Heap Decomposition for Concurrent Shape Analysis [chapter]

Roman Manevich, Tal Lev-Ami, Mooly Sagiv, Ganesan Ramalingam, Josh Berdine
Lecture Notes in Computer Science  
We demonstrate shape analyses that can achieve a state space reduction exponential in the number of threads compared to the state-of-the-art analyses, while retaining sufficient precision to verify sophisticated properties such as linearizability. The key idea is to abstract the global heap by decomposing it into (not necessarily disjoint) subheaps, abstracting away some correlations between them. These new shape analyses are instances of an analysis framework based on heap decomposition. This
more » ... ramework allows rapid prototyping of complex static analyses by providing efficient abstract transformers given user-specified decomposition schemes. Initial experiments confirm the value of heap decomposition in scaling concurrent shape analyses. Introduction The problem of verifying concurrent programs that manipulate heap-allocated data structures is challenging: it requires considering arbitrarily interleaved threads manipulating unbounded data structures. Both heap-allocated data structures and concurrency can introduce state explosion. Their combination only makes matters worse. This paper develops new static analysis algorithms that address the state space explosion problem in a systematic and generic way. The result of these analyses can be used to automatically establish interesting properties of concurrent heap-manipulating programs such as the absence of null dereferences, the absence of memory leaks, the preservation of data structure invariants, and linearizability [10]. The Intuition. Typical programs manipulate a large number of (instances of) data structures (possibly nested within other data structures). Each individual data structure can usually be in one of several different states (even in an abstract representation). This can lead to a combinatorial explosion in the number of distinct abstract states that can arise during abstract interpretation. The essential idea we pursue is that of decomposing the heap into multiple subheaps and abstracting away some correlations between the subheaps. Decomposition allows reusing subheaps that were decomposed from different heaps, thus representing a set of ⋆ This research was partially supported by the Clore Fellowship Programme. ⋆⋆ Supported by an Adams Fellowship through the Israel Academy of Sciences and Humanities. heaps more compactly (and more abstractly). For example, consider a program maintaining k disjoint lists. A powerset-based shape analysis such as the one in [20] uses a lattice whose height is exponential in k. An abstraction that ignores the correlations between the k lists reduces the lattice height to be linear in k, leading to exponentially faster analysis. (The savings come from not maintaining the correlations between different states of the different lists, which we observe are often irrelevant for a specific property of interest.) Similar situations arise in the kind of multithreaded programs discussed earlier, where the size of the state space is a function of the number of threads rather than the number of data structures. In this paper, we allow decomposing the heap into non-disjoint (i.e., overlapping) subheaps, which is important for handling programs with fine-grained concurrency (where different threads can simultaneously access the same objects) in a thread-modular way. Fine-Grained Concurrency. Fine-grained concurrent heap-manipulating programs allow multiple threads to use the same data structure simultaneously. They trade the simplicity of the single-thread-owning-a-data-structure model, which is at the heart of the coarse-grained concurrency approach, to achieve a higher degree of concurrency. However, the additional performance comes with a price: these programs are notoriously hard to develop and prove correct, even when the manipulated data structures are singlylinked lists (see, e.g., [5]). It is hard to employ thread-modular approaches that exploit locking [8] to analyze fine-grained concurrent programs because they have intentional (benign) dataraces. Thus, state-of-the-art shape analyses capable of verifying intricate properties of fine-grained concurrent heap-manipulating programs, e.g., linearizability (explained in Sec. 3), track all correlations between the states of all the threads [1]. This makes these analyses hard to scale. For example, the shape analysis in [1] handles at most 3 threads. It is interesting to observe, however, that it is often the case that although proving properties of these programs requires tracking sophisticated correlations between every thread and the part of the heap that it manipulates, the correlations between the states of different threads is often irrelevant. Intuitively, this is because fine-grained concurrent programs are often written in a way which attempts to ensure the correct operation of every thread regardless of the actions taken by other threads. This programming paradigm makes these programs an ideal match with our approach explained below. The Conceptual Framework. To permit the use of heap decomposition in several settings, we first present it as a parametric abstraction that can be tuned by the analysis designer in three ways: Decomposition: Specify along what lines a concrete heap should be decomposed into (possibly overlapping) subheaps. One of the strengths of the specification mechanism is that the decomposition of a heap depends on its properties. This allows us, for example, to decompose the state of a concurrent program based on the association between threads and data-structures in that state, which is usually not known a priori. Subheap abstraction: Create a bounded abstract heap representation from concrete subheaps (which are unbounded). Subheap abstractions can be obtained from existing whole-heap abstractions that satisfy certain properties.
doi:10.1007/978-3-540-69166-2_24 fatcat:soot4jzsg5gkhenewgj7esmnei