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Effective Data-Race Detection for the Kernel

John Erickson, Madanlal Musuvathi, sebastian burckhardt, and kirk olynyk

Abstract

Data races are an important class of concurrency errors where two threads erroneously access a shared memory location without appropriate synchronization. This paper presents DataCollider, a lightweight and effective technique for dynamically detecting data races in kernel modules. Unlike existing data-race detection techniques, DataCollider is oblivious to the synchronization protocols (such as locking disciplines) the program uses to protect shared memory accesses. This is particularly important for low-level kernel code that uses a myriad of complex architecture/device specific synchronization mechanisms. To reduce the runtime overhead, DataCollider randomly samples a small percentage of memory accesses as candidates for data-race detection. The key novelty of DataCollider is that it uses breakpoint facilities already supported by many hardware architectures to achieve negligible runtime overheads. We have implemented DataCollider for the Windows 7 kernel and have found 25 confirmed erroneous data races of which 12 have already been fixed.

Details

Publication typeInproceedings
Published inOperating System Design and Implementation (OSDI'10)
PublisherUSENIX
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