Weighted pushdown systems and their application to interprocedural dataflow analysis

AbstractRecently, pushdown systems (PDSs) have been extended to weighted PDSs, in which each transition is labeled with a value, and the goal is to determine the meet-over-all-paths value (for paths that meet a certain criterion). This paper shows how weighted PDSs yield new algorithms for certain classes of interprocedural dataflow-analysis problems

Faster Algorithms for Weighted Recursive State Machines

Pushdown systems (PDSs) and recursive state machines (RSMs), which are linearly equivalent, are standard models for interprocedural analysis. Yet RSMs are more convenient as they (a) explicitly model function calls and returns, and (b) specify many natural parameters for algorithmic analysis, e.g., the number of entries and exits. We consider a general framework where RSM transitions are labeled from a semiring and path properties are algebraic with semiring operations, which can model, e.g., interprocedural reachability and dataflow analysis problems. Our main contributions are new algorithms for several fundamental problems. As compared to a direct translation of RSMs to PDSs and the best-known existing bounds of PDSs, our analysis algorithm improves the complexity for finite-height semirings (that subsumes reachability and standard dataflow properties). We further consider the problem of extracting distance values from the representation structures computed by our algorithm, and give efficient algorithms that distinguish the complexity of a one-time preprocessing from the complexity of each individual query. Another advantage of our algorithm is that our improvements carry over to the concurrent setting, where we improve the best-known complexity for the context-bounded analysis of concurrent RSMs. Finally, we provide a prototype implementation that gives a significant speed-up on several benchmarks from the SLAM/SDV project

Faster Algorithms for Dynamic Algebraic Queries in Basic RSMs with Constant Treewidth

Interprocedural analysis is at the heart of numerous applications in programming languages, such as alias analysis, constant propagation, and so on. Recursive state machines (RSMs) are standard models for interprocedural analysis. We consider a general framework with RSMs where the transitions are labeled from a semiring and path properties are algebraic with semiring operations. RSMs with algebraic path properties can model interprocedural dataflow analysis problems, the shortest path problem, the most probable path problem, and so on. The traditional algorithms for interprocedural analysis focus on path properties where the starting point is fixed as the entry point of a specific method. In this work, we consider possible multiple queries as required in many applications such as in alias analysis. The study of multiple queries allows us to bring in an important algorithmic distinction between the resource usage of the one-time preprocessing vs for each individual query. The second aspect we consider is that the control flow graphs for most programs have constant treewidth. Our main contributions are simple and implementable algorithms that support multiple queries for algebraic path properties for RSMs that have constant treewidth. Our theoretical results show that our algorithms have small additional one-time preprocessing but can answer subsequent queries significantly faster as compared to the current algorithmic solutions for interprocedural dataflow analysis. We have also implemented our algorithms and evaluated their performance for performing on-demand interprocedural dataflow analysis on various domains, such as for live variable analysis and reaching definitions, on a standard benchmark set. Our experimental results align with our theoretical statements and show that after a lightweight preprocessing, on-demand queries are answered much faster than the standard existing algorithmic approaches

Systematic Approaches to Advanced Information Flow Analysis – and Applications to Software Security

In dieser Arbeit berichte ich über Anwendungen von Slicing und Programmabhängigkeitsgraphen (PAG) in der Softwaresicherheit. Außerdem schlage ich ein Analyse-Rahmenwerk vor, welches Datenflussanalyse auf Kontrollflussgraphen und Slicing auf Programmabhängigkeitsgraphen verallgemeinert. Mit einem solchen Rahmenwerk lassen sich neue PAG-basierte Analysen systematisch ableiten, die über Slicing hinausgehen. Die Hauptthesen meiner Arbeit lauten wie folgt: (1) PAG-basierte Informationsflusskontrolle ist nützlich, praktisch anwendbar und relevant. (2) Datenflussanalyse kann systematisch auf Programmabhängigkeitsgraphen angewendet werden. (3) Datenflussanalyse auf Programmabhängigkeitsgraphen ist praktisch durchführbar

Parallelizing with BDSC, a resource-constrained scheduling algorithm for shared and distributed memory systems

International audienceWe introduce a new parallelization framework for scientific computing based on BDSC, an efficient automatic scheduling algorithm for parallel programs in the presence of resource constraints on the number of processors and their local memory size. BDSC extends Yang and Gerasoulis's Dominant Sequence Clus-tering (DSC) algorithm; it uses sophisticated cost models and addresses both shared and distributed parallel memory architectures. We describe BDSC, its integration within the PIPS compiler infrastructure and its application to the parallelization of four well-known scientific applications: Harris, ABF, equake and IS. Our experiments suggest that BDSC's focus on efficient resource man-agement leads to significant parallelization speedups on both shared and dis-tributed memory systems, improving upon DSC results, as shown by the com-parison of the sequential and parallelized versions of these four applications running on both OpenMP and MPI frameworks