Parallel Synchronization-Free Approximate Data Structure Construction

We present approximate data structures with construction algorithms that execute without synchronization. The data races present in these algorithms may cause them to drop inserted or appended elements. Nevertheless, the algorithms 1) do not crash and 2) may produce a data structure that is accurate enough for its clients to use successfully. We advocate an approach in which the approximate data structures are composed of basic tree and array building blocks with associated synchronization-free construction algorithms. This approach enables developers to reuse the construction algorithms, which have been engineered to execute successfully in parallel contexts despite the presence of data races, without having to understand the details of why they execute successfully.

We evaluate the end-to-end accuracy and performance consequences of our approach by building a space-subdivision tree for the Barnes-Hut N-body simulation out of our presented tree and array building blocks. The resulting approximate data structure construction algorithm eliminates synchronization overhead and anomalies such as excessive serialization and deadlock. The algorithm exhibits good performance (running 14 times faster on 16 cores than the sequential version) and good accuracy (the accuracy loss is four orders of magnitude less than the accuracy gain associated with increasing the accuracy of the Barnes-Hut center of mass approximation by 20%).