Vectorization system for unstructured codes with a Data-parallel Compiler IR

With Dennard Scaling coming to an end, Single Instruction Multiple Data (SIMD) offers itself as a way to improve the compute throughput of CPUs. One fundamental technique in SIMD code generators is the vectorization of data-parallel code regions. This has applications in outer-loop vectorization, whole-function vectorization and vectorization of explicitly data-parallel languages. This thesis makes contributions to the reliable vectorization of data-parallel code regions with unstructured, reducible control flow. Reducibility is the case in practice where all control-flow loops have exactly one entry point. We present P-LLVM, a novel, full-featured, intermediate representation for vectorizers that provides a semantics for the code region at every stage of the vectorization pipeline. Partial control-flow linearization is a novel partial if-conversion scheme, an essential technique to vectorize divergent control flow. Different to prior techniques, partial linearization has linear running time, does not insert additional branches or blocks and gives proved guarantees on the control flow retained. Divergence of control induces value divergence at join points in the control-flow graph (CFG). We present a novel control-divergence analysis for directed acyclic graphs with optimal running time and prove that it is correct and precise under common static assumptions. We extend this technique to obtain a quadratic-time, control-divergence analysis for arbitrary reducible CFGs. For this analysis, we show on a range of realistic examples how earlier approaches are either less precise or incorrect. We present a feature-complete divergence analysis for P-LLVM programs. The analysis is the first to analyze stack-allocated objects in an unstructured control setting. Finally, we generalize single-dimensional vectorization of outer loops to multi-dimensional tensorization of loop nests. SIMD targets benefit from tensorization through more opportunities for re-use of loaded values and more efficient memory access behavior. The techniques were implemented in the Region Vectorizer (RV) for vectorization and TensorRV for loop-nest tensorization. Our evaluation validates that the general-purpose RV vectorization system matches the performance of more specialized approaches. RV performs on par with the ISPC compiler, which only supports its structured domain-specific language, on a range of tree traversal codes with complex control flow. RV is able to outperform the loop vectorizers of state-of-the-art compilers, as we show for the SPEC2017 nab_s benchmark and the XSBench proxy application.Mit dem Ausreizen des Dennard Scalings erreichen die gewohnten Zuwächse in der skalaren Rechenleistung zusehends ihr Ende. Moderne Prozessoren setzen verstärkt auf parallele Berechnung, um den Rechendurchsatz zu erhöhen. Hierbei spielen SIMD Instruktionen (Single Instruction Multiple Data), die eine Operation gleichzeitig auf mehrere Eingaben anwenden, eine zentrale Rolle. Eine fundamentale Technik, um SIMD Programmcode zu erzeugen, ist der Einsatz datenparalleler Vektorisierung. Diese unterliegt populären Verfahren, wie der Vektorisierung äußerer Schleifen, der Vektorisierung gesamter Funktionen bis hin zu explizit datenparallelen Programmiersprachen. Der Beitrag der vorliegenden Arbeit besteht darin, ein zuverlässiges Vektorisierungssystem für datenparallelen Code mit reduziblem Steuerfluss zu entwickeln. Diese Anforderung ist für alle Steuerflussgraphen erfüllt, deren Schleifen nur einen Eingang haben, was in der Praxis der Fall ist. Wir präsentieren P-LLVM, eine ausdrucksstarke Zwischendarstellung für Vektorisierer, welche dem Programm in jedem Stadium der Transformation von datenparallelem Code zu SIMD Code eine definierte Semantik verleiht. Partielle Steuerfluss-Linearisierung ist ein neuer Algorithmus zur If-Conversion, welcher Sprünge erhalten kann. Anders als existierende Verfahren hat Partielle Linearisierung eine lineare Laufzeit und fügt keine neuen Sprünge oder Blöcke ein. Wir zeigen Kriterien, unter denen der Algorithmus Steuerfluss erhält, und beweisen diese. Steuerflussdivergenz induziert Divergenz an Punkten zusammenfließenden Steuerflusses. Wir stellen eine neue Steuerflussdivergenzanalyse für azyklische Graphen mit optimaler Laufzeit vor und beweisen deren Korrektheit und Präzision. Wir verallgemeinern die Technik zu einem Algorithmus mit quadratischer Laufzeit für beliebiege, reduzible Steuerflussgraphen. Eine Studie auf realistischen Beispielgraphen zeigt, dass vergleichbare Techniken entweder weniger präsize sind oder falsche Ergebnisse liefern. Ebenfalls präsentieren wir eine Divergenzanalyse für P-LLVM Programme. Diese Analyse ist die erste Divergenzanalyse, welche Divergenz in stapelallokierten Objekten unter unstrukturiertem Steuerfluss analysiert. Schließlich generalisieren wir die eindimensionale Vektorisierung von äußeren Schleifen zur multidimensionalen Tensorisierung von Schleifennestern. Tensorisierung eröffnet für SIMD Prozessoren mehr Möglichkeiten, bereits geladene Werte wiederzuverwenden und das Speicherzugriffsverhalten des Programms zu optimieren, als dies mit Vektorisierung der Fall ist. Die vorgestellten Techniken wurden in den Region Vectorizer (RV) für Vektorisierung und TensorRV für die Tensorisierung von Schleifennestern implementiert. Wir zeigen auf einer Reihe von steuerflusslastigen Programmen für die Traversierung von Baumdatenstrukturen, dass RV das gleiche Niveau erreicht wie der ISPC Compiler, welcher nur seine strukturierte Eingabesprache verarbeiten kann. RV kann schnellere SIMD-Programme erzeugen als die Schleifenvektorisierer in aktuellen Industriecompilern. Dies demonstrieren wir mit dem nab_s benchmark aus der SPEC2017 Benchmarksuite und der XSBench Proxy-Anwendung

Defensive Points-To Analysis: Effective Soundness via Laziness

We present a defensive may-point-to analysis approach, which offers soundness even in the presence of arbitrary opaque code: all non-empty points-to sets computed are guaranteed to be over-approximations of the sets of values arising at run time. A key design tenet of the analysis is laziness: the analysis computes points-to relationships only for variables or objects that are guaranteed to never escape into opaque code. This means that the analysis misses some valid inferences, yet it also never wastes work to compute sets of values that are not "complete", i.e., that may be missing elements due to opaque code. Laziness enables great efficiency, allowing a highly precise points-to analysis (such as a 5-call-site-sensitive, flow-sensitive analysis). Despite its conservative nature, our analysis yields sound, actionable results for a large subset of the program code, achieving (under worst-case assumptions) 34-74% of the program coverage of an unsound state-of-the-art analysis for real-world programs

Transformations Déclaratives dans le Modèle Polyédrique

Despite the availability of sophisticated automatic optimizers, performance-critical code sections are in practice still tuned by human experts. Pragma-based languages such as OpenMP or OpenACC are the standard interface to apply such transformations to large code bases and loop transformation pragmas would be a straightforward extension to provide fine-grained control over a compilers loop optimizer. However, the manual optimization of programs via explicit sequences of directives is unlikely to fully solve this problem as expressing complex optimization sequences explicitly results in difficult to read and non-performance-portable code. We address this problem by presenting a novel framework of composable program transformations based on the internal tree-like program representation of a polyhedral compiler. Based on a set of tree matchers and transformers, we describe an embedded transformation language which provides the foundation for the development of program optimization tactics. Using this language, we express core building blocks such as loop tiling, fusion, or data-layout-transformations, and compose them to higher-level transformations expressing algorithm-specific optimization strategies for stencils, dense linear-algebra, etc. We expect our approach to simplify the development of polyhedral optimizers and integration of polyhedral and syntactic approaches.Malgré l’existence d’outils sophistiqués d’optimisation automatique, les parties des programmes dont la performance est cruciale sont toujoursoptimisées manuellement par des humains experts. Les langages basés sur des directives “pragma”, tels que OpenMP ou OpenACC, sont une interface typique pour exprimer les transformations sur des grandes bases de code source. Telles directives pour transformer des nids de boucles seraient une extension naturelle permettant de contrôler l’optimiseur de boucles d’une manière précise. Pourtant l’optimisation manuelle des programmes à travers les séquences des directives de transformation n’est pas toujours souhaitable car ces séquences longues et complexes produisent des programmes peu lisibles et ne bénéficiant pas de la portabilité de performance entre les différentes architectures matérielles. Nous proposons une nouvelle approche pour définir les transformations composables des programmes basée sur la représentation interne d’un compilateur polyédrique sous forme de l’arbre. Grâce à un ensemble des “motifs” et “transformateurs” des arbres, nous décrivons un langage de transformation sur lequel nous basons le développement des tactiques d’optimisation. Avec ce langage, il est possible d’exprimer les transformations basiques, telles que le tuilage, la fusion ou la transposition de données, ainsi que la composition de ces transformations afin de définir une stratégie d’optimisation pour les grandes classes des programmes, telles que les pochoirs, les contractions de tenseurs, etc. Notre approche pourrait simplifier le développement des optimiseurs polyédriques et l’intégration des transformations polyédriques et syntaxique

Secure Compilation of Side-Channel Countermeasures: The Case of Cryptographic “Constant-Time”

International audienceSoftware-based countermeasures provide effective mitigation against side-channel attacks, often with minimal efficiency and deployment overheads. Their effectiveness is often amenable to rigorous analysis: specifically, several popular countermeasures can be formalized as information flow policies, and correct implementation of the countermeasures can be verified with state-of-the-art analysis and verification techniques. However , in absence of further justification, the guarantees only hold for the language (source, target, or intermediate representation) on which the analysis is performed. We consider the problem of preserving side-channel countermeasures by compilation for cryptographic "constant-time", a popular countermeasure against cache-based timing attacks. We present a general method, based on the notion of constant-time-simulation, for proving that a compilation pass preserves the constant-time countermeasure. Using the Coq proof assistant, we verify the correctness of our method and of several representative instantiations

When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection

Automated code vulnerability detection has gained increasing attention in recent years. The deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have proven effective in vulnerability detection. The performance of DL-based methods usually relies on the quantity and quality of labeled data. However, the current labeled data are generally automatically collected, such as crawled from human-generated commits, making it hard to ensure the quality of the labels. Prior studies have demonstrated that the non-vulnerable code (i.e., negative labels) tends to be unreliable in commonly-used datasets, while vulnerable code (i.e., positive labels) is more determined. Considering the large numbers of unlabeled data in practice, it is necessary and worth exploring to leverage the positive data and large numbers of unlabeled data for more accurate vulnerability detection. In this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i.e., PositIve and unlabeled Learning mOdel for vulnerability deTection. PILOT only learns from positive and unlabeled data for vulnerability detection. It mainly contains two modules: (1) A distance-aware label selection module, aiming at generating pseudo-labels for selected unlabeled data, which involves the inter-class distance prototype and progressive fine-tuning; (2) A mixed-supervision representation learning module to further alleviate the influence of noise and enhance the discrimination of representations.Comment: This paper is accepted by ASE 202

The last mile: High-Assurance and High-Speed cryptographic implementations

We develop a new approach for building cryptographic implementations. Our approach goes the last mile and delivers assembly code that is provably functionally correct, protected against side-channels, and as efficient as handwritten assembly. We illustrate our approach using ChaCha20Poly1305, one of the two ciphersuites recommended in TLS 1.3, and deliver formally verified vectorized implementations which outperform the fastest non-verified code.We realize our approach by combining the Jasmin framework, which offers in a single language features of high-level and low-level programming, and the EasyCrypt proof assistant, which offers a versatile verification infrastructure that supports proofs of functional correctness and equivalence checking. Neither of these tools had been used for functional correctness before. Taken together, these infrastructures empower programmers to develop efficient and verified implementations by "game hopping", starting from reference implementations that are proved functionally correct against a specification, and gradually introducing program optimizations that are proved correct by equivalence checking.We also make several contributions of independent interest, including a new and extensible verified compiler for Jasmin, with a richer memory model and support for vectorized instructions, and a new embedding of Jasmin in EasyCrypt.This work is partially supported by project ONR N00014-19-1-2292. Manuel Barbosa was supported by grant SFRH/BSAB/143018/2018 awarded by FCT. This work was partially funded by national funds via FCT in the context of project PTDC/CCI-INF/31698/2017