A program is said to be isochronous if its running time does not depend on classified information. The programming languages literature contains much work that transforms programs to ensure isochronicity. The current state-of-the-art approach is a code transformation technique due to Wu et al., published in 2018. That technique has an important virtue: it ensures that the transformed program runs exactly the same set of operations, regardless of inputs. However, in this paper we demonstrate that it has also a shortcoming: it might add out-of-bounds memory accesses into programs that were originally memory sound. From this observation, we show how to deliver the same runtime guarantees that Wu et al. provide, in a memory-safe way. In addition to being safer, our LLVM-based implementation is more efficient than its original inspiration, achieving shorter repairing times, and producing code that is smaller and faster.

Memory-safe elimination of side channels

Compilers are usually distributed with a test framework. This framework supports the task of tuning optimizations and static analyses. As an example, clang has a test suite that, in March 2019, counted 259 benchmarks. Although in principle a large collection, this number is small once we consider the needs of the automatic tuning techniques that became fashionable recently. To mitigate the problems caused by such lack of benchmarks, this paper introduces a technique that allows the automatic construction of compilable programs out of open-source repositories. Our approach has made it possible to build, in less than 24 hours, a collection with over 500 thousand functions that clang can compile. In this paper, we show that such abundance of data gives us precise information about the behavior of compiler optimizations, and lets us create accurate prediction models. This collection of benchmarks is today freely available to the open-source community.

Synthesis of Benchmarks for the C Programming Language by Mining Software Repositories

In high performance computing (HPC), scientific simulation codes are executed repeatedly with different inputs. The peak performance of these programs heavily depends on various compiler optimizations, which are often selected agnostically on program input or may be selected with sensitivity to just a single input. When subsequently executed, often with different inputs, performance may suffer for all or all but the one input tested, and for the latter potentially even compared to the O3 baseline. This work proposes a new auto-tuning framework, CodeSeer, to assess and improve existing input-agnostic or single-input centric rigid application tuning methods. Aided by CodeSeer, it is observed that modern HPC programs expose different types of input sensitivities, which present a significant challenge for prior work. To tackle this problem, CodeSeer proceeds with several machine learning models to predict the best per-input code variant on-the-fly. Our evaluation shows that CodeSeer incurs less than 0.01 second overhead, predicts the best code variant with a geometric mean precision 92% of the time and is capable of improving per-input peak performance to unprecedented levels.

https://dl.acm.org/doi/pdf/10.1145/3392717.3392741

CodeSeer: input-dependent code variants selection via machine learning

Modular elliptic curves and fermat's last theorem

Owing to the continued use of C (and C++), spatial safety violations (e.g., buffer overflows) still constitute one of today's most dangerous and prevalent security vulnerabilities. To combat these violations, Checked C extends C with bounds-enforced checked pointer types. Checked C is essentially a gradually typed spatially safe C - checked pointers are backwards-binary compatible with legacy pointers, and the language allows them to be added piecemeal, rather than necessarily all at once, so that safety retrofitting can be incremental. This paper presents a semi-automated process for porting a legacy C program to Checked C. The process centers on 3C, a static analysis-based annotation tool. 3C employs two novel static analysis algorithms - typ3c and boun3c - to annotate legacy pointers as checked pointers, and to infer array bounds annotations for pointers that need them. 3C performs a root cause analysis to direct a human developer to code that should be refactored; once done, 3C can be re-run to infer further annotations (and updated root causes). Experiments on 11 programs totaling 319KLoC show 3C to be effective at inferring checked pointer types, and experience with previously and newly ported code finds 3C works well when combined with human-driven refactoring.

C to checked C by 3c

This paper presents a technique to generate in-bounds inputs for arrays used in memory-unsafe programming languages, such as C and C++. We show that most memory indexation found in actual C programs follows patterns that are easy to analyze statically. Based on this observation, we show how symbolic range analysis can be used to establish contracts between the arguments of a function and the arrays used within that function. To demonstrate the effectiveness of our ideas, we use them to implement Griffin-TG, a tool to stress-test C programs whose source code might be partially available. We show how Griffin-TG improves Aprof, a well-known algorithmic profiling tool, and we show how it lets us enrich Polybench with a large set of new inputs.

Generation of in-bounds inputs for arrays in memory-unsafe languages

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Generation of in-bounds inputs for arrays in memory-unsafe languages