Scalable Verification Techniques for Data-Parallel Programs

Nathan Yong Seng Chong
Department of Computing, Imperial College London
Imperial College London, 2014


   title={Scalable Verification Techniques for Data-Parallel Programs},

   author={Chong, Nathan Yong Seng},



This thesis is about scalable formal verification techniques for software. A verification technique is scalable if it is able to scale to reasoning about real (rather than synthetic or toy) programs. Scalable verification techniques are essential for practical program verifiers. In this work, we consider three key characteristics of scalability: precision, performance and automation. We explore trade-offs between these factors by developing verification techniques in the context of data-parallel programs, as exemplified by graphics processing unit (GPU) programs (called kernels). This thesis makes three original contributions to the field of program verification: a) An empirical study of candidate-based invariant generation that explores the tradeoffs between precision and performance. An invariant is a property that captures program behaviours by expressing a fact that always holds at a particular program point. The generation of invariants is critical for automatic and precise verification. Over a benchmark suite comprising 356 GPU kernels, we find that candidate-based invariant generation allows precise reasoning for 256 (72%) kernels. b) Barrier invariants: a new abstraction for precise and scalable reasoning about data-dependent GPU kernels, an important class of kernel beyond the scope of existing techniques. Our evaluation shows that barrier invariants enable us to capture a functional specification for three distinct prefix sum implementations for problem sizes using hundreds of threads and race-freedom for a real-world stream compaction example. c) The interval of summations: a new abstraction for precise and scalable reasoning for parallel prefix sums, an important data-parallel primitive. We give theoretical results showing that the interval of summations is, surprisingly, both sound and complete. That is, all correct prefix sums can be precisely captured by this abstraction. Our evaluation shows that the interval of summations allow us to automatically prove full functional correctness of four distinct prefix sum implementations for all power-of-two problem sizes up to 2^20.
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