  A key part of implementing high-level languages is providing built-in and
default data structures. Yet selecting good defaults is hard. A mutable data
structure's workload is not known in advance, and it may shift over its
lifetime - e.g., between read-heavy and write-heavy, or from heavy contention
by multiple threads to single-threaded or low-frequency use. One idea is to
switch implementations adaptively, but it is nontrivial to switch the
implementation of a concurrent data structure at runtime. Performing the
transition requires a concurrent snapshot of data structure contents, which
normally demands special engineering in the data structure's design. However,
in this paper we identify and formalize an relevant property of lock-free
algorithms. Namely, lock-freedom is sufficient to guarantee that freezing
memory locations in an arbitrary order will result in a valid snapshot. Several
functional languages have data structures that freeze and thaw, transitioning
between mutable and immutable, such as Haskell vectors and Clojure transients,
but these enable only single-threaded writers. We generalize this approach to
augment an arbitrary lock-free data structure with the ability to gradually
freeze and optionally transition to a new representation. This augmentation
doesn't require changing the algorithm or code for the data structure, only
replacing its datatype for mutable references with a freezable variant. In this
paper, we present an algorithm for lifting plain to adaptive data and prove
that the resulting hybrid data structure is itself lock-free, linearizable, and
simulates the original. We also perform an empirical case study in the context
of heating up and cooling down concurrent maps.
