A general technique for non-blocking trees

Brown, Thomas J.; Ellen, Faith; Ruppert, Eric

doi:10.1145/2555243.2555267

Cited by 79 publications

(86 citation statements)

References 36 publications

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“…It traverses the data structure without any synchronization or retry. Many recent tree-based algorithms such as [2] share this property, and many of these algorithms can be verified with our method.…”

Section: Verification Targetmentioning

confidence: 64%

“…The shared data of the threads underlying the set algorithm is an external binary search tree composed of dynamic allocated nodes of two types, leaf and internal, which we refer to as heap. Each internal node contains three fields, two pointer fields child(1), child (2) pointing to its left and right children, and an integer field key storing the key of this node. Each leaf node contains only an integer field key.…”

Section: Verification Overviewmentioning

confidence: 99%

“…However, one can also perform rebalancing by addition and removal of nodes instead of in-place rotation. Performing a tree rotation is equivalent to performing the following procedure [2]: create two new nodes with key 6 and key 8, set their pointers to form the rotated subtree, and change the pointer of node with key −3 to the new node with key 6. In this way, the rebalancing procedure satisfies almost all the step and state invariants listed in Table 1, thus can be verified in this way.…”

Section: Balanced Binary Search Treesmentioning

confidence: 99%

“…Listing 2 is an example of balanced binary search tree implemented in [2]. We only list the Delete operation here for simplicity.…”

Section: Balanced Binary Search Treesmentioning

confidence: 99%

“…First, we briefly review the LLX and SCX primitives [2]. To simplify our proof, we assume that the primitives are atomic.…”

Section: Balanced Binary Search Treesmentioning

confidence: 99%

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Verifying Concurrent Data Structures Using Data-Expansion

Che

2015

Networked Systems

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We present the first thread modular proof of a highly concurrent binary search tree. This proof tackles the problem of reasoning about complicated thread interferences using only thread modular invariants. The key tool in this proof is the Data-Expansion Lemma, a novel lemma that allows us to reason about search operations in any given state. We highlight the power of this lemma when combined with our generalized version of the classical Hindsight Lemma, which enables us to prove linearizability by reasoning about the temporal properties of the operations instead of reasoning about the linearizability points directly.The Data-Expansion Lemma provides an interesting solution to the proof blowup problem when reasoning about concurrent data structures by separating the verification of effectful and effectless operations. We show that our proof methodology is widely applicable to several published algorithms and argue that many advanced highly concurrent data structures can be surprisingly easy to verify using thread-modular arguments.

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Section: Verification Targetmentioning

confidence: 64%

Section: Verification Overviewmentioning

confidence: 99%

Section: Balanced Binary Search Treesmentioning

confidence: 99%

“…Listing 2 is an example of balanced binary search tree implemented in [2]. We only list the Delete operation here for simplicity.…”

Section: Balanced Binary Search Treesmentioning

confidence: 99%

“…First, we briefly review the LLX and SCX primitives [2]. To simplify our proof, we assume that the primitives are atomic.…”

Section: Balanced Binary Search Treesmentioning

confidence: 99%

See 3 more Smart Citations

Verifying Concurrent Data Structures Using Data-Expansion

Che

2015

Networked Systems

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RCU‐HTM: A generic synchronization technique for highly efficient concurrent search trees

Siakavaras

Nikas

Goumas

et al. 2021

Concurrency and Computation

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Concurrent search trees (STs) are among the most widely used data structures to store and retrieve data in contemporary multithreaded applications. Despite the high amount of prior work, it still remains challenging to implement highly efficient concurrent STs. This is mainly due to the fact that both traditional synchronization methods (i.e., locks and atomic operations) and more novel ones (i.e., read‐copy‐update and transactional memory) fail to provide solutions that are generic and at the same time able to attain high performance under diverse workloads and contention levels. In this work, we present RCU‐HTM, a technique that combines read‐copy‐update (RCU) and hardware transactional memory (HTM), and: (a) supports the implementation of a concurrent version of any type of search tree, and (b) achieves high performance across all execution scenarios. We also incorporate a low‐overhead, epoch‐based memory reclamation scheme to make our RCU‐HTM trees practical for large‐scale long‐running applications. To showcase the capabilities of our technique, we implement and evaluate multiple RCU‐HTM trees and compare their performance with several state‐of‐the‐art competitors. More specifically, we apply RCU‐HTM to 12 different types of binary, B+ and (a,b)‐trees and compare against 18 state‐of‐the‐art implementations that use four different synchronization mechanisms, namely, locks, atomic operations, RCU, and HTM. We evaluate the trees under different levels of contention by varying the size of the tree, the operations mix, and the number of threads, for a total of 210 execution scenarios for each implementation. Our evaluation shows that in the majority of executions, RCU‐HTM trees outperform their state‐of‐the‐art alternatives, and even in the cases where they do not, their performance is very close to that of the best implementation.

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