29,019 research outputs found
Fast and Precise Symbolic Analysis of Concurrency Bugs in Device Drivers
© 2015 IEEE.Concurrency errors, such as data races, make device drivers notoriously hard to develop and debug without automated tool support. We present Whoop, a new automated approach that statically analyzes drivers for data races. Whoop is empowered by symbolic pairwise lockset analysis, a novel analysis that can soundly detect all potential races in a driver. Our analysis avoids reasoning about thread interleavings and thus scales well. Exploiting the race-freedom guarantees provided by Whoop, we achieve a sound partial-order reduction that significantly accelerates Corral, an industrial-strength bug-finder for concurrent programs. Using the combination of Whoop and Corral, we analyzed 16 drivers from the Linux 4.0 kernel, achieving 1.5 - 20× speedups over standalone Corral
SmartTrack: Efficient Predictive Race Detection
Widely used data race detectors, including the state-of-the-art FastTrack
algorithm, incur performance costs that are acceptable for regular in-house
testing, but miss races detectable from the analyzed execution. Predictive
analyses detect more data races in an analyzed execution than FastTrack
detects, but at significantly higher performance cost.
This paper presents SmartTrack, an algorithm that optimizes predictive race
detection analyses, including two analyses from prior work and a new analysis
introduced in this paper. SmartTrack's algorithm incorporates two main
optimizations: (1) epoch and ownership optimizations from prior work, applied
to predictive analysis for the first time; and (2) novel conflicting critical
section optimizations introduced by this paper. Our evaluation shows that
SmartTrack achieves performance competitive with FastTrack-a qualitative
improvement in the state of the art for data race detection.Comment: Extended arXiv version of PLDI 2020 paper (adds Appendices A-E) #228
SmartTrack: Efficient Predictive Race Detectio
Modular Verification of Interrupt-Driven Software
Interrupts have been widely used in safety-critical computer systems to
handle outside stimuli and interact with the hardware, but reasoning about
interrupt-driven software remains a difficult task. Although a number of static
verification techniques have been proposed for interrupt-driven software, they
often rely on constructing a monolithic verification model. Furthermore, they
do not precisely capture the complete execution semantics of interrupts such as
nested invocations of interrupt handlers. To overcome these limitations, we
propose an abstract interpretation framework for static verification of
interrupt-driven software that first analyzes each interrupt handler in
isolation as if it were a sequential program, and then propagates the result to
other interrupt handlers. This iterative process continues until results from
all interrupt handlers reach a fixed point. Since our method never constructs
the global model, it avoids the up-front blowup in model construction that
hampers existing, non-modular, verification techniques. We have evaluated our
method on 35 interrupt-driven applications with a total of 22,541 lines of
code. Our results show the method is able to quickly and more accurately
analyze the behavior of interrupts.Comment: preprint of the ASE 2017 pape
GPUVerify: A Verifier for GPU Kernels
We present a technique for verifying race- and divergence-freedom of GPU kernels that are written in mainstream ker-nel programming languages such as OpenCL and CUDA. Our approach is founded on a novel formal operational se-mantics for GPU programming termed synchronous, delayed visibility (SDV) semantics. The SDV semantics provides a precise definition of barrier divergence in GPU kernels and allows kernel verification to be reduced to analysis of a sequential program, thereby completely avoiding the need to reason about thread interleavings, and allowing existing modular techniques for program verification to be leveraged. We describe an efficient encoding for data race detection and propose a method for automatically inferring loop invari-ants required for verification. We have implemented these techniques as a practical verification tool, GPUVerify, which can be applied directly to OpenCL and CUDA source code. We evaluate GPUVerify with respect to a set of 163 kernels drawn from public and commercial sources. Our evaluation demonstrates that GPUVerify is capable of efficient, auto-matic verification of a large number of real-world kernels
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