Title: Gang-GC: Locality-aware Parallel Data Placement Optimizations for Key-Value Storages,
Abstract: Many cloud applications rely on fast and non-relational storage to aid in the
processing of large amounts of data. Managed runtimes are now widely used to
support the execution of several storage solutions of the NoSQL movement,
particularly when dealing with big data key-value store-driven applications.
The benefits of these runtimes can however be limited by modern parallel
throughput-oriented GC algorithms, where related objects have the potential to
be dispersed in memory, either in the same or different generations. In the
long run this causes more page faults and degradation of locality on
system-level memory caches.
We propose, Gang-CG, an extension to modern heap layouts and to a parallel GC
algorithm to promote locality between groups of related objects. This is done
without extensive profiling of the applications and in a way that is
transparent to the programmer, without the need to use specialized data
structures. The heap layout and algorithmic extensions were implemented over
the Parallel Scavenge garbage collector of the HotSpot JVM\@.
Using microbenchmarks that capture the architecture of several key-value
stores databases, we show negligible overhead in frequent operations such as
the allocation of new objects and improvements to the access speed of data,
supported by lower misses in system-level memory caches. Overall, we show a 6\%
improvement in the average time of read and update operations and an average
decrease of 12.4\% in page faults.