Understanding GCC builtins to develop better tools

Rigger, Manuel; Marr, Stefan; Adams, Bram; Mössenböck, Hanspeter

doi:10.1145/3338906.3338907

Cited by 9 publications

(2 citation statements)

References 58 publications

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“…While CPUs have flags set whenever an overflow occurs, it is impossible to directly access them in C/C++. However, compiler builtins such as __builtin_add_overflow are provided by mature compilers [Rigger et al 2019], which, apart from performing the arithmetic, also return the overflow flag status. The multi-versioned hot loops are implemented in terms of such intrinsics: arithmetic operations are checked, and the overflow status is reported to the dispatcher.…”

Section: Transprecision Computingmentioning

confidence: 99%

Fast linear programming through transprecision computing on small and sparse data

Grosser

Theodoridis

Falkenstein

et al. 2020

Proc. ACM Program. Lang.

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A plethora of program analysis and optimization techniques rely on linear programming at their heart. However, such techniques are often considered too slow for production use. While today's best solvers are optimized for complex problems with thousands of dimensions, linear programming, as used in compilers, is typically applied to small and seemingly trivial problems, but to many instances in a single compilation run. As a result, compilers do not benefit from decades of research on optimizing large-scale linear programming. We design a simplex solver targeted at compilers. A novel theory of transprecision computation applied from individual elements to full data-structures provides the computational foundation. By carefully combining it with optimized representations for small and sparse matrices and specialized small-coefficient algorithms, we (1) reduce memory traffic, (2) exploit wide vectors, and (3) use low-precision arithmetic units effectively. We evaluate our work by embedding our solver into a state-of-the-art integer set library and implement one essential operation, coalescing, on top of our transprecision solver. Our evaluation shows more than an order-of-magnitude speedup on the core simplex pivot operation and a mean speedup of 3.2x (vs. GMP) and 4.6x (vs. IMath) for the optimized coalescing operation. Our results demonstrate that our optimizations exploit the wide SIMD instructions of modern microarchitectures effectively. We expect our work to provide foundations for a future integer set library that uses transprecision arithmetic to accelerate compiler analyses.

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Section: Transprecision Computingmentioning

confidence: 99%

Fast linear programming through transprecision computing on small and sparse data

Grosser

Theodoridis

Falkenstein

et al. 2020

Proc. ACM Program. Lang.

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“…Although languages often define a large number of builtins, not all of them are frequently used. For example, Rigger et al [52,53] found out that most software projects in the C language only use a small subset of the inline assembly and compiler-specific builtins available.…”

Section: Propagation Semanticsmentioning

confidence: 99%

Multi-language dynamic taint analysis in a polyglot virtual machine

Kreindl¹,

Bonetta

Leopoldseder³

et al. 2020

Proceedings of the 17th International Conference on Managed Programming Languages and Runtimes

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Dynamic taint analysis is a popular program analysis technique in which sensitive data is marked as tainted and the propagation of tainted data is tracked in order to determine whether that data reaches critical program locations. This analysis technique has been successfully applied to software vulnerability detection, malware analysis, testing and debugging, and many other fields. However, existing approaches of dynamic taint analysis are either languagespecific or they target native code. Neither is suitable for analyzing applications in which high-level dynamic languages such as JavaScript and low-level languages such as C interact. In these approaches, the language boundary forms an opaque barrier that prevents a sound analysis of data flow in the other language and can thus lead to the analysis being evaded.In this paper we introduce TruffleTaint, a platform for multilanguage dynamic taint analysis that uses language-independent techniques for propagating taint labels to overcome the language boundary but still allows for language-specific taint propagation rules. Based on the Truffle framework for implementing runtimes for programming languages, TruffleTaint supports propagating taint in and between a selection of dynamic and low-level programming languages and can be easily extended to support additional languages. We demonstrate TruffleTaint's propagation capabilities and evaluate its performance using several benchmarks from the Computer Language Benchmarks Game, which we implemented as combinations of C, JavaScript and Python code and which we adapted to propagate taint in various scenarios of language interaction. Our evaluation shows that TruffleTaint causes low to zero slowdown when no taint is introduced, rivaling state-of-the-art dynamic taint analysis platforms, and only up to ∼40x slowdown when taint is introduced.

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Online recognition of peanut leaf diseases based on the data balance algorithm and deep transfer learning

Feng

et al. 2022

Precision Agric

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Understanding GCC builtins to develop better tools

Cited by 9 publications

References 58 publications

Fast linear programming through transprecision computing on small and sparse data

Fast linear programming through transprecision computing on small and sparse data

Multi-language dynamic taint analysis in a polyglot virtual machine

Online recognition of peanut leaf diseases based on the data balance algorithm and deep transfer learning

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