Uses Angluin-style learning to synthesize a model of hardware cache eviction policies. Models are readable. Evaluated on a simulator to test what policies it can learn and also on x86 cores to learn L1, L2 and L3 policies and found two new eviction policies.
On the L3 cache that has adaptive policies (i.e., a follower policy is used to select between multiple leader policies), it only learns
Built as a set of components, that solve different parts of the problem.
POLCA is the learning algorithm. Built using the off-the-shelf “LearnLib” tool. The classic algorithm requires an equivalence test that is not available in hardware but can be approximated using m-complete test suites (that can detect differences between policies with up to m control states) and then further approximating to avoid an exponential blowup.
MemBlockLang is a language for describing queries consisting of blocks to be profiled and blocks to be invalidated using clflush.
CacheQuery’s frontend is a python script that can run as an interactive REPL or in batch mode. This is sometimes (always?) run on a separate machine to reduce noise and(?) increase performance.
CacheQuery’s backend is a Linux Kernel Module that runs queries in a low-noise environment using performance/cycle counters, handling V-to-P translations, evicting caches above the one being profiled, turns off prefetching, hyper-threads, DVFS, other cores, collisions between instructions and data, etc.
Explainable policies are created using syntax-guided synthesis to fill holes in templates for four separate operations: promote, evict, insert and normalize. The templates and the grammars used for synthesis limit the policies that can be learned (in particular, excluding states with global control state such as PLRU).
Explainable policies are easier to compare against known policies to find new policies.
Runtimes on real hardware vary from 10s of seconds to several days.