We have observed new workload patterns (namely ML training type) that cycle
through oversized allocations frequently, because 1) the dataset might be sparse
which is faster to go through, and 2) GPU accelerated. As a result, the eager
purging from the oversize arena becomes a bottleneck. To offer an easy
solution, allow normal purging of the oversized extents when background threads
are enabled.
