Dataflow analysis for concurrent programs using datarace detection

Abstract

Dataflow analyses for concurrent programs differ from their singlethreaded counterparts in that they must account for shared memory locations being overwritten by concurrent threads. Existing dataflow analysis techniques for concurrent programs typically fall at either end of a spectrum: at one end, the analysis conservatively kills facts about all data that might possibly be shared by multiple threads; at the other end, a precise thread-interleaving analysis determines which data may be shared, and thus which dataflow facts must be invalidated. The former approach can suffer from imprecision, whereas the latter does not scale.We present RADAR, a framework that automatically converts a dataflow analysis for sequential programs into one that is correct for concurrent programs. RADAR uses a race detection engine to kill the dataflow facts, generated and propagated by the sequential analysis, that become invalid due to concurrent writes. Our approach of factoring all reasoning about concurrency into a race detection engine yields two benefits. First, to obtain analyses for code using new concurrency constructs, one need only design a suitable race detection engine for the constructs. Second, it gives analysis designers an easy way to tune the scalability and precision of the overall analysis by only modifying the race detection engine. We describe the RADAR framework and its implementation using a preexisting race detection engine. We show how RADAR was used to generate a concurrent version of a null-pointer dereference analysis, and we analyze the result of running the generated concurrent analysis on several benchmarks.

DEICS: Data Erasure in Concurrent Software

“…In particular, we use and build on the RADAR [19] framework for concurrent program analysis. RADAR is a data-flow analysis framework which converts a sequential analysis into the one that is sound for concurrent programs.…”

“…In addition, we handle concurrent programs which is not handled by prior work. Static Analysis of Concurrent Applications: There are tools and frameworks developed to perform dataflow analysis [20,19]. In [32,22,39,20], a graph to represent the parallelism is built and a modified version of sequential analysis is performed.…”

“…The main contribution of this paper is to address the abovementioned challenge by introducing a formal notion of RacyPairs, which assists in determining whether a given erasure for a shared object is safe or not. In addition, we give an algorithm to effectively compute RacyPairs, by leveraging existing work [19] on concurrent dataflow analysis using a race-detection engine. Our approach is implemented in a tool called DEICS, which transforms given concurrent C program into an equivalent C program with reduced lifetime of sensitive data used in the program.…”

Soundness of Data Flow Analyses for Weak Memory Models

“…With the re-Analysis Soundness w.r.t. weak memory models Knoop et al [33] yes (separable) Chugh et al [34] yes (if no datarace) Steensgaard [35] yes (flow-insensitive) Miné [16] yes Rugina and Rinard [24] yes Jeannet [14] no Ferrara [15] yes on Java Memory Model Farzan and Kincaid [25] yes (separable) Khedker and Dhamdhere [36] separable: yes; non-separable: not in general Constant propagation [32] yes (non-relational) Fig. 6.…”

Semantic Reduction of Thread Interleavings in Concurrent Programs

“…There are also other recent efforts to leverage sequential analysis in concurrent settings [4,20]. Our approach also exploits specific patterns of synchronization, but our main focus is on deriving sound invariants for reduction in thread interleavings, by lifting abstract interpretation techniques to the concurrency setting.…”