Skip to content

Latest commit

 

History

History
83 lines (47 loc) · 5.03 KB

README.md

File metadata and controls

83 lines (47 loc) · 5.03 KB
Raw

GreenBST: Energy-efficient concurrent search tree

This repository contains the source code and the benchmark framework from the below article:

Phuong H. Ha, Otto J. Anshus, Ibrahim Umar. Efficient concurrent search trees using portable fine-grained locality. IEEE Transactions on Parallel and Distributed Systems, 30(7): 1580-1595, 2019. IEEE. (link)

Ibrahim Umar, Otto J. Anshus, and Phuong H. Ha. GreenBST: An energy-efficient concurrent search tree. Proceedings of the 22nd International European Conference on Parallel and Distributed Computing (Euro-Par ’16), 2016, LNCS, pp. 502-517, Springer.(link)

License

  • GreenBST, the benchmark routines, and the building frameworks is developed by UiT and licensed under the Apache License, Version 2.0.

  • CBTree and SVEB are developed by UiT based on the others’ work. They are licensed under the The GNU General Public License v3.0.

  • Other trees are developed by their respective authors and retain their original licenses

To run the benchmark:

  1. Please make sure you have the Intel PCM library and PAPI library installed.
  2. Edit the common/common.mk to modify the the Intel PCM and PAPI libraries location.
  3. Go to bench/ directory.
  4. Run make-bins.sh. This should create the trees' binaries.
  5. Run runtest.{ARCH}.sh to run the benchmark.
  6. You will find the benchmark results (in CSV format) inside the bench/results/ directory

The trees

1. GreenBST

GreenBST is a locality-aware and energy-efficient concurrent search tree. GreenBST portable, namely it can maintain their locality-awareness and energy-efficiency on different computing platforms (platform-independent).

2. Concurrent B-tree (CBTree)

CBTree is a prominent locality-aware concurrent B+tree. CBTree is a representation of the classic coarse-grained locality-aware search concurrent trees that are usually platform-dependent. CBTree can only perform well if their node size is set correctly (e.g., to the system’s page size). This tree is also often referred as the B-link tree.

  • Philip L. Lehman and s. Bing Yao. 1981. Efficient locking for concurrent operations on B-trees. ACM Trans. Database Syst. 6, 4 (December 1981), 650-670.

3. Lock-based (SVEB) dynamic cache-oblivious tree

SVEB is the concurrent implementation of the fine-grained locality-aware vEB binary search tree. SVEB uses a global mutex to serialize its concurrent tree operations.

  • Gerth Stølting Brodal, Rolf Fagerberg, and Riko Jacob. Cache oblivious search trees via binary trees of small height. In Proc. 13th ACM-SIAM Symp. Discrete algorithms, SODA ’02, pages 39–48, 2002.

4. Non-blocking binary search tree (LFBST)

LFBST is the improved variant of the original non-blocking binary search tree. These non-blocking search trees are locality-oblivious.

LFBST official repository: https://github.com/anataraja/lfbst

  • Aravind Natarajan and Neeraj Mittal. Fast concurrent lock-free binary search trees. In Proc. 19th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’14, pages 317–328, 2014.

5. RCU-based concurrent search tree (Citrus)

Citrus is a concurrent binary search tree that utilizes Read-Copy-Update (RCU) synchronization and fine-grained locking for synchronization among updaters. Citrus contain operation is wait-free. This concurrent search tree is locality-oblivious.

Citrus official repository: https://bitbucket.org/mayaarl/citrus

  • Maya Arbel and Hagit Attiya. Concurrent updates with rcu: Search tree as an example. In Proc. 2014 ACM Symposium on Principles of Distributed Computing, PODC ’14, pages 196–205. ACM, 2014.

6. Portably scalable concurrent search tree (BSTTK)

BST-TK is the state-of- the-art lock-based concurrent search tree based on the asynchronous concurrency paradigm. BST-TK is portably scalable, namely it scales across different types of hardware platforms. BSTTK is a locality-oblivious tree.

BSTTK official repository: https://github.com/LPD-EPFL/ASCYLIB

  • Tudor David, Rachid Guerraoui, and Vasileios Trigonakis. Asynchronized concurrency: The secret to scaling concurrent search data structures. In Proc. 12th Intl. Conf. on Architectural Support for Programming Languages and Operating Systems, ASPLOS’15, pages 631–644, 2015.

7. Lock-free ABTREE

ABTREE official repository: https://bitbucket.org/trbot86/setbench/src/master/ds/brown_ext_abtree_lf

  • Trevor Brown. A Template for Implementing Fast Lock-free Trees Using HTM. In Proceedings of the ACM Symposium on Principles of Distributed Computing (PODC '17). ACM, New York, NY, USA, 293-302, 2017.

8. Lock-free BWTREE

BWTREE official repository: https://github.com/wangziqi2013/BwTree/tree/master/src

  • Ziqi Wang, Andrew Pavlo, Hyeontaek Lim, Viktor Leis, Huanchen Zhang, Michael Kaminsky, and David G. Andersen. Building a Bw-Tree Takes More Than Just Buzz Words. In Proceedings of the 2018 International Conference on Management of Data (SIGMOD '18). ACM, New York, NY, USA, 473-488, 2018.