Accurate Data Race Prediction in the Linux Kernel through Sparse Fourier Learning

Abstract

Testing for data races in the Linux OS kernel is challenging because there is an exponentially large space of system calls and thread interleavings that can potentially lead to concurrent executions with races. In this work, we introduce a new approach for modeling execution trace feasibility and apply it to Linux OS Kernel race prediction. To address the fundamental scalability challenge posed by the exponentially large domain of possible execution traces, we decompose the task of predicting trace feasibility into independent prediction subtasks encoded as learning Boolean indicator functions for specific memory accesses, and apply a sparse fourier learning approach to learning each feasibility subtask. Boolean functions that are sparse in their fourier domain can be efficiently learned by estimating the coefficients of their fourier expansion. Since the feasibility of each memory access depends on only a few other relevant memory accesses or system calls (e.g., relevant inter-thread communications), we observe that trace feasibility functions often have this sparsity property and can be learned efficiently. We use learned trace feasibility functions in conjunction with conservative alias analysis to implement a kernel race-testing system, HBFourier, that uses sparse fourier learning to efficiently model feasibility when making predictions. We evaluate our approach on a recent Linux development kernel and show it finds 44 more races with 15.7% more accurate race predictions than the next best performing system in our evaluation, in addition to identifying 5 new race bugs confirmed by kernel developers.