Results 81 to 90 of about 406 (180)
, 2022 Метою роботи є пошук неоптимального машинного коду, який генерується iнфраструктурою LLVM для мов програмування, якi її використовують та подальша оптимiзацiя. Результатом роботи стала реалiзацiя проходу, який знаходить патерн перевiрки двох цiлих знакових чисел на одинаковий знак наївним методом та перетворює вiдповiдний IR на оптимальнiший ...
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Classification of Obfuscation Techniques in LLVM IR: Machine Learning on Vector Representations
Machine Learning and Knowledge ExtractionWe present a novel methodology for classifying code obfuscation techniques in LLVM IR program embeddings. We apply isolated and layered code obfuscations to C source code using the Tigress obfuscator, compile them to LLVM IR, and convert each IR code ...
Sebastian Raubitzek +3 more
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, 2011 Massively parallel architectures are gaining momentum thanks to the opportunities for both high performance and low-power consumption. After being a matter for experiments in academia, manycores are now in production and industries with products that rely on high performance, for example, in the field of telecom and radar, are in the process of ...
Ullah, Haseeb, Mofakhar, Amir
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, 2023 У роботі наведено огляд компонентів платформи LLVM, проаналізовано механізми безпеки та виконано тестування захищеностів від шкідливого коду. В аналіз безпеки входить перевірка можливих вразливостей, та систем захисту які інтегровані в системі.
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Translation Validation for LLVM’s AArch64 Backend
Proceedings of the ACM on Programming LanguagesLLVM's backends translate its intermediate representation (IR) to assembly or object code. Alongside register allocation and instruction selection, these backends contain many analogues of components traditionally associated with compiler middle ends: dataflow analyses, common subexpression elimination, loop invariant code motion, and a first-
Ryan Berger +8 more
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LLVM Code Generation for Open Dylan
, 2020 The Open Dylan compiler, DFMC, was originally designed in the 1990s to compile Dylan language code targeting the 32-bit Intel x86 platform, or other platforms via portable C. As platforms have evolved since, this approach has been unable to provide efficient code generation for a broader range of target platforms, or to adequately support tools such as
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Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing. [PDF]
Genome Biol, 2023 Smajlović H +4 more
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CoMeT: Configurable Tagged Memory Extension. [PDF]
Sensors (Basel), 2021 Lee J, Pratama D, Kim M, Kim H, Kwon D.
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A Low-Latency Optimization of a Rust-Based Secure Operating System for Embedded Devices. [PDF]
Sensors (Basel), 2022 Culic I, Vochescu A, Radovici A.
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