<\/p>\n<\/td>\n

\n

360<\/p>\n<\/td>\n

\n

76.9%<\/p>\n<\/td>\n

\n

295<\/p>\n<\/td>\n

\n

324<\/p>\n<\/td>\n

\n

91.0%<\/p>\n<\/td>\n<\/tr>\n

\n

523.xalancbmk_r<\/p>\n<\/td>\n

\n

166<\/p>\n<\/td>\n

\n

204<\/p>\n<\/td>\n

\n

81.4%<\/p>\n<\/td>\n

\n

189<\/p>\n<\/td>\n

\n

197<\/p>\n<\/td>\n

\n

95.9%<\/p>\n<\/td>\n<\/tr>\n

\n

525.x264_r<\/p>\n<\/td>\n

\n

154<\/p>\n<\/td>\n

\n

176<\/p>\n<\/td>\n

\n

87.5%<\/p>\n<\/td>\n

\n

161<\/p>\n<\/td>\n

\n

189<\/p>\n<\/td>\n

\n

85.2%<\/p>\n<\/td>\n<\/tr>\n

\n

531.deepsjeng_r<\/p>\n<\/td>\n

\n

175<\/p>\n<\/td>\n

\n

221<\/p>\n<\/td>\n

\n

79.2%<\/p>\n<\/td>\n

\n

190<\/p>\n<\/td>\n

\n

197<\/p>\n<\/td>\n

\n

96.4%<\/p>\n<\/td>\n<\/tr>\n

\n

541.leela_r<\/p>\n<\/td>\n

\n

282<\/p>\n<\/td>\n

\n

294<\/p>\n<\/td>\n

\n

95.9%<\/p>\n<\/td>\n

\n

285<\/p>\n<\/td>\n

\n

277<\/p>\n<\/td>\n

\n

102.9%<\/p>\n<\/td>\n<\/tr>\n

\n

557.xz_r<\/p>\n<\/td>\n

\n

275<\/p>\n<\/td>\n

\n

319<\/p>\n<\/td>\n

\n

86.2%<\/p>\n<\/td>\n

\n

307<\/p>\n<\/td>\n

\n

335<\/p>\n<\/td>\n

\n

91.6%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean Int<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

82.7%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

91.6%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n<\/tr>\n

\n

FP tests<\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n<\/tr>\n

\n

508.namd_r<\/p>\n<\/td>\n

\n

111<\/p>\n<\/td>\n

\n

134<\/p>\n<\/td>\n

\n

82.8%<\/p>\n<\/td>\n

\n

111<\/p>\n<\/td>\n

\n

133<\/p>\n<\/td>\n

\n

83.5%<\/p>\n<\/td>\n<\/tr>\n

\n

510.parest_r<\/p>\n<\/td>\n

\n

308<\/p>\n<\/td>\n

\n

331<\/p>\n<\/td>\n

\n

93.1%<\/p>\n<\/td>\n

\n

304<\/p>\n<\/td>\n

\n

336<\/p>\n<\/td>\n

\n

90.5%<\/p>\n<\/td>\n<\/tr>\n

\n

511.povray_r<\/p>\n<\/td>\n

\n

211<\/p>\n<\/td>\n

\n

320<\/p>\n<\/td>\n

\n

65.9%<\/p>\n<\/td>\n

\n

196<\/p>\n<\/td>\n

\n

253<\/p>\n<\/td>\n

\n

77.5%<\/p>\n<\/td>\n<\/tr>\n

\n

519.lbm_r<\/p>\n<\/td>\n

\n

121<\/p>\n<\/td>\n

\n

153<\/p>\n<\/td>\n

\n

79.1%<\/p>\n<\/td>\n

\n

127<\/p>\n<\/td>\n

\n

142<\/p>\n<\/td>\n

\n

89.4%<\/p>\n<\/td>\n<\/tr>\n

\n

526.blender_r<\/p>\n<\/td>\n

\n

149<\/p>\n<\/td>\n

\n

179<\/p>\n<\/td>\n

\n

83.2%<\/p>\n<\/td>\n

\n

163<\/p>\n<\/td>\n

\n

175<\/p>\n<\/td>\n

\n

93.1%<\/p>\n<\/td>\n<\/tr>\n

\n

538.imagick_r<\/p>\n<\/td>\n

\n

210<\/p>\n<\/td>\n

\n

345<\/p>\n<\/td>\n

\n

60.9%<\/p>\n<\/td>\n

\n

227<\/p>\n<\/td>\n

\n

271<\/p>\n<\/td>\n

\n

83.8%<\/p>\n<\/td>\n<\/tr>\n

\n

544.nab_r<\/p>\n<\/td>\n

\n

156<\/p>\n<\/td>\n

\n

186<\/p>\n<\/td>\n

\n

83.9%<\/p>\n<\/td>\n

\n

150<\/p>\n<\/td>\n

\n

174<\/p>\n<\/td>\n

\n

86.2%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean FP<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

77.7%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

86.1%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

\u041a\u0430\u043a \u0432\u0438\u0434\u0438\u043c, \u043d\u0430 SpecCPU2006\/2017 \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u044f \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u0430 \u0442\u0435\u0441\u0442\u0430\u043c \u043d\u0430 Geekbench \u2013 \u0432 \u0441\u0440\u0435\u0434\u043d\u0435\u043c Huawei ExaGear \u043e\u0431\u044b\u0433\u0440\u044b\u0432\u0430\u0435\u0442 Apple Rosetta 2, \u0445\u043e\u0442\u044f \u043d\u0430 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u044b\u0445 \u043f\u043e\u0434\u0442\u0435\u0441\u0442\u0430\u0445 \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u044f \u043e\u0431\u0440\u0430\u0442\u043d\u0430\u044f.<\/p>\n

Huawei ExaGear vs Microsoft binary translator<\/h2>\n

\u0422\u0435\u043f\u0435\u0440\u044c \u0441\u0440\u0430\u0432\u043d\u0438\u043c \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c Huawei ExaGear \u0441 \u0434\u0432\u043e\u0438\u0447\u043d\u044b\u043c \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u043e\u043c \u043e\u0442 Microsoft \u043d\u0430 Huawei MateBook E. <\/p>\n

ExaGear \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u0442\u0441\u044f \u0432 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0438 WSL, \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440 \u043e\u0442 Microsoft \u0432 \u0440\u043e\u0434\u043d\u043e\u0439 MS Windows 10.<\/p>\n

\u0412 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0445 ExaGear \u0432 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0438 WSL \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u043b\u0430\u0441\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0430, \u0447\u0442\u043e WSL \u043d\u0435 \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c\u044b\u0435 \u043d\u0430\u043c\u0438 \u0434\u043b\u044f \u043f\u0440\u043e\u0444\u0438\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0442\u0430\u0439\u043c\u0435\u0440\u044b, \u0438\u0437-\u0437\u0430 \u0447\u0435\u0433\u043e \u043d\u0435 \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442\u0441\u044f \u0440\u0435\u0436\u0438\u043c \u0434\u043e\u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043a\u043e\u0434\u0430 \u0438 ExaGear \u043d\u0435 \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0438\u0440\u0443\u0435\u0442 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u0435 \u0446\u0438\u0444\u0440\u044b \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438. \u041f\u043e\u0442\u0435\u0440\u0438 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u0432 \u0442\u0430\u043a\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u043c\u043e\u0436\u043d\u043e \u043e\u0446\u0435\u043d\u0438\u0442\u044c \u0432 10-20% \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0430. \u0422\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435, \u0441 \u0443\u0447\u0451\u0442\u043e\u043c \u0432\u044b\u0448\u0435\u043f\u0440\u0438\u0432\u0435\u0434\u0451\u043d\u043d\u044b\u0445 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f \u0441 Apple Rosetta 2, \u043e\u0431\u0449\u0430\u044f \u043a\u0430\u0440\u0442\u0438\u043d\u0430 \u0431\u0443\u0434\u0435\u0442 \u044f\u0441\u043d\u0430.<\/p>\n

\u00a0Geekbench 5.4.1<\/strong> (\u0447\u0435\u043c \u0432\u044b\u0448\u0435 \u2013 \u0442\u0435\u043c \u043b\u0443\u0447\u0448\u0435):<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Bench name<\/p>\n<\/td>\n

\n

ARM64 Windows<\/p>\n<\/td>\n

\n

MS BT (x86)<\/p>\n<\/td>\n

\n

MS BT efficiency<\/p>\n<\/td>\n

\n

ARM64 WSL<\/p>\n<\/td>\n

\n

ExaGear (x86)<\/p>\n<\/td>\n

\n

ExaGear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

AES-XTS<\/p>\n<\/td>\n

\n

872<\/p>\n<\/td>\n

\n

437<\/p>\n<\/td>\n

\n

50.1%<\/p>\n<\/td>\n

\n

892<\/p>\n<\/td>\n

\n

437<\/p>\n<\/td>\n

\n

49.0%<\/p>\n<\/td>\n<\/tr>\n

\n

Text Compression<\/p>\n<\/td>\n

\n

514<\/p>\n<\/td>\n

\n

381<\/p>\n<\/td>\n

\n

74.1%<\/p>\n<\/td>\n

\n

518<\/p>\n<\/td>\n

\n

451<\/p>\n<\/td>\n

\n

87.1%<\/p>\n<\/td>\n<\/tr>\n

\n

Image Compression<\/p>\n<\/td>\n

\n

577<\/p>\n<\/td>\n

\n

328<\/p>\n<\/td>\n

\n

56.8%<\/p>\n<\/td>\n

\n

606<\/p>\n<\/td>\n

\n

433<\/p>\n<\/td>\n

\n

71.5%<\/p>\n<\/td>\n<\/tr>\n

\n

Navigation<\/p>\n<\/td>\n

\n

402<\/p>\n<\/td>\n

\n

393<\/p>\n<\/td>\n

\n

97.8%<\/p>\n<\/td>\n

\n

522<\/p>\n<\/td>\n

\n

502<\/p>\n<\/td>\n

\n

96.2%<\/p>\n<\/td>\n<\/tr>\n

\n

HTML5<\/p>\n<\/td>\n

\n

517<\/p>\n<\/td>\n

\n

224<\/p>\n<\/td>\n

\n

43.3%<\/p>\n<\/td>\n

\n

522<\/p>\n<\/td>\n

\n

371<\/p>\n<\/td>\n

\n

71.1%<\/p>\n<\/td>\n<\/tr>\n

\n

SQLite<\/p>\n<\/td>\n

\n

534<\/p>\n<\/td>\n

\n

240<\/p>\n<\/td>\n

\n

44.9%<\/p>\n<\/td>\n

\n

565<\/p>\n<\/td>\n

\n

412<\/p>\n<\/td>\n

\n

72.9%<\/p>\n<\/td>\n<\/tr>\n

\n

PDF Rendering<\/p>\n<\/td>\n

\n

515<\/p>\n<\/td>\n

\n

253<\/p>\n<\/td>\n

\n

49.1%<\/p>\n<\/td>\n

\n

574<\/p>\n<\/td>\n

\n

462<\/p>\n<\/td>\n

\n

80.5%<\/p>\n<\/td>\n<\/tr>\n

\n

Text Rendering<\/p>\n<\/td>\n

\n

530<\/p>\n<\/td>\n

\n

264<\/p>\n<\/td>\n

\n

49.8%<\/p>\n<\/td>\n

\n

544<\/p>\n<\/td>\n

\n

430<\/p>\n<\/td>\n

\n

79.0%<\/p>\n<\/td>\n<\/tr>\n

\n

Clang<\/p>\n<\/td>\n

\n

485<\/p>\n<\/td>\n

\n

187<\/p>\n<\/td>\n

\n

38.6%<\/p>\n<\/td>\n

\n

601<\/p>\n<\/td>\n

\n

405<\/p>\n<\/td>\n

\n

67.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Camera<\/p>\n<\/td>\n

\n

437<\/p>\n<\/td>\n

\n

221<\/p>\n<\/td>\n

\n

50.6%<\/p>\n<\/td>\n

\n

479<\/p>\n<\/td>\n

\n

308<\/p>\n<\/td>\n

\n

64.3%<\/p>\n<\/td>\n<\/tr>\n

\n

N-Body Physics<\/p>\n<\/td>\n

\n

390<\/p>\n<\/td>\n

\n

253<\/p>\n<\/td>\n

\n

64.9%<\/p>\n<\/td>\n

\n

390<\/p>\n<\/td>\n

\n

317<\/p>\n<\/td>\n

\n

81.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Rigid Body Physics<\/p>\n<\/td>\n

\n

634<\/p>\n<\/td>\n

\n

299<\/p>\n<\/td>\n

\n

47.2%<\/p>\n<\/td>\n

\n

717<\/p>\n<\/td>\n

\n

485<\/p>\n<\/td>\n

\n

67.6%<\/p>\n<\/td>\n<\/tr>\n

\n

Gaussian Blur<\/p>\n<\/td>\n

\n

279<\/p>\n<\/td>\n

\n

217<\/p>\n<\/td>\n

\n

77.8%<\/p>\n<\/td>\n

\n

282<\/p>\n<\/td>\n

\n

238<\/p>\n<\/td>\n

\n

84.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Face Detection<\/p>\n<\/td>\n

\n

598<\/p>\n<\/td>\n

\n

301<\/p>\n<\/td>\n

\n

50.3%<\/p>\n<\/td>\n

\n

657<\/p>\n<\/td>\n

\n

387<\/p>\n<\/td>\n

\n

58.9%<\/p>\n<\/td>\n<\/tr>\n

\n

Horizon Detection<\/p>\n<\/td>\n

\n

484<\/p>\n<\/td>\n

\n

235<\/p>\n<\/td>\n

\n

48.6%<\/p>\n<\/td>\n

\n

542<\/p>\n<\/td>\n

\n

331<\/p>\n<\/td>\n

\n

61.1%<\/p>\n<\/td>\n<\/tr>\n

\n

Image Inpainting<\/p>\n<\/td>\n

\n

580<\/p>\n<\/td>\n

\n

419<\/p>\n<\/td>\n

\n

72.2%<\/p>\n<\/td>\n

\n

775<\/p>\n<\/td>\n

\n

504<\/p>\n<\/td>\n

\n

65.0%<\/p>\n<\/td>\n<\/tr>\n

\n

HDR<\/p>\n<\/td>\n

\n

637<\/p>\n<\/td>\n

\n

481<\/p>\n<\/td>\n

\n

75.5%<\/p>\n<\/td>\n

\n

1035<\/p>\n<\/td>\n

\n

843<\/p>\n<\/td>\n

\n

81.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Ray Tracing<\/p>\n<\/td>\n

\n

758<\/p>\n<\/td>\n

\n

296<\/p>\n<\/td>\n

\n

39.1%<\/p>\n<\/td>\n

\n

762<\/p>\n<\/td>\n

\n

513<\/p>\n<\/td>\n

\n

67.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Structure from Motion<\/p>\n<\/td>\n

\n

379<\/p>\n<\/td>\n

\n

203<\/p>\n<\/td>\n

\n

53.6%<\/p>\n<\/td>\n

\n

457<\/p>\n<\/td>\n

\n

289<\/p>\n<\/td>\n

\n

63.2%<\/p>\n<\/td>\n<\/tr>\n

\n

Speech Recognition<\/p>\n<\/td>\n

\n

346<\/p>\n<\/td>\n

\n

283<\/p>\n<\/td>\n

\n

81.8%<\/p>\n<\/td>\n

\n

355<\/p>\n<\/td>\n

\n

312<\/p>\n<\/td>\n

\n

87.9%<\/p>\n<\/td>\n<\/tr>\n

\n

Machine Learning<\/p>\n<\/td>\n

\n

252<\/p>\n<\/td>\n

\n

108<\/p>\n<\/td>\n

\n

42.9%<\/p>\n<\/td>\n

\n

240<\/p>\n<\/td>\n

\n

128<\/p>\n<\/td>\n

\n

53.3%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

55.6%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

70.9%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

SpecCPU2017 <\/strong>(\u0441\u0435\u043a\u0443\u043d\u0434\u044b)<\/p>\n

\u041a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440: clang 11.0\u00a0 -O3 -flto<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

INT tests<\/p>\n<\/td>\n

\n

ARM64 Windows<\/p>\n<\/td>\n

\n

MS BT (x86)<\/p>\n<\/td>\n

\n

MS BT efficiency<\/p>\n<\/td>\n

\n

ARM64 WSL<\/p>\n<\/td>\n

\n

ExaGear (x86)<\/p>\n<\/td>\n

\n

ExaGear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

500.perlbench_r<\/p>\n<\/td>\n

\n

824<\/p>\n<\/td>\n

\n

1526<\/p>\n<\/td>\n

\n

54.0%<\/p>\n<\/td>\n

\n

800<\/p>\n<\/td>\n

\n

1042<\/p>\n<\/td>\n

\n

76.8%<\/p>\n<\/td>\n<\/tr>\n

\n

502.gcc_r<\/p>\n<\/td>\n

\n

716<\/p>\n<\/td>\n

\n

1119<\/p>\n<\/td>\n

\n

64.0%<\/p>\n<\/td>\n

\n

690<\/p>\n<\/td>\n

\n

820<\/p>\n<\/td>\n

\n

84.1%<\/p>\n<\/td>\n<\/tr>\n

\n

505.mcf_r<\/p>\n<\/td>\n

\n

781<\/p>\n<\/td>\n

\n

1069<\/p>\n<\/td>\n

\n

73.1%<\/p>\n<\/td>\n

\n

792<\/p>\n<\/td>\n

\n

992<\/p>\n<\/td>\n

\n

79.8%<\/p>\n<\/td>\n<\/tr>\n

\n

520.omnetpp_r<\/p>\n<\/td>\n

\n

1280<\/p>\n<\/td>\n

\n

2025<\/p>\n<\/td>\n

\n

63.2%<\/p>\n<\/td>\n

\n

1186<\/p>\n<\/td>\n

\n

1414<\/p>\n<\/td>\n

\n

83.9%<\/p>\n<\/td>\n<\/tr>\n

\n

525.x264_r<\/p>\n<\/td>\n

\n

460<\/p>\n<\/td>\n

\n

771<\/p>\n<\/td>\n

\n

59.7%<\/p>\n<\/td>\n

\n

481<\/p>\n<\/td>\n

\n

638<\/p>\n<\/td>\n

\n

75.4%<\/p>\n<\/td>\n<\/tr>\n

\n

531.deepsjeng_r<\/p>\n<\/td>\n

\n

507<\/p>\n<\/td>\n

\n

711<\/p>\n<\/td>\n

\n

71.3%<\/p>\n<\/td>\n

\n

524<\/p>\n<\/td>\n

\n

629<\/p>\n<\/td>\n

\n

83.3%<\/p>\n<\/td>\n<\/tr>\n

\n

541.leela_r<\/p>\n<\/td>\n

\n

564<\/p>\n<\/td>\n

\n

897<\/p>\n<\/td>\n

\n

62.9%<\/p>\n<\/td>\n

\n

547<\/p>\n<\/td>\n

\n

652<\/p>\n<\/td>\n

\n

83.9%<\/p>\n<\/td>\n<\/tr>\n

\n

557.xz_r<\/p>\n<\/td>\n

\n

733<\/p>\n<\/td>\n

\n

938<\/p>\n<\/td>\n

\n

78.1%<\/p>\n<\/td>\n

\n

733<\/p>\n<\/td>\n

\n

827<\/p>\n<\/td>\n

\n

88.6%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean Int<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

65.4%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

81.9%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n<\/tr>\n

\n

FP tests<\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n<\/tr>\n

\n

508.namd_r<\/p>\n<\/td>\n

\n

423<\/p>\n<\/td>\n

\n

698<\/p>\n<\/td>\n

\n

60.6%<\/p>\n<\/td>\n

\n

429<\/p>\n<\/td>\n

\n

619<\/p>\n<\/td>\n

\n

69.3%<\/p>\n<\/td>\n<\/tr>\n

\n

510.parest_r<\/p>\n<\/td>\n

\n

1006<\/p>\n<\/td>\n

\n

1274<\/p>\n<\/td>\n

\n

79.0%<\/p>\n<\/td>\n

\n

970<\/p>\n<\/td>\n

\n

1179<\/p>\n<\/td>\n

\n

82.3%<\/p>\n<\/td>\n<\/tr>\n

\n

511.povray_r<\/p>\n<\/td>\n

\n

808<\/p>\n<\/td>\n

\n

1540<\/p>\n<\/td>\n

\n

52.5%<\/p>\n<\/td>\n

\n

761<\/p>\n<\/td>\n

\n

1027<\/p>\n<\/td>\n

\n

74.1%<\/p>\n<\/td>\n<\/tr>\n

\n

519.lbm_r<\/p>\n<\/td>\n

\n

428<\/p>\n<\/td>\n

\n

767<\/p>\n<\/td>\n

\n

55.8%<\/p>\n<\/td>\n

\n

429<\/p>\n<\/td>\n

\n

549<\/p>\n<\/td>\n

\n

78.1%<\/p>\n<\/td>\n<\/tr>\n

\n

526.blender_r<\/p>\n<\/td>\n

\n

474<\/p>\n<\/td>\n

\n

688<\/p>\n<\/td>\n

\n

68.9%<\/p>\n<\/td>\n

\n

484<\/p>\n<\/td>\n

\n

716<\/p>\n<\/td>\n

\n

67.6%<\/p>\n<\/td>\n<\/tr>\n

\n

538.imagick_r<\/p>\n<\/td>\n

\n

560<\/p>\n<\/td>\n

\n

1075<\/p>\n<\/td>\n

\n

52.1%<\/p>\n<\/td>\n

\n

566<\/p>\n<\/td>\n

\n

847<\/p>\n<\/td>\n

\n

66.8%<\/p>\n<\/td>\n<\/tr>\n

\n

544.nab_r<\/p>\n<\/td>\n

\n

522<\/p>\n<\/td>\n

\n

1050<\/p>\n<\/td>\n

\n

49.7%<\/p>\n<\/td>\n

\n

517<\/p>\n<\/td>\n

\n

617<\/p>\n<\/td>\n

\n

83.8%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean FP<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

59.0%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

74.3%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

\u0421\u0431\u043e\u0440\u043a\u0430 SpecCPU2006 \u043f\u043e\u0434 Windows \u0432\u044b\u0437\u0432\u0430\u043b\u0430 \u043c\u043d\u043e\u0433\u043e\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044b\u0435 \u043e\u0448\u0438\u0431\u043a\u0438 \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0446\u0438\u0438. \u041f\u043e\u044d\u0442\u043e\u043c\u0443 \u0431\u044b\u043b\u043e \u0440\u0435\u0448\u0435\u043d\u043e \u043d\u0435 \u0442\u0440\u0430\u0442\u0438\u0442\u044c \u0432\u0440\u0435\u043c\u044f \u043d\u0430 \u0437\u0430\u043f\u0443\u0441\u043a \u0434\u0430\u043d\u043d\u043e\u0433\u043e \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0430 \u043f\u0440\u0438 \u043d\u0430\u043b\u0438\u0447\u0438\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0434\u043b\u044f SpecCPU2017, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0432 \u0446\u0435\u043b\u043e\u043c \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0438\u0434\u0435\u043d\u0442\u0438\u0447\u043d\u044b SpecCPU2006.<\/p>\n

\u0420\u0435\u0437\u044e\u043c\u0435<\/h3>\n

\u0418\u043d\u0436\u0435\u043d\u0435\u0440\u044b \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 Apple \u0432\u044b\u043f\u0443\u0441\u0442\u0438\u043b\u0438 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0432\u044b\u0434\u0430\u044e\u0449\u0438\u0439\u0441\u044f \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440, \u043d\u043e \u0438 \u0441\u043d\u0430\u0431\u0434\u0438\u043b\u0438 \u0435\u0433\u043e \u043e\u0447\u0435\u043d\u044c \u043f\u0440\u0438\u043b\u0438\u0447\u043d\u044b\u043c \u0434\u0432\u043e\u0438\u0447\u043d\u044b\u043c \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u043e\u043c \u0434\u043b\u044f \u0437\u0430\u043f\u0443\u0441\u043a\u0430 x86-\u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0439, \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0449\u0438\u043c \u0434\u043e\u0441\u0442\u043e\u0439\u043d\u044b\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438. \u00a0\u0422\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435, Huawei ExaGear \u0443\u0432\u0435\u0440\u0435\u043d\u043d\u043e \u043e\u0431\u044b\u0433\u0440\u044b\u0432\u0430\u0435\u0442 Apple Rosetta 2. <\/p>\n

\u0420\u0435\u0448\u0435\u043d\u0438\u0435 \u043e\u0442 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 Microsoft \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u0443\u0441\u0442\u0443\u043f\u0430\u0435\u0442 \u043f\u043e \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0432\u044b\u0448\u0435\u0443\u043a\u0430\u0437\u0430\u043d\u043d\u044b\u043c \u043e\u043f\u043f\u043e\u043d\u0435\u0442\u0430\u043c, \u0447\u0442\u043e \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u043e\u0436\u0438\u0434\u0430\u0435\u043c\u043e, \u0432\u0432\u0438\u0434\u0443 \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u044f \u0442\u0430\u043a\u043e\u0433\u043e \u0431\u043e\u043b\u044c\u0448\u043e\u0433\u043e \u043e\u043f\u044b\u0442\u0430 \u0432 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0435 \u0434\u0432\u043e\u0438\u0447\u043d\u044b\u0445 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u043e\u0432.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

<\/div>\n


\u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 https:\/\/habr.com\/ru\/articles\/577206\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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10 \u043d\u043e\u044f\u0431\u0440\u044f 2020 \u0433\u043e\u0434\u0430 \u043f\u0440\u043e\u0438\u0437\u043e\u0448\u043b\u043e \u0432\u043e \u043c\u043d\u043e\u0433\u043e\u043c \u044d\u043f\u043e\u0445\u0430\u043b\u044c\u043d\u043e\u0435 \u0441\u043e\u0431\u044b\u0442\u0438\u0435 \u0432 \u0438\u043d\u0434\u0443\u0441\u0442\u0440\u0438\u0438 \u043c\u0438\u043a\u0440\u043e\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u0432 — \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Apple \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u043e\u0432\u0430\u043b\u0430 \u043d\u043e\u0432\u044b\u0439 Mac Mini, \u0433\u043b\u0430\u0432\u043d\u043e\u0439 \u0444\u0438\u0448\u043a\u043e\u0439 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u044f\u0432\u043b\u044f\u043b\u0441\u044f \u0447\u0438\u043f \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0439 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 Apple M1. \u0414\u0430\u043d\u043d\u044b\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440, \u0431\u0435\u0437 \u043f\u0440\u0435\u0443\u0432\u0435\u043b\u0438\u0447\u0435\u043d\u0438\u044f, \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0437\u043d\u0430\u043a\u043e\u0432\u044b\u043c \u0434\u043e\u0441\u0442\u0438\u0436\u0435\u043d\u0438\u0435\u043c \u0434\u043b\u044f \u044d\u043a\u043e\u0441\u0438\u0441\u0442\u0435\u043c\u044b ARM — \u043d\u0430\u043a\u043e\u043d\u0435\u0446-\u0442\u043e \u0431\u044b\u043b \u0441\u0434\u0435\u043b\u0430\u043d \u0447\u0438\u043f, \u043e\u0431\u043e\u0448\u0435\u0434\u0448\u0438\u0439 \u043a\u043e\u043d\u043a\u0443\u0440\u0435\u043d\u0442\u043e\u0432 \u0438\u0437 Intel \u0432 \u0442\u043e\u0439 \u043d\u0438\u0448\u0435, \u0433\u0434\u0435 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430 x86 \u0434\u043e\u043c\u0438\u043d\u0438\u0440\u043e\u0432\u0430\u043b\u0430 \u0434\u0435\u0441\u044f\u0442\u0438\u043b\u0435\u0442\u0438\u044f.<\/p>\n

\u041d\u043e \u0434\u043b\u044f \u043d\u0430\u0441 \u0433\u043b\u0430\u0432\u043d\u044b\u043c \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043e\u043c \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043d\u0435 \u0441\u0430\u043c \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440 M1, \u0430 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u044f \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438 Rosetta 2, \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u043d\u0430\u044f \u0441 \u0446\u0435\u043b\u044c\u044e \u0437\u0430\u043f\u0443\u0441\u043a\u0430 legacy x86 \u043a\u043e\u0434\u0430, \u043d\u0435 \u0443\u0441\u043f\u0435\u0432\u0448\u0435\u0433\u043e \u043f\u0435\u0440\u0435\u0435\u0445\u0430\u0442\u044c \u043d\u0430 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0443 ARM.\u00a0 \u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Apple \u0438\u043c\u0435\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u043e\u043f\u044b\u0442 \u0432 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0439 \u043f\u043e \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438 \u0438 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043f\u0440\u0438\u0437\u043d\u0430\u043d\u043d\u044b\u043c \u043b\u0438\u0434\u0435\u0440\u043e\u043c \u0432 \u0434\u0430\u043d\u043d\u043e\u0439 \u043e\u0431\u043b\u0430\u0441\u0442\u0438. \u041f\u0435\u0440\u0432\u0430\u044f \u0432\u0435\u0440\u0441\u0438\u044f \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0433\u043e \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u0430 Rosetta \u043f\u043e\u044f\u0432\u0438\u043b\u0430\u0441\u044c \u0432 2006-\u043e\u043c \u0433\u043e\u0434\u0443, \u043a\u043e\u0433\u0434\u0430 \u043f\u043e\u043c\u043e\u0433\u043b\u0430 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 Apple \u0441\u043e\u0432\u0435\u0440\u0448\u0438\u0442\u044c \u043f\u0435\u0440\u0435\u0445\u043e\u0434 \u0441 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b PowerPC \u043d\u0430 x86. \u0418 \u0445\u043e\u0442\u044f \u0432 \u044d\u0442\u043e\u0442 \u0440\u0430\u0437 \u0433\u043e\u0441\u0442\u0435\u0432\u0430\u044f \u0438 \u0446\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b \u043e\u0442\u043b\u0438\u0447\u0430\u043b\u0438\u0441\u044c \u043e\u0442 \u0442\u0435\u0445, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0431\u044b\u043b\u0438 \u0432 2006-\u043e\u043c, \u043e\u0447\u0435\u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e \u0432\u0435\u0441\u044c \u043e\u043f\u044b\u0442, \u043d\u0430\u043a\u043e\u043f\u043b\u0435\u043d\u043d\u044b\u0439 \u0438\u043d\u0436\u0435\u043d\u0435\u0440\u0430\u043c\u0438 Apple \u0437\u0430 \u044d\u0442\u0438 \u0433\u043e\u0434\u044b, \u0431\u044b\u043b \u0443\u0447\u0442\u0451\u043d \u0438 \u0432\u043b\u043e\u0436\u0435\u043d \u0432 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0443\u044e \u0432\u0435\u0440\u0441\u0438\u044e Rosetta 2. \u0422\u0435\u043c \u0438\u043d\u0442\u0440\u0435\u0441\u043d\u0435\u0435 \u0431\u044b\u043b\u043e \u0441\u0440\u0430\u0432\u043d\u0438\u0442\u044c \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u043e\u0442 Apple \u0441 \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u044b\u043c \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u043c Huawei ExaGear (\u0432\u0435\u0434\u0443\u0449\u0435\u0433\u043e \u0441\u0432\u043e\u044e \u0440\u043e\u0434\u043e\u0441\u043b\u043e\u0432\u043d\u0443\u044e \u043e\u0442 Eltechs ExaGear), \u0440\u0430\u0437\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0435\u043c\u043e\u0433\u043e \u043d\u0430\u0448\u0435\u0439 \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439. \u0417\u0430\u043e\u0434\u043d\u043e, \u043c\u044b \u043e\u0446\u0435\u043d\u0438\u043b\u0438 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438 \u0438\u0437 x86 \u0432 ARM \u043e\u0442 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 Microsoft (\u0432\u0445\u043e\u0434\u044f\u0449\u0435\u0439 \u0432 \u0441\u043e\u0441\u0442\u0430\u0432 MS Windows 10 \u0434\u043b\u044f Arm \u0443\u0441\u0442\u0440\u043e\u0439\u0441\u0442\u0432) \u043d\u0430 \u043b\u044d\u043f\u0442\u043e\u043f\u0435 Huawei MateBook E. \u041d\u0430 \u0442\u0435\u043a\u0443\u0449\u0438\u0439 \u043c\u043e\u043c\u0435\u043d\u0442, \u044d\u0442\u043e \u0432\u0441\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u044b\u0435 \u043d\u0430\u043c \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u043f\u043e \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438 \u0438\u0437 X86 \u0432 ARM, \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b\u0435 \u043d\u0430 \u0448\u0438\u0440\u043e\u043a\u043e\u043c \u0440\u044b\u043d\u043a\u0435.<\/p>\n

\u0422.\u043a. \u0432\u0441\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u0438\u0437\u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e \u0441\u043e\u0437\u0434\u0430\u043d\u044b \u043f\u043e\u0434 \u0440\u0430\u0437\u043d\u044b\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 \u0441\u0438\u0441\u0442\u0435\u043c\u044b (Huawei Exagear \u2013 \u043f\u043e\u0434 Linux, Apple Rosetta 2 — \u043f\u043e\u0434 MacOS, Microsoft binary translator \u2013 \u043f\u043e\u0434 MS Windows), \u0432\u043e\u0437\u043d\u0438\u043a\u043b\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u044c\u00a0 \u043d\u0430\u0439\u0442\u0438 \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u044b\u0439 \u043c\u0435\u0442\u043e\u0434 \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f, \u0442.\u043a. \u043d\u0430\u043f\u0440\u044f\u043c\u0443\u044e \u043f\u0440\u043e\u0432\u0435\u0441\u0442\u0438 \u00a0\u0437\u0430\u043f\u0443\u0441\u043a\u0438 \u0431\u044b\u043b\u043e \u043d\u0435\u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e. \u041c\u044b \u043f\u0440\u0438\u043d\u044f\u043b\u0438 \u0437\u0430 \u043e\u0441\u043d\u043e\u0432\u0443 \u043c\u0435\u0442\u0440\u0438\u043a\u0443 \u00ab\u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0438 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438\u00bb — \u044d\u0442\u043e \u043e\u0442\u043d\u043e\u0448\u0435\u043d\u0438\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044f \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0438\u0441\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0430 \u0432 \u043d\u0430\u0442\u0438\u0432\u043d\u043e\u043c \u0434\u043b\u044f \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b \u0440\u0435\u0436\u0438\u043c\u0435 (ARM \u0432 \u043d\u0430\u0448\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435) \u043a\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0437\u0430\u043f\u0443\u0441\u043a\u0430 \u044d\u0442\u043e\u0433\u043e \u0436\u0435 \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0430 \u0432 x86-\u043a\u043e\u0434\u0430\u0445 \u0437\u0430\u043f\u0443\u0449\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u043e\u0434 \u0434\u0432\u043e\u0438\u0447\u043d\u044b\u043c \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u043e\u043c(\u0438\u043b\u0438 \u0436\u0435 \u043e\u0431\u0440\u0430\u0442\u043d\u0430\u044f \u0432\u0435\u043b\u0438\u0447\u0438\u043d\u0430 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u0438\u0437\u043c\u0435\u0440\u0435\u043d\u0438\u044f \u043f\u043e\u043f\u0443\u0433\u0430\u0435\u0432 \u00ab\u0431\u043e\u043b\u044c\u0448\u0435-\u043b\u0443\u0447\u0448\u0435\u00bb). \u0418\u043d\u044b\u043c\u0438 \u0441\u043b\u043e\u0432\u0430\u043c\u0438, \u043c\u044b \u0441\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u043b\u0438 \u0442\u043e, \u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u043e\u0432 \u043e\u0442 \u043d\u0430\u0442\u0438\u0432\u043d\u043e\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0434\u043e\u0441\u0442\u0438\u0433\u0430\u0435\u0442 \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a \u0432 \u0440\u0435\u0436\u0438\u043c\u0435 \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438. \u0422\u0430\u043a\u043e\u0439 \u0436\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u043e\u0439 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b\u0438\u0441\u044c \u044d\u043a\u0441\u043f\u0435\u0440\u0442\u044b \u0441 \u0441\u0430\u0439\u0442\u0430 Anandtech, \u0432\u044b\u043f\u0443\u0441\u0442\u0438\u0432\u0448\u0438\u0435 \u043e\u0431\u0437\u043e\u0440\u043d\u0443\u044e \u0441\u0442\u0430\u0442\u044c\u044e \u043f\u0440\u043e Apple M1, \u0433\u0434\u0435 \u0442\u0430\u043a\u0436\u0435 \u0431\u044b\u043b\u0438 \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u044b \u0446\u0438\u0444\u0440\u044b \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438<\/a> Rosetta 2.<\/p>\n

\u0412\u0441\u0435 \u0437\u0430\u043c\u0435\u0440\u044b \u043f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u044b \u0432 \u043e\u0434\u043d\u043e\u043f\u043e\u0442\u043e\u0447\u043d\u043e\u043c \u0440\u0435\u0436\u0438\u043c\u0435, \u0442.\u043a. \u0434\u043b\u044f \u043d\u0430\u0441 \u0431\u044b\u043b\u043e \u0432\u0430\u0436\u043d\u043e \u0437\u0430\u043c\u0435\u0440\u0438\u0442\u044c \u0438\u043c\u0435\u043d\u043d\u043e\u00a0 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u043e\u0442\u0442\u0440\u0430\u043d\u0441\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0433\u043e \u043a\u043e\u0434\u0430, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043e\u043d \u0443\u0441\u0442\u0443\u043f\u0430\u0435\u0442 \u043d\u0430\u0442\u0438\u0432\u043d\u043e\u043c\u0443.<\/p>\n

Huawei ExaGear vs Apple Rosetta 2<\/h2>\n

\u0418\u0442\u0430\u043a, \u043d\u0430\u0447\u043d\u0451\u043c \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 Huawei ExaGear \u0441 Apple Rosetta 2 \u043d\u0430 Apple MacBook Pro (M1). \u0422\u0435\u0441\u0442\u044b \u043f\u043e\u0434 Rosetta 2 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u044e\u0442\u0441\u044f \u0432 \u0440\u043e\u0434\u043d\u043e\u0439 MacOS BigSur 11.1, \u0442\u0435\u0441\u0442\u044b \u043f\u043e\u0434 ExaGear\u00a0 — \u0432 \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u043a\u0435 \u0441 Linux kernel 5.4.1.<\/p>\n

Geekbench 5.4.1<\/strong> (\u0447\u0435\u043c \u0432\u044b\u0448\u0435 \u2013 \u0442\u0435\u043c \u043b\u0443\u0447\u0448\u0435):<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Bench name<\/p>\n<\/td>\n

\n

ARM64 MacOS<\/p>\n<\/td>\n

\n

Rosetta 2 (x86)<\/p>\n<\/td>\n

\n

Rosetta 2 efficiency<\/p>\n<\/td>\n

\n

ARM64 Linux\u00a0(VM)<\/p>\n<\/td>\n

\n

Exagear (x86)<\/p>\n<\/td>\n

\n

Exagear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

AES-XTS<\/p>\n<\/td>\n

\n

2769<\/p>\n<\/td>\n

\n

1720<\/p>\n<\/td>\n

\n

62.1%<\/p>\n<\/td>\n

\n

2703<\/p>\n<\/td>\n

\n

1823<\/p>\n<\/td>\n

\n

67.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Text Compression<\/p>\n<\/td>\n

\n

1502<\/p>\n<\/td>\n

\n

1319<\/p>\n<\/td>\n

\n

87.8%<\/p>\n<\/td>\n

\n

1438<\/p>\n<\/td>\n

\n

1349<\/p>\n<\/td>\n

\n

93.8%<\/p>\n<\/td>\n<\/tr>\n

\n

Image Compression<\/p>\n<\/td>\n

\n

1356<\/p>\n<\/td>\n

\n

1056<\/p>\n<\/td>\n

\n

77.9%<\/p>\n<\/td>\n

\n

1335<\/p>\n<\/td>\n

\n

1230<\/p>\n<\/td>\n

\n

92.1%<\/p>\n<\/td>\n<\/tr>\n

\n

Navigation<\/p>\n<\/td>\n

\n

1716<\/p>\n<\/td>\n

\n

1678<\/p>\n<\/td>\n

\n

97.8%<\/p>\n<\/td>\n

\n

1717<\/p>\n<\/td>\n

\n

1605<\/p>\n<\/td>\n

\n

93.5%<\/p>\n<\/td>\n<\/tr>\n

\n

HTML5<\/p>\n<\/td>\n

\n

1642<\/p>\n<\/td>\n

\n

1066<\/p>\n<\/td>\n

\n

64.9%<\/p>\n<\/td>\n

\n

1717<\/p>\n<\/td>\n

\n

1302<\/p>\n<\/td>\n

\n

75.8%<\/p>\n<\/td>\n<\/tr>\n

\n

SQLite<\/p>\n<\/td>\n

\n

1400<\/p>\n<\/td>\n

\n

1000<\/p>\n<\/td>\n

\n

71.4%<\/p>\n<\/td>\n

\n

1328<\/p>\n<\/td>\n

\n

1229<\/p>\n<\/td>\n

\n

92.6%<\/p>\n<\/td>\n<\/tr>\n

\n

PDF Rendering<\/p>\n<\/td>\n

\n

1600<\/p>\n<\/td>\n

\n

1324<\/p>\n<\/td>\n

\n

82.8%<\/p>\n<\/td>\n

\n

1738<\/p>\n<\/td>\n

\n

1464<\/p>\n<\/td>\n

\n

84.2%<\/p>\n<\/td>\n<\/tr>\n

\n

Text Rendering<\/p>\n<\/td>\n

\n

1778<\/p>\n<\/td>\n

\n

1290<\/p>\n<\/td>\n

\n

72.6%<\/p>\n<\/td>\n

\n

1726<\/p>\n<\/td>\n

\n

1480<\/p>\n<\/td>\n

\n

85.8%<\/p>\n<\/td>\n<\/tr>\n

\n

Clang<\/p>\n<\/td>\n

\n

1661<\/p>\n<\/td>\n

\n

1244<\/p>\n<\/td>\n

\n

74.9%<\/p>\n<\/td>\n

\n

1738<\/p>\n<\/td>\n

\n

1335<\/p>\n<\/td>\n

\n

76.8%<\/p>\n<\/td>\n<\/tr>\n

\n

Camera<\/p>\n<\/td>\n

\n

1612<\/p>\n<\/td>\n

\n

1278<\/p>\n<\/td>\n

\n

79.3%<\/p>\n<\/td>\n

\n

1464<\/p>\n<\/td>\n

\n

1148<\/p>\n<\/td>\n

\n

78.4%<\/p>\n<\/td>\n<\/tr>\n

\n

N-Body Physics<\/p>\n<\/td>\n

\n

1789<\/p>\n<\/td>\n

\n

1503<\/p>\n<\/td>\n

\n

84.0%<\/p>\n<\/td>\n

\n

1830<\/p>\n<\/td>\n

\n

1616<\/p>\n<\/td>\n

\n

88.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Rigid Body Physics<\/p>\n<\/td>\n

\n

1772<\/p>\n<\/td>\n

\n

1352<\/p>\n<\/td>\n

\n

76.3%<\/p>\n<\/td>\n

\n

1690<\/p>\n<\/td>\n

\n

1176<\/p>\n<\/td>\n

\n

69.6%<\/p>\n<\/td>\n<\/tr>\n

\n

Gaussian Blur<\/p>\n<\/td>\n

\n

1407<\/p>\n<\/td>\n

\n

1251<\/p>\n<\/td>\n

\n

88.9%<\/p>\n<\/td>\n

\n

1435<\/p>\n<\/td>\n

\n

1297<\/p>\n<\/td>\n

\n

90.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Face Detection<\/p>\n<\/td>\n

\n

2215<\/p>\n<\/td>\n

\n

1500<\/p>\n<\/td>\n

\n

67.7%<\/p>\n<\/td>\n

\n

2216<\/p>\n<\/td>\n

\n

1632<\/p>\n<\/td>\n

\n

73.7%<\/p>\n<\/td>\n<\/tr>\n

\n

Horizon Detection<\/p>\n<\/td>\n

\n

1964<\/p>\n<\/td>\n

\n

1268<\/p>\n<\/td>\n

\n

64.6%<\/p>\n<\/td>\n

\n

1879<\/p>\n<\/td>\n

\n

1386<\/p>\n<\/td>\n

\n

73.8%<\/p>\n<\/td>\n<\/tr>\n

\n

Image Inpainting<\/p>\n<\/td>\n

\n

3214<\/p>\n<\/td>\n

\n

2893<\/p>\n<\/td>\n

\n

90.0%<\/p>\n<\/td>\n

\n

3345<\/p>\n<\/td>\n

\n

2903<\/p>\n<\/td>\n

\n

86.8%<\/p>\n<\/td>\n<\/tr>\n

\n

HDR<\/p>\n<\/td>\n

\n

2486<\/p>\n<\/td>\n

\n

2250<\/p>\n<\/td>\n

\n

90.5%<\/p>\n<\/td>\n

\n

2761<\/p>\n<\/td>\n

\n

2690<\/p>\n<\/td>\n

\n

97.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Ray Tracing<\/p>\n<\/td>\n

\n

2553<\/p>\n<\/td>\n

\n

1970<\/p>\n<\/td>\n

\n

77.2%<\/p>\n<\/td>\n

\n

2055<\/p>\n<\/td>\n

\n

1992<\/p>\n<\/td>\n

\n

96.9%<\/p>\n<\/td>\n<\/tr>\n

\n

Structure from Motion<\/p>\n<\/td>\n

\n

1406<\/p>\n<\/td>\n

\n

1068<\/p>\n<\/td>\n

\n

76.0%<\/p>\n<\/td>\n

\n

1507<\/p>\n<\/td>\n

\n

1150<\/p>\n<\/td>\n

\n

76.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Speech Recognition<\/p>\n<\/td>\n

\n

1601<\/p>\n<\/td>\n

\n

1371<\/p>\n<\/td>\n

\n

85.6%<\/p>\n<\/td>\n

\n

1485<\/p>\n<\/td>\n

\n

1355<\/p>\n<\/td>\n

\n

91.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Machine Learning<\/p>\n<\/td>\n

\n

1243<\/p>\n<\/td>\n

\n

713<\/p>\n<\/td>\n

\n

57.4%<\/p>\n<\/td>\n

\n

1229<\/p>\n<\/td>\n

\n

727<\/p>\n<\/td>\n

\n

59.2%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean<\/strong><\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n

76.9%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

82.4%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

\u041a\u0430\u043a \u0432\u0438\u0434\u043d\u043e, \u0441\u0440\u0435\u0434\u043d\u044f\u044f \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c Huawei ExaGear ( 82.4%<\/strong>) \u043e\u0431\u0433\u043e\u043d\u044f\u0435\u0442 Apple Rosetta 2 (76.9%<\/strong>), \u043f\u0440\u0438\u0447\u0451\u043c \u043f\u0440\u043e\u0438\u0433\u0440\u044b\u0432\u0430\u0435\u0442 ExaGear \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u0430 4-\u0445 \u0442\u0435\u0441\u0442\u0430\u0445 \u0438\u0437 21-\u0433\u043e \u2013 Navigation, Camera, Rigid Body Physics \u0438 Image Inpainting. <\/p>\n

\u0422\u0443\u0442 \u043d\u0430\u0434\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043d\u0435\u0431\u043e\u043b\u044c\u0448\u043e\u0435, \u043d\u043e \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u043e\u0435 \u043e\u0442\u0441\u0442\u0443\u043f\u043b\u0435\u043d\u0438\u0435. \u0414\u0435\u043b\u043e \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e \u043f\u0440\u0438 \u0431\u043e\u043b\u0435\u0435 \u0434\u0435\u0442\u0430\u043b\u044c\u043d\u043e\u043c \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u0438 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0430 M1 \u043c\u043e\u0436\u043d\u043e \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u0442\u044c, \u0447\u0442\u043e \u043d\u0435\u0441\u043c\u043e\u0442\u0440\u044f \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u043e\u043d \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u0432\u044b\u0448\u0435\u043b \u0442\u043e\u043b\u044c\u043a\u043e \u0432 \u043d\u043e\u044f\u0431\u0440\u0435 2020-\u0433\u043e \u0433\u043e\u0434\u0430 (\u0430 \u0437\u043d\u0430\u0447\u0438\u0442 \u0435\u0433\u043e \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u043a\u043e\u043c\u0430\u043d\u0434 \u0431\u044b\u043b\u0430 \u0437\u0430\u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u0430 \u0437\u0430\u0434\u043e\u043b\u0433\u043e \u0434\u043e \u044d\u0442\u043e\u0433\u043e \u043c\u043e\u043c\u0435\u043d\u0442\u0430, \u044f\u0432\u043d\u043e \u043d\u0435 \u043f\u043e\u0437\u0434\u043d\u0435\u0435 2019-\u0433\u043e \u0433\u043e\u0434\u0430), \u043e\u043d \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0432 \u0441\u0435\u0431\u0435 \u043d\u0430\u0431\u043e\u0440 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u0439 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b ArmV8.7, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u0431\u044b\u043b\u0430 \u043f\u0440\u0438\u043d\u044f\u0442\u0430 \u0438 \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d\u0430 \u0442\u043e\u043b\u044c\u043a\u043e \u043e\u0441\u0435\u043d\u044c\u044e 2020-\u0433\u043e \u0433\u043e\u0434\u0430. \u041f\u0440\u0438\u0447\u0451\u043c \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u0430\u044f \u0447\u0430\u0441\u0442\u044c \u044d\u0442\u043e\u0433\u043e \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u044f \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043d\u0438\u0447\u0435\u043c \u0438\u043d\u044b\u043c, \u043a\u0430\u043a \u0430\u043f\u043f\u0430\u0440\u0430\u0442\u043d\u043e\u0439 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438, \u043f\u0440\u0438\u0437\u0432\u0430\u043d\u043d\u043e\u0439 \u0443\u043f\u0440\u043e\u0441\u0442\u0438\u0442\u044c \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u044e \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u0438\u0437 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b x86. \u0422.\u0435. \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Apple \u0440\u0430\u0437\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u043b\u0430 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440 \u0441 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u0435\u043c, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u043d\u0430 \u0442\u043e\u0442 \u043c\u043e\u043c\u0435\u043d\u0442 \u043d\u0435 \u0431\u044b\u043b\u043e \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u043c. \u0411\u043e\u043b\u0435\u0435 \u0442\u043e\u0433\u043e, \u043f\u0440\u0438 \u0432\u043d\u0438\u043c\u0430\u0442\u0435\u043b\u044c\u043d\u043e \u0440\u0435\u0442\u0440\u043e\u0441\u043f\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u043c \u0432\u0437\u0433\u043b\u044f\u0434\u0435 \u043d\u0430 \u0431\u043e\u043b\u0435\u0435 \u0440\u0430\u043d\u043d\u0438\u0435 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u044f \u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e ArmV8.5 \u0438 ArmV8.4 \u0442\u0430\u043a\u0436\u0435 \u0432\u043a\u043b\u044e\u0447\u0430\u043b\u0438 \u0432 \u0441\u0435\u0431\u044f \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0434\u043b\u044f \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0438 \u0434\u0432\u043e\u0438\u0447\u043d\u043e\u0439 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0446\u0438\u0438. \u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u043e\u0447\u0435\u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Apple \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0434\u0430\u0432\u043d\u043e \u0432 \u043a\u043e\u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0441 Arm Ltd. \u0440\u0430\u0431\u043e\u0442\u0430\u043b\u043e \u043d\u0430\u0434 \u0430\u043f\u043f\u0430\u0440\u0442\u043d\u043e\u0439 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0441\u0432\u043e\u0435\u0433\u043e \u0440\u0435\u0448\u0435\u043d\u0438\u044f. Apple Rosetta 2 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0432\u0441\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0438 \u0438 \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0438\u043c\u0435\u0435\u0442 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u043e\u0435 \u043f\u0440\u0435\u0438\u043c\u0443\u0449\u0435\u0441\u0442\u0432\u043e, \u043f\u043e \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044e \u0441 Huawei Exagear, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0434\u0430\u043d\u043d\u044b\u0435 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u044f \u043d\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442. <\/p>\n

\u0418\u043c\u0435\u043d\u043d\u043e \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0434\u0430\u043d\u043d\u044b\u0435 4 \u0442\u0435\u0441\u0442\u0430 Apple Rosetta 2 \u0438\u0441\u043f\u043e\u043b\u043d\u044f\u0435\u0442 \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u0435\u0435. \u041d\u043e \u0442\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435, \u043d\u0430 \u043e\u0441\u0442\u0430\u043b\u044c\u043d\u044b\u0445 \u0442\u0435\u0441\u0442\u0430\u0445, \u0434\u0430\u0436\u0435 \u0441 \u0443\u0447\u0451\u0442\u043e\u043c \u0438\u043c\u0435\u044e\u0449\u0435\u0433\u043e\u0441\u044f \u0433\u0430\u043d\u0434\u0438\u043a\u0430\u043f\u0430 \u0443 \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u043e\u0442 Apple, Huawei ExaGear \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u0431\u043e\u043b\u0435\u0435 \u0432\u044b\u0441\u043e\u043a\u0438\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b.<\/p>\n

\u0422\u0430\u043a\u0436\u0435 \u0441\u0442\u043e\u0438\u0442 \u043e\u0442\u043c\u0435\u0442\u0438\u0442\u044c, \u0447\u0442\u043e \u0446\u0438\u0444\u0440\u044b \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0432 \u043d\u0430\u0442\u0438\u0432\u043d\u043e\u043c Arm-\u0440\u0435\u0436\u0438\u043c\u0435 \u043d\u0430 MacOS \u0438 Linux \u0432 \u0446\u0435\u043b\u043e\u043c \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0431\u043b\u0438\u0437\u043a\u0438, \u0447\u0442\u043e \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0430\u0435\u0442 \u043e\u0431\u0449\u0443\u044e \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e\u0441\u0442\u044c \u043d\u0430\u0448\u0435\u0433\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u0430 \u043f\u043e \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044e \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0434\u0432\u043e\u0438\u0447\u043d\u044b\u0445 \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u043e\u0432.<\/p>\n

\u0414\u0430\u043b\u0435\u0435, \u0441\u0440\u0430\u0432\u043d\u0438\u043c \u0437\u0430\u043f\u0443\u0441\u043a\u0438 \u043d\u0430 SpecCPU2006 \u0438 SpecCPU2017. \u0418\u0437\u043c\u0435\u0440\u044f\u0435\u0442\u0441\u044f \u0432\u0440\u0435\u043c\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043f\u043e\u0434\u0442\u0435\u0441\u0442\u0430 \u0432 \u0441\u0435\u043a\u0443\u043d\u0434\u0430\u0445. \u0418\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u044b \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0438, \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0435 \u043d\u0430 Fortran.<\/p>\n

SpecCPU2006 <\/strong>(\u0441\u0435\u043a\u0443\u043d\u0434\u044b)<\/p>\n

\u041a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440: clang 11.0\u00a0 -O3 \u2013flto<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

INT tests<\/p>\n<\/td>\n

\n

ARM64 MacOS<\/p>\n<\/td>\n

\n

Rosetta 2 (x86)<\/p>\n<\/td>\n

\n

Rosetta 2 efficiency<\/p>\n<\/td>\n

\n

ARM64 Linux (VM)<\/p>\n<\/td>\n

\n

ExaGear (x86)<\/p>\n<\/td>\n

\n

ExaGear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

400.perlbench<\/p>\n<\/td>\n

\n

157<\/p>\n<\/td>\n

\n

218<\/p>\n<\/td>\n

\n

72.0%<\/p>\n<\/td>\n

\n

145<\/p>\n<\/td>\n

\n

166<\/p>\n<\/td>\n

\n

87.3%<\/p>\n<\/td>\n<\/tr>\n

\n

401.bzip2<\/p>\n<\/td>\n

\n

248<\/p>\n<\/td>\n

\n

326<\/p>\n<\/td>\n

\n

76.1%<\/p>\n<\/td>\n

\n

247<\/p>\n<\/td>\n

\n

282<\/p>\n<\/td>\n

\n

87.6%<\/p>\n<\/td>\n<\/tr>\n

\n

429.mcf<\/p>\n<\/td>\n

\n

106<\/p>\n<\/td>\n

\n

118<\/p>\n<\/td>\n

\n

89.8%<\/p>\n<\/td>\n

\n

129<\/p>\n<\/td>\n

\n

123<\/p>\n<\/td>\n

\n

104.9%<\/p>\n<\/td>\n<\/tr>\n

\n

445.gobmk<\/p>\n<\/td>\n

\n

178<\/p>\n<\/td>\n

\n

198<\/p>\n<\/td>\n

\n

89.9%<\/p>\n<\/td>\n

\n

183<\/p>\n<\/td>\n

\n

193<\/p>\n<\/td>\n

\n

94.8%<\/p>\n<\/td>\n<\/tr>\n

\n

456.hmmer<\/p>\n<\/td>\n

\n

159<\/p>\n<\/td>\n

\n

170<\/p>\n<\/td>\n

\n

93.5%<\/p>\n<\/td>\n

\n

164<\/p>\n<\/td>\n

\n

142<\/p>\n<\/td>\n

\n

115.5%<\/p>\n<\/td>\n<\/tr>\n

\n

458.sjeng<\/p>\n<\/td>\n

\n

246<\/p>\n<\/td>\n

\n

300<\/p>\n<\/td>\n

\n

82.0%<\/p>\n<\/td>\n

\n

253<\/p>\n<\/td>\n

\n

281<\/p>\n<\/td>\n

\n

90.0%<\/p>\n<\/td>\n<\/tr>\n

\n

462.libquantum<\/p>\n<\/td>\n

\n

94<\/p>\n<\/td>\n

\n

107<\/p>\n<\/td>\n

\n

87.9%<\/p>\n<\/td>\n

\n

101<\/p>\n<\/td>\n

\n

128<\/p>\n<\/td>\n

\n

78.9%<\/p>\n<\/td>\n<\/tr>\n

\n

464.h264ref<\/p>\n<\/td>\n

\n

203<\/p>\n<\/td>\n

\n

328<\/p>\n<\/td>\n

\n

61.9%<\/p>\n<\/td>\n

\n

201<\/p>\n<\/td>\n

\n

271<\/p>\n<\/td>\n

\n

74.2%<\/p>\n<\/td>\n<\/tr>\n

\n

471.omnetpp<\/p>\n<\/td>\n

\n

146<\/p>\n<\/td>\n

\n

179<\/p>\n<\/td>\n

\n

81.6%<\/p>\n<\/td>\n

\n

173<\/p>\n<\/td>\n

\n

193<\/p>\n<\/td>\n

\n

89.6%<\/p>\n<\/td>\n<\/tr>\n

\n

473.astar<\/p>\n<\/td>\n

\n

184<\/p>\n<\/td>\n

\n

203<\/p>\n<\/td>\n

\n

90.6%<\/p>\n<\/td>\n

\n

196<\/p>\n<\/td>\n

\n

201<\/p>\n<\/td>\n

\n

97.5%<\/p>\n<\/td>\n<\/tr>\n

\n

483.xalancbmk<\/p>\n<\/td>\n

\n

82<\/p>\n<\/td>\n

\n

104<\/p>\n<\/td>\n

\n

78.8%<\/p>\n<\/td>\n

\n

96<\/p>\n<\/td>\n

\n

112<\/p>\n<\/td>\n

\n

85.7%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean Int<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

81.7%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

90.8%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n<\/tr>\n
\n

FP tests<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n<\/tr>\n
\n

433.milc<\/p>\n<\/td>\n

\n

95<\/p>\n<\/td>\n

\n

130<\/p>\n<\/td>\n

\n

73.1%<\/p>\n<\/td>\n

\n

98<\/p>\n<\/td>\n

\n

127<\/p>\n<\/td>\n

\n

77.2%<\/p>\n<\/td>\n<\/tr>\n

\n

444.namd<\/p>\n<\/td>\n

\n

139<\/p>\n<\/td>\n

\n

172<\/p>\n<\/td>\n

\n

80.8%<\/p>\n<\/td>\n

\n

139<\/p>\n<\/td>\n

\n

164<\/p>\n<\/td>\n

\n

84.8%<\/p>\n<\/td>\n<\/tr>\n

\n

447.dealII<\/p>\n<\/td>\n

\n

108<\/p>\n<\/td>\n

\n

117<\/p>\n<\/td>\n

\n

92.3%<\/p>\n<\/td>\n

\n

115<\/p>\n<\/td>\n

\n

151<\/p>\n<\/td>\n

\n

76.2%<\/p>\n<\/td>\n<\/tr>\n

\n

450.soplex<\/p>\n<\/td>\n

\n

91<\/p>\n<\/td>\n

\n

104<\/p>\n<\/td>\n

\n

87.5%<\/p>\n<\/td>\n

\n

95<\/p>\n<\/td>\n

\n

107<\/p>\n<\/td>\n

\n

88.8%<\/p>\n<\/td>\n<\/tr>\n

\n

453.povray<\/p>\n<\/td>\n

\n

60<\/p>\n<\/td>\n

\n

78<\/p>\n<\/td>\n

\n

76.9%<\/p>\n<\/td>\n

\n

52<\/p>\n<\/td>\n

\n

65<\/p>\n<\/td>\n

\n

80.0%<\/p>\n<\/td>\n<\/tr>\n

\n

470.lbm<\/p>\n<\/td>\n

\n

113<\/p>\n<\/td>\n

\n

119<\/p>\n<\/td>\n

\n

95.0%<\/p>\n<\/td>\n

\n

114<\/p>\n<\/td>\n

\n

99<\/p>\n<\/td>\n

\n

115.2%<\/p>\n<\/td>\n<\/tr>\n

\n

482.sphinx3<\/p>\n<\/td>\n

\n

194<\/p>\n<\/td>\n

\n

207<\/p>\n<\/td>\n

\n

93.7%<\/p>\n<\/td>\n

\n

195<\/p>\n<\/td>\n

\n

215<\/p>\n<\/td>\n

\n

90.7%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean FP<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

85.2%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

86.7%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

SpecCPU2017 <\/strong>(\u0441\u0435\u043a\u0443\u043d\u0434\u044b)<\/p>\n

\u041a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440: clang 11.0\u00a0 -O3 \u2013flto<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

INT tests<\/p>\n<\/td>\n

\n

ARM64 MacOS<\/p>\n<\/td>\n

\n

Rosetta 2 (x86)<\/p>\n<\/td>\n

\n

Rosetta 2 efficiency<\/p>\n<\/td>\n

\n

ARM64 Linux(VM)<\/p>\n<\/td>\n

\n

ExaGear (x86)<\/p>\n<\/td>\n

\n

ExaGear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

500.perlbench_r<\/p>\n<\/td>\n

\n

216<\/p>\n<\/td>\n

\n

282<\/p>\n<\/td>\n

\n

76.6%<\/p>\n<\/td>\n

\n

210<\/p>\n<\/td>\n

\n

241<\/p>\n<\/td>\n

\n

87.1%<\/p>\n<\/td>\n<\/tr>\n

\n

502.gcc_r <\/p>\n<\/td>\n

\n

125<\/p>\n<\/td>\n

\n

164<\/p>\n<\/td>\n

\n

76.2%<\/p>\n<\/td>\n

\n

125<\/p>\n<\/td>\n

\n

153<\/p>\n<\/td>\n

\n

81.7%<\/p>\n<\/td>\n<\/tr>\n

\n

505.mcf_r<\/p>\n<\/td>\n

\n

202<\/p>\n<\/td>\n

\n

235<\/p>\n<\/td>\n

\n

86.0%<\/p>\n<\/td>\n

\n

217<\/p>\n<\/td>\n

\n

231<\/p>\n<\/td>\n

\n

93.9%<\/p>\n<\/td>\n<\/tr>\n

\n

520.omnetpp_r<\/p>\n<\/td>\n

\n

277<\/p>\n<\/td>\n

\n

360<\/p>\n<\/td>\n

\n

76.9%<\/p>\n<\/td>\n

\n

295<\/p>\n<\/td>\n

\n

324<\/p>\n<\/td>\n

\n

91.0%<\/p>\n<\/td>\n<\/tr>\n

\n

523.xalancbmk_r<\/p>\n<\/td>\n

\n

166<\/p>\n<\/td>\n

\n

204<\/p>\n<\/td>\n

\n

81.4%<\/p>\n<\/td>\n

\n

189<\/p>\n<\/td>\n

\n

197<\/p>\n<\/td>\n

\n

95.9%<\/p>\n<\/td>\n<\/tr>\n

\n

525.x264_r<\/p>\n<\/td>\n

\n

154<\/p>\n<\/td>\n

\n

176<\/p>\n<\/td>\n

\n

87.5%<\/p>\n<\/td>\n

\n

161<\/p>\n<\/td>\n

\n

189<\/p>\n<\/td>\n

\n

85.2%<\/p>\n<\/td>\n<\/tr>\n

\n

531.deepsjeng_r<\/p>\n<\/td>\n

\n

175<\/p>\n<\/td>\n

\n

221<\/p>\n<\/td>\n

\n

79.2%<\/p>\n<\/td>\n

\n

190<\/p>\n<\/td>\n

\n

197<\/p>\n<\/td>\n

\n

96.4%<\/p>\n<\/td>\n<\/tr>\n

\n

541.leela_r<\/p>\n<\/td>\n

\n

282<\/p>\n<\/td>\n

\n

294<\/p>\n<\/td>\n

\n

95.9%<\/p>\n<\/td>\n

\n

285<\/p>\n<\/td>\n

\n

277<\/p>\n<\/td>\n

\n

102.9%<\/p>\n<\/td>\n<\/tr>\n

\n

557.xz_r<\/p>\n<\/td>\n

\n

275<\/p>\n<\/td>\n

\n

319<\/p>\n<\/td>\n

\n

86.2%<\/p>\n<\/td>\n

\n

307<\/p>\n<\/td>\n

\n

335<\/p>\n<\/td>\n

\n

91.6%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean Int<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

82.7%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

91.6%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n<\/tr>\n

\n

FP tests<\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n<\/tr>\n

\n

508.namd_r<\/p>\n<\/td>\n

\n

111<\/p>\n<\/td>\n

\n

134<\/p>\n<\/td>\n

\n

82.8%<\/p>\n<\/td>\n

\n

111<\/p>\n<\/td>\n

\n

133<\/p>\n<\/td>\n

\n

83.5%<\/p>\n<\/td>\n<\/tr>\n

\n

510.parest_r<\/p>\n<\/td>\n

\n

308<\/p>\n<\/td>\n

\n

331<\/p>\n<\/td>\n

\n

93.1%<\/p>\n<\/td>\n

\n

304<\/p>\n<\/td>\n

\n

336<\/p>\n<\/td>\n

\n

90.5%<\/p>\n<\/td>\n<\/tr>\n

\n

511.povray_r<\/p>\n<\/td>\n

\n

211<\/p>\n<\/td>\n

\n

320<\/p>\n<\/td>\n

\n

65.9%<\/p>\n<\/td>\n

\n

196<\/p>\n<\/td>\n

\n

253<\/p>\n<\/td>\n

\n

77.5%<\/p>\n<\/td>\n<\/tr>\n

\n

519.lbm_r<\/p>\n<\/td>\n

\n

121<\/p>\n<\/td>\n

\n

153<\/p>\n<\/td>\n

\n

79.1%<\/p>\n<\/td>\n

\n

127<\/p>\n<\/td>\n

\n

142<\/p>\n<\/td>\n

\n

89.4%<\/p>\n<\/td>\n<\/tr>\n

\n

526.blender_r<\/p>\n<\/td>\n

\n

149<\/p>\n<\/td>\n

\n

179<\/p>\n<\/td>\n

\n

83.2%<\/p>\n<\/td>\n

\n

163<\/p>\n<\/td>\n

\n

175<\/p>\n<\/td>\n

\n

93.1%<\/p>\n<\/td>\n<\/tr>\n

\n

538.imagick_r<\/p>\n<\/td>\n

\n

210<\/p>\n<\/td>\n

\n

345<\/p>\n<\/td>\n

\n

60.9%<\/p>\n<\/td>\n

\n

227<\/p>\n<\/td>\n

\n

271<\/p>\n<\/td>\n

\n

83.8%<\/p>\n<\/td>\n<\/tr>\n

\n

544.nab_r<\/p>\n<\/td>\n

\n

156<\/p>\n<\/td>\n

\n

186<\/p>\n<\/td>\n

\n

83.9%<\/p>\n<\/td>\n

\n

150<\/p>\n<\/td>\n

\n

174<\/p>\n<\/td>\n

\n

86.2%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean FP<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

77.7%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

86.1%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

\u041a\u0430\u043a \u0432\u0438\u0434\u0438\u043c, \u043d\u0430 SpecCPU2006\/2017 \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u044f \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u0430 \u0442\u0435\u0441\u0442\u0430\u043c \u043d\u0430 Geekbench \u2013 \u0432 \u0441\u0440\u0435\u0434\u043d\u0435\u043c Huawei ExaGear \u043e\u0431\u044b\u0433\u0440\u044b\u0432\u0430\u0435\u0442 Apple Rosetta 2, \u0445\u043e\u0442\u044f \u043d\u0430 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u044b\u0445 \u043f\u043e\u0434\u0442\u0435\u0441\u0442\u0430\u0445 \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u044f \u043e\u0431\u0440\u0430\u0442\u043d\u0430\u044f.<\/p>\n

Huawei ExaGear vs Microsoft binary translator<\/h2>\n

\u0422\u0435\u043f\u0435\u0440\u044c \u0441\u0440\u0430\u0432\u043d\u0438\u043c \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c Huawei ExaGear \u0441 \u0434\u0432\u043e\u0438\u0447\u043d\u044b\u043c \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440\u043e\u043c \u043e\u0442 Microsoft \u043d\u0430 Huawei MateBook E. <\/p>\n

ExaGear \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u0442\u0441\u044f \u0432 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0438 WSL, \u0442\u0440\u0430\u043d\u0441\u043b\u044f\u0442\u043e\u0440 \u043e\u0442 Microsoft \u0432 \u0440\u043e\u0434\u043d\u043e\u0439 MS Windows 10.<\/p>\n

\u0412 \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0445 ExaGear \u0432 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0438 WSL \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u043b\u0430\u0441\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0430, \u0447\u0442\u043e WSL \u043d\u0435 \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c\u044b\u0435 \u043d\u0430\u043c\u0438 \u0434\u043b\u044f \u043f\u0440\u043e\u0444\u0438\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0442\u0430\u0439\u043c\u0435\u0440\u044b, \u0438\u0437-\u0437\u0430 \u0447\u0435\u0433\u043e \u043d\u0435 \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442\u0441\u044f \u0440\u0435\u0436\u0438\u043c \u0434\u043e\u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043a\u043e\u0434\u0430 \u0438 ExaGear \u043d\u0435 \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0438\u0440\u0443\u0435\u0442 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u0435 \u0446\u0438\u0444\u0440\u044b \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438. \u041f\u043e\u0442\u0435\u0440\u0438 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u0432 \u0442\u0430\u043a\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u043c\u043e\u0436\u043d\u043e \u043e\u0446\u0435\u043d\u0438\u0442\u044c \u0432 10-20% \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0431\u0435\u043d\u0447\u043c\u0430\u0440\u043a\u0430. \u0422\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435, \u0441 \u0443\u0447\u0451\u0442\u043e\u043c \u0432\u044b\u0448\u0435\u043f\u0440\u0438\u0432\u0435\u0434\u0451\u043d\u043d\u044b\u0445 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f \u0441 Apple Rosetta 2, \u043e\u0431\u0449\u0430\u044f \u043a\u0430\u0440\u0442\u0438\u043d\u0430 \u0431\u0443\u0434\u0435\u0442 \u044f\u0441\u043d\u0430.<\/p>\n

\u00a0Geekbench 5.4.1<\/strong> (\u0447\u0435\u043c \u0432\u044b\u0448\u0435 \u2013 \u0442\u0435\u043c \u043b\u0443\u0447\u0448\u0435):<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Bench name<\/p>\n<\/td>\n

\n

ARM64 Windows<\/p>\n<\/td>\n

\n

MS BT (x86)<\/p>\n<\/td>\n

\n

MS BT efficiency<\/p>\n<\/td>\n

\n

ARM64 WSL<\/p>\n<\/td>\n

\n

ExaGear (x86)<\/p>\n<\/td>\n

\n

ExaGear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

AES-XTS<\/p>\n<\/td>\n

\n

872<\/p>\n<\/td>\n

\n

437<\/p>\n<\/td>\n

\n

50.1%<\/p>\n<\/td>\n

\n

892<\/p>\n<\/td>\n

\n

437<\/p>\n<\/td>\n

\n

49.0%<\/p>\n<\/td>\n<\/tr>\n

\n

Text Compression<\/p>\n<\/td>\n

\n

514<\/p>\n<\/td>\n

\n

381<\/p>\n<\/td>\n

\n

74.1%<\/p>\n<\/td>\n

\n

518<\/p>\n<\/td>\n

\n

451<\/p>\n<\/td>\n

\n

87.1%<\/p>\n<\/td>\n<\/tr>\n

\n

Image Compression<\/p>\n<\/td>\n

\n

577<\/p>\n<\/td>\n

\n

328<\/p>\n<\/td>\n

\n

56.8%<\/p>\n<\/td>\n

\n

606<\/p>\n<\/td>\n

\n

433<\/p>\n<\/td>\n

\n

71.5%<\/p>\n<\/td>\n<\/tr>\n

\n

Navigation<\/p>\n<\/td>\n

\n

402<\/p>\n<\/td>\n

\n

393<\/p>\n<\/td>\n

\n

97.8%<\/p>\n<\/td>\n

\n

522<\/p>\n<\/td>\n

\n

502<\/p>\n<\/td>\n

\n

96.2%<\/p>\n<\/td>\n<\/tr>\n

\n

HTML5<\/p>\n<\/td>\n

\n

517<\/p>\n<\/td>\n

\n

224<\/p>\n<\/td>\n

\n

43.3%<\/p>\n<\/td>\n

\n

522<\/p>\n<\/td>\n

\n

371<\/p>\n<\/td>\n

\n

71.1%<\/p>\n<\/td>\n<\/tr>\n

\n

SQLite<\/p>\n<\/td>\n

\n

534<\/p>\n<\/td>\n

\n

240<\/p>\n<\/td>\n

\n

44.9%<\/p>\n<\/td>\n

\n

565<\/p>\n<\/td>\n

\n

412<\/p>\n<\/td>\n

\n

72.9%<\/p>\n<\/td>\n<\/tr>\n

\n

PDF Rendering<\/p>\n<\/td>\n

\n

515<\/p>\n<\/td>\n

\n

253<\/p>\n<\/td>\n

\n

49.1%<\/p>\n<\/td>\n

\n

574<\/p>\n<\/td>\n

\n

462<\/p>\n<\/td>\n

\n

80.5%<\/p>\n<\/td>\n<\/tr>\n

\n

Text Rendering<\/p>\n<\/td>\n

\n

530<\/p>\n<\/td>\n

\n

264<\/p>\n<\/td>\n

\n

49.8%<\/p>\n<\/td>\n

\n

544<\/p>\n<\/td>\n

\n

430<\/p>\n<\/td>\n

\n

79.0%<\/p>\n<\/td>\n<\/tr>\n

\n

Clang<\/p>\n<\/td>\n

\n

485<\/p>\n<\/td>\n

\n

187<\/p>\n<\/td>\n

\n

38.6%<\/p>\n<\/td>\n

\n

601<\/p>\n<\/td>\n

\n

405<\/p>\n<\/td>\n

\n

67.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Camera<\/p>\n<\/td>\n

\n

437<\/p>\n<\/td>\n

\n

221<\/p>\n<\/td>\n

\n

50.6%<\/p>\n<\/td>\n

\n

479<\/p>\n<\/td>\n

\n

308<\/p>\n<\/td>\n

\n

64.3%<\/p>\n<\/td>\n<\/tr>\n

\n

N-Body Physics<\/p>\n<\/td>\n

\n

390<\/p>\n<\/td>\n

\n

253<\/p>\n<\/td>\n

\n

64.9%<\/p>\n<\/td>\n

\n

390<\/p>\n<\/td>\n

\n

317<\/p>\n<\/td>\n

\n

81.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Rigid Body Physics<\/p>\n<\/td>\n

\n

634<\/p>\n<\/td>\n

\n

299<\/p>\n<\/td>\n

\n

47.2%<\/p>\n<\/td>\n

\n

717<\/p>\n<\/td>\n

\n

485<\/p>\n<\/td>\n

\n

67.6%<\/p>\n<\/td>\n<\/tr>\n

\n

Gaussian Blur<\/p>\n<\/td>\n

\n

279<\/p>\n<\/td>\n

\n

217<\/p>\n<\/td>\n

\n

77.8%<\/p>\n<\/td>\n

\n

282<\/p>\n<\/td>\n

\n

238<\/p>\n<\/td>\n

\n

84.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Face Detection<\/p>\n<\/td>\n

\n

598<\/p>\n<\/td>\n

\n

301<\/p>\n<\/td>\n

\n

50.3%<\/p>\n<\/td>\n

\n

657<\/p>\n<\/td>\n

\n

387<\/p>\n<\/td>\n

\n

58.9%<\/p>\n<\/td>\n<\/tr>\n

\n

Horizon Detection<\/p>\n<\/td>\n

\n

484<\/p>\n<\/td>\n

\n

235<\/p>\n<\/td>\n

\n

48.6%<\/p>\n<\/td>\n

\n

542<\/p>\n<\/td>\n

\n

331<\/p>\n<\/td>\n

\n

61.1%<\/p>\n<\/td>\n<\/tr>\n

\n

Image Inpainting<\/p>\n<\/td>\n

\n

580<\/p>\n<\/td>\n

\n

419<\/p>\n<\/td>\n

\n

72.2%<\/p>\n<\/td>\n

\n

775<\/p>\n<\/td>\n

\n

504<\/p>\n<\/td>\n

\n

65.0%<\/p>\n<\/td>\n<\/tr>\n

\n

HDR<\/p>\n<\/td>\n

\n

637<\/p>\n<\/td>\n

\n

481<\/p>\n<\/td>\n

\n

75.5%<\/p>\n<\/td>\n

\n

1035<\/p>\n<\/td>\n

\n

843<\/p>\n<\/td>\n

\n

81.4%<\/p>\n<\/td>\n<\/tr>\n

\n

Ray Tracing<\/p>\n<\/td>\n

\n

758<\/p>\n<\/td>\n

\n

296<\/p>\n<\/td>\n

\n

39.1%<\/p>\n<\/td>\n

\n

762<\/p>\n<\/td>\n

\n

513<\/p>\n<\/td>\n

\n

67.3%<\/p>\n<\/td>\n<\/tr>\n

\n

Structure from Motion<\/p>\n<\/td>\n

\n

379<\/p>\n<\/td>\n

\n

203<\/p>\n<\/td>\n

\n

53.6%<\/p>\n<\/td>\n

\n

457<\/p>\n<\/td>\n

\n

289<\/p>\n<\/td>\n

\n

63.2%<\/p>\n<\/td>\n<\/tr>\n

\n

Speech Recognition<\/p>\n<\/td>\n

\n

346<\/p>\n<\/td>\n

\n

283<\/p>\n<\/td>\n

\n

81.8%<\/p>\n<\/td>\n

\n

355<\/p>\n<\/td>\n

\n

312<\/p>\n<\/td>\n

\n

87.9%<\/p>\n<\/td>\n<\/tr>\n

\n

Machine Learning<\/p>\n<\/td>\n

\n

252<\/p>\n<\/td>\n

\n

108<\/p>\n<\/td>\n

\n

42.9%<\/p>\n<\/td>\n

\n

240<\/p>\n<\/td>\n

\n

128<\/p>\n<\/td>\n

\n

53.3%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

55.6%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

70.9%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

SpecCPU2017 <\/strong>(\u0441\u0435\u043a\u0443\u043d\u0434\u044b)<\/p>\n

\u041a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440: clang 11.0\u00a0 -O3 -flto<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

INT tests<\/p>\n<\/td>\n

\n

ARM64 Windows<\/p>\n<\/td>\n

\n

MS BT (x86)<\/p>\n<\/td>\n

\n

MS BT efficiency<\/p>\n<\/td>\n

\n

ARM64 WSL<\/p>\n<\/td>\n

\n

ExaGear (x86)<\/p>\n<\/td>\n

\n

ExaGear efficiency<\/p>\n<\/td>\n<\/tr>\n

\n

500.perlbench_r<\/p>\n<\/td>\n

\n

824<\/p>\n<\/td>\n

\n

1526<\/p>\n<\/td>\n

\n

54.0%<\/p>\n<\/td>\n

\n

800<\/p>\n<\/td>\n

\n

1042<\/p>\n<\/td>\n

\n

76.8%<\/p>\n<\/td>\n<\/tr>\n

\n

502.gcc_r<\/p>\n<\/td>\n

\n

716<\/p>\n<\/td>\n

\n

1119<\/p>\n<\/td>\n

\n

64.0%<\/p>\n<\/td>\n

\n

690<\/p>\n<\/td>\n

\n

820<\/p>\n<\/td>\n

\n

84.1%<\/p>\n<\/td>\n<\/tr>\n

\n

505.mcf_r<\/p>\n<\/td>\n

\n

781<\/p>\n<\/td>\n

\n

1069<\/p>\n<\/td>\n

\n

73.1%<\/p>\n<\/td>\n

\n

792<\/p>\n<\/td>\n

\n

992<\/p>\n<\/td>\n

\n

79.8%<\/p>\n<\/td>\n<\/tr>\n

\n

520.omnetpp_r<\/p>\n<\/td>\n

\n

1280<\/p>\n<\/td>\n

\n

2025<\/p>\n<\/td>\n

\n

63.2%<\/p>\n<\/td>\n

\n

1186<\/p>\n<\/td>\n

\n

1414<\/p>\n<\/td>\n

\n

83.9%<\/p>\n<\/td>\n<\/tr>\n

\n

525.x264_r<\/p>\n<\/td>\n

\n

460<\/p>\n<\/td>\n

\n

771<\/p>\n<\/td>\n

\n

59.7%<\/p>\n<\/td>\n

\n

481<\/p>\n<\/td>\n

\n

638<\/p>\n<\/td>\n

\n

75.4%<\/p>\n<\/td>\n<\/tr>\n

\n

531.deepsjeng_r<\/p>\n<\/td>\n

\n

507<\/p>\n<\/td>\n

\n

711<\/p>\n<\/td>\n

\n

71.3%<\/p>\n<\/td>\n

\n

524<\/p>\n<\/td>\n

\n

629<\/p>\n<\/td>\n

\n

83.3%<\/p>\n<\/td>\n<\/tr>\n

\n

541.leela_r<\/p>\n<\/td>\n

\n

564<\/p>\n<\/td>\n

\n

897<\/p>\n<\/td>\n

\n

62.9%<\/p>\n<\/td>\n

\n

547<\/p>\n<\/td>\n

\n

652<\/p>\n<\/td>\n

\n

83.9%<\/p>\n<\/td>\n<\/tr>\n

\n

557.xz_r<\/p>\n<\/td>\n

\n

733<\/p>\n<\/td>\n

\n

938<\/p>\n<\/td>\n

\n

78.1%<\/p>\n<\/td>\n

\n

733<\/p>\n<\/td>\n

\n

827<\/p>\n<\/td>\n

\n

88.6%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean Int<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

65.4%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

81.9%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n<\/tr>\n

\n

FP tests<\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n<\/tr>\n

\n

508.namd_r<\/p>\n<\/td>\n

\n

423<\/p>\n<\/td>\n

\n

698<\/p>\n<\/td>\n

\n

60.6%<\/p>\n<\/td>\n

\n

429<\/p>\n<\/td>\n

\n

619<\/p>\n<\/td>\n

\n

69.3%<\/p>\n<\/td>\n<\/tr>\n

\n

510.parest_r<\/p>\n<\/td>\n

\n

1006<\/p>\n<\/td>\n

\n

1274<\/p>\n<\/td>\n

\n

79.0%<\/p>\n<\/td>\n

\n

970<\/p>\n<\/td>\n

\n

1179<\/p>\n<\/td>\n

\n

82.3%<\/p>\n<\/td>\n<\/tr>\n

\n

511.povray_r<\/p>\n<\/td>\n

\n

808<\/p>\n<\/td>\n

\n

1540<\/p>\n<\/td>\n

\n

52.5%<\/p>\n<\/td>\n

\n

761<\/p>\n<\/td>\n

\n

1027<\/p>\n<\/td>\n

\n

74.1%<\/p>\n<\/td>\n<\/tr>\n

\n

519.lbm_r<\/p>\n<\/td>\n

\n

428<\/p>\n<\/td>\n

\n

767<\/p>\n<\/td>\n

\n

55.8%<\/p>\n<\/td>\n

\n

429<\/p>\n<\/td>\n

\n

549<\/p>\n<\/td>\n

\n

78.1%<\/p>\n<\/td>\n<\/tr>\n

\n

526.blender_r<\/p>\n<\/td>\n

\n

474<\/p>\n<\/td>\n

\n

688<\/p>\n<\/td>\n

\n

68.9%<\/p>\n<\/td>\n

\n

484<\/p>\n<\/td>\n

\n

716<\/p>\n<\/td>\n

\n

67.6%<\/p>\n<\/td>\n<\/tr>\n

\n

538.imagick_r<\/p>\n<\/td>\n

\n

560<\/p>\n<\/td>\n

\n

1075<\/p>\n<\/td>\n

\n

52.1%<\/p>\n<\/td>\n

\n

566<\/p>\n<\/td>\n

\n

847<\/p>\n<\/td>\n

\n

66.8%<\/p>\n<\/td>\n<\/tr>\n

\n

544.nab_r<\/p>\n<\/td>\n

\n

522<\/p>\n<\/td>\n

\n

1050<\/p>\n<\/td>\n

\n

49.7%<\/p>\n<\/td>\n

\n

517<\/p>\n<\/td>\n

\n

617<\/p>\n<\/td>\n

\n

83.8%<\/p>\n<\/td>\n<\/tr>\n

\n

GeoMean FP<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

59.0%<\/strong><\/p>\n<\/td>\n

\n

\n<\/td>\n

\n

\n<\/td>\n

\n

74.3%<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

\u0421\u0431\u043e\u0440\u043a\u0430 SpecCPU2006 \u043f\u043e\u0434 Windows \u0432\u044b\u0437\u0432\u0430\u043b\u0430 \u043c\u043d\u043e\u0433\u043e\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044b\u0435 \u043e\u0448\u0438\u0431\u043a\u0438 \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0446\u0438\u0438. \u041f\u043e\u044d\u0442\u043e\u043c\u0443 \u0431\u044b\u043b\u043e \u0440\u0435\u0448\u0435\u043d\u043e \u043d\u0435 \u0442\u0440\u0430\u0442\u0438\u0442\u044c \u0432\u0440\u0435\u043c\u044f \u043d\u0430 \u0437\u0430\u043f\u0443\u0441\u043a<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-415021","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/415021","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=415021"}],"version-history":[{"count":0,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/415021\/revisions"}],"wp:attachment":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=415021"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=415021"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=415021"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}