Using model checking with symbolic execution to verify parallel numerical programs

Siegel, Stephen F.; Mironova, Anastasia; Avrunin, George S.; Clarke, Lori A.

doi:10.1145/1146238.1146256

Cited by 47 publications

(29 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Siegel [38] uses the SPIN model checker and symbolic execution techniques to compare and verify correctness of the Message Passing parallel implementation of the algorithms given its sequential counterpart. Person [33] proposes differential symbolic execution to determine differences between two versions of the same application.…”

Section: Related Workmentioning

confidence: 99%

The nearest replica can be farther than you think

Bogdanov

Peón-Quirós

Maguire

et al. 2015

Proceedings of the Sixth ACM Symposium on Cloud Computing

View full text Add to dashboard Cite

Modern distributed systems are geo-distributed for reasons of increased performance, reliability, and survivability. At the heart of many such systems, e.g., the widely used Cassandra and MongoDB data stores, is an algorithm for choosing a closest set of replicas to service a client request. Suboptimal replica choices due to dynamically changing network conditions result in reduced performance as a result of increased response latency. We present GeoPerf, a tool that tries to automate the process of systematically testing the performance of replica selection algorithms for geodistributed storage systems. Our key idea is to combine symbolic execution and lightweight modeling to generate a set of inputs that can expose weaknesses in replica selection. As part of our evaluation, we analyzed network round trip times between geographically distributed Amazon EC2 regions, and showed a significant number of daily changes in nearest-K replica orders. We tested Cassandra and MongoDB using our tool, and found bugs in each of these systems. Finally, we use our collected Amazon EC2 latency traces to quantify the time lost due to these bugs. For example due to the bug in Cassandra, the median wasted time for 10% of all requests is above 50 ms.

show abstract

Section: Related Workmentioning

confidence: 99%

The nearest replica can be farther than you think

Bogdanov

Peón-Quirós

Maguire

et al. 2015

Proceedings of the Sixth ACM Symposium on Cloud Computing

View full text Add to dashboard Cite

show abstract

“…To make this general crosschecking approach effective to OpenCL code, we had to construct an OpenCL model, and add support for concurrency, race detection and several additional rules and assumptions. In addition to our work on KLEE-FP, this approach has been successfully used in the past to verify code equivalence in other contexts, such as hardware verification [4], compiler optimisations [17], block cipher implementations [23] and parallel numerical programs [22]. The main advantages of normalizing symbolic expressions and then comparing them syntactically are that (1) the technique is lightweight compared to more precise symbolic analyses such as [20], and (2) it can deal with floatingpoint constraints, for which there are no efficient constraint solvers currently available.…”

Section: Related Workmentioning

confidence: 99%

Symbolic Testing of OpenCL Code

Collingbourne

Cadar

Kelly

2012

Hardware and Software: Verification and Testing

View full text Add to dashboard Cite

Abstract. We present an effective technique for crosschecking a C or C++ program against an accelerated OpenCL version, as well as a technique for detecting data races in OpenCL programs. Our techniques are implemented in KLEE-CL, a symbolic execution engine based on KLEE and KLEE-FP that supports symbolic reasoning on the equivalence between symbolic values. Our approach is to symbolically model the OpenCL environment using an OpenCL runtime library targeted to symbolic execution. Using this model we are able to run OpenCL programs symbolically, keeping track of memory accesses for the purpose of race detection. We then compare the symbolic result against the plain C or C++ implementation in order to detect mismatches between the two versions. We applied KLEE-CL to the Parboil benchmark suite, the Bullet physics library and the OP2 library, in which we were able to find a total of seven errors: two mismatches between the OpenCL and C implementations, three memory errors, one OpenCL compiler bug and one race condition.

show abstract

“…-Program and Data Structure Repair can be done using symbolic execution; e.g., given an assertion that represents desired structural integrity constraints and a structure that violates them, the algorithm from [40] can "mutate" the given structure to satisfy the constraints. -Parallel numerical program analysis [55] involves combining model checking and symbolic execution to establish the equivalence of a sequential and a parallel program. The sequential program acts as the "specification" for the parallel one.…”

Section: Other Applicationsmentioning

confidence: 99%

A survey of new trends in symbolic execution for software testing and analysis

Păsăreanu

Visser

2009

Int J Softw Tools Technol Transfer

183

View full text Add to dashboard Cite

Symbolic execution is a well-known program analysis technique which represents program inputs with symbolic values instead of concrete, initialized, data and executes the program by manipulating program expressions involving the symbolic values. Symbolic execution has been proposed over three decades ago but recently it has found renewed interest in the research community, due in part to the progress in decision procedures, availability of powerful computers and new algorithmic developments. We provide here a survey of some of the new research trends in symbolic execution, with particular emphasis on applications to test generation and program analysis. We first describe an approach that handles complex programming constructs such as input recursive data structures, arrays, as well as multithreading. Furthermore, we describe recent hybrid techniques that combine concrete and symbolic execution to overcome some of the inherent limitations of symbolic execution, such as handling native code or availability of decision procedures for the application domain. We follow with a discussion of techniques that can be used to limit the (possibly infinite) number of symbolic configurations that need to be analyzed for the symbolic execution of looping programs. Finally, we give a short survey of interesting new applications, such as predictive testing, invariant inference,

show abstract

Using model checking with symbolic execution to verify parallel numerical programs

Cited by 47 publications

References 20 publications

The nearest replica can be farther than you think

The nearest replica can be farther than you think

Symbolic Testing of OpenCL Code

A survey of new trends in symbolic execution for software testing and analysis

Contact Info

Product

Resources

About