Virtualization

Effective Data-Race Detection for the Kernel

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Executive Summary

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-race 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 run-time 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 run-time overheads.

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