GreenBST: Energy-Efficient Concurrent Search Tree

Abstract

Abstract. Like other fundamental abstractions for energy-efficient computing, search trees need to support both high concurrency and finegrained data locality. However, existing locality-aware search trees such as ones based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations while existing highly-concurrent search trees such as the non-blocking binary search trees do not consider data locality. We present GreenBST, a practical energy-efficient concurrent search tree that supports fine-grained data locality as vEB-based trees do, but unlike vEB-based trees, GreenBST supports high concurrency. GreenBST is a k-ary leaf-oriented tree of GNodes where each GNode is a fixed size tree-container with the van Emde Boas layout. As a result, GreenBST minimizes data transfer between memory levels while supporting highly concurrent (update) operations. Our experimental evaluation using the recent implementation of non-blocking binary search trees, highly concurrent B-trees, conventional vEB trees, as well as the portably scalable concurrent trees shows that GreenBST is efficient: its energy efficiency (in operations/Joule) and throughput (in operations/second) are up to 65% and 69% higher, respectively, than the other trees on a high performance computing (HPC) platform (Intel Xeon), an embedded platform (ARM), and an accelerator platform (Intel Xeon Phi). The results also provide insights into how to develop energy-efficient data structures in general.

Efficient Concurrent Search Trees Using Portable Fine-Grained Locality

“…or split operations. The other optimization techniques such as incremental rebalancing (see Section 5.3) significantly reduce GreenBST maintenance overhead (e.g., rebalancing overhead) (see Section 3 in [42] for performance comparison between GreenBST and its variation without the optimization called DeltaTree).…”