  Writing reliable concurrent software remains a huge challenge for today's
programmers. Programmers rarely reason about their code by explicitly
considering different possible inter-leavings of its execution. We consider the
problem of detecting data races from individual executions in a sound manner.
The classical approach to solving this problem has been to use Lamport's
happens-before (HB) relation. Until now HB remains the only approach that runs
in linear time. Previous efforts in improving over HB such as causally-precedes
(CP) and maximal causal models fall short due to the fact that they are not
implementable efficiently and hence have to compromise on their race detecting
ability by limiting their techniques to bounded sized fragments of the
execution. We present a new relation weak-causally-precedes (WCP) that is
provably better than CP in terms of being able to detect more races, while
still remaining sound. Moreover it admits a linear time algorithm which works
on the entire execution without having to fragment it.
