POPL '95 Proceedings of the 22nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Eraser: a dynamic data race detector for multi-threaded programs
Proceedings of the sixteenth ACM symposium on Operating systems principles
Type-based race detection for Java
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
Automatic Deductive Verification with Invisible Invariants
TACAS 2001 Proceedings of the 7th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Liveness with (0, 1, infty)-Counter Abstraction
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Dynamic partial-order reduction for model checking software
Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Efficient Stateful Dynamic Partial Order Reduction
SPIN '08 Proceedings of the 15th international workshop on Model Checking Software
Efficient compilation of fine-grained SPMD-threaded programs for multicore CPUs
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Proving ptolemy right: the environment abstraction framework for model checking concurrent systems
TACAS'08/ETAPS'08 Proceedings of the Theory and practice of software, 14th international conference on Tools and algorithms for the construction and analysis of systems
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
Scalable SMT-based verification of GPU kernel functions
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
GRace: a low-overhead mechanism for detecting data races in GPU programs
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Symbolic crosschecking of floating-point and SIMD code
Proceedings of the sixth conference on Computer systems
GKLEE: concolic verification and test generation for GPUs
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Verifying GPU kernels by test amplification
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Parameterized Verification of GPU Kernel Programs
IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
GPUVerify: a verifier for GPU kernels
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Symbolic testing of OpenCL code
HVC'11 Proceedings of the 7th international Haifa Verification conference on Hardware and Software: verification and testing
GPUVerify: a verifier for GPU kernels
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Barrier invariants: a shared state abstraction for the analysis of data-dependent GPU kernels
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
Efficient search for inputs causing high floating-point errors
Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming
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The growing scale of concurrency requires automated abstraction techniques to cut down the effort in concurrent system analysis. In this paper, we show that the high degree of behavioral symmetry present in GPU programs allows CUDA race detection to be dramatically simplified through abstraction. Our abstraction techniques is one of automatically creating parametric flows---control-flow equivalence classes of threads that diverge in the same manner---and checking for data races only across a pair of threads per parametric flow. We have implemented this approach as an extension of our recently proposed GKLEE symbolic analysis framework and show that all our previous results are dramatically improved in that (i) the parametric flow-based analysis takes far less time, and (ii) because of the much higher scalability of the analysis, we can detect even more data race situations that were previously missed by GKLEE because it was forced to downscale examples to limit analysis complexity. Moreover, the parametric flow-based analysis is applicable to other programs with SPMD models.