Deletion Without Rebalancing in Balanced Binary Trees

Sen, Siddhartha; Tarjan, Robert E.

doi:10.1137/1.9781611973075.121

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Balanced binary trees are a common choice for implementations of ordered sets and mappings in standard libraries, as well as industrial databases, due to predictable performance and scalability. Since deletions, typically the most computationally difficult task in a balanced binary tree [26], are never performed in our implementation, such trees are a natural choice of data structure.…”

Section: Implementation Detailsmentioning

confidence: 99%

A Meet-in-the-Middle Algorithm for Fast Synthesis of Depth-Optimal Quantum Circuits

Amy

Maslov²,

Mosca

et al. 2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

448

414

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We present an algorithm for computing depthoptimal decompositions of logical operations, leveraging a meetin-the-middle technique to provide a significant speedup over simple brute force algorithms. As an illustration of our method, we implemented this algorithm and found factorizations of commonly used quantum logical operations into elementary gates in the Clifford+T set. In particular, we report a decomposition of the Toffoli gate over the set of Clifford and T gates. Our decomposition achieves a total T -depth of 3, thereby providing a 40% reduction over the previously best known decomposition for the Toffoli gate. Due to the size of the search space, the algorithm is only practical for small parameters, such as the number of qubits, and the number of gates in an optimal implementation. Index Terms-Brute force search, meet-in-the-middle, quantum circuit optimization, quantum circuit synthesis.

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Section: Implementation Detailsmentioning

confidence: 99%

A Meet-in-the-Middle Algorithm for Fast Synthesis of Depth-Optimal Quantum Circuits

Amy

Maslov²,

Mosca

et al. 2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

448

414

View full text Add to dashboard Cite

show abstract

“…This process, like split, continues up the tree as necessary. Though remove in classical B + -trees can redistribute keys among nodes to preserve balance, removal without rebalancing has theoretical and practical advantages [33].…”

Section: Nonconcurrent Modificationmentioning

confidence: 99%

Cache craftiness for fast multicore key-value storage

Mao

Kohler

Morris

2012

Proceedings of the 7th ACM European Conference on Computer Systems

364

179

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We present Masstree, a fast key-value database designed for SMP machines. Masstree keeps all data in memory. Its main data structure is a trie-like concatenation of B + -trees, each of which handles a fixed-length slice of a variable-length key. This structure effectively handles arbitrary-length possiblybinary keys, including keys with long shared prefixes. B + -tree fanout was chosen to minimize total DRAM delay when descending the tree and prefetching each tree node. Lookups use optimistic concurrency control, a read-copy-update-like technique, and do not write shared data structures; updates lock only affected nodes. Logging and checkpointing provide consistency and durability. Though some of these ideas appear elsewhere, Masstree is the first to combine them. We discuss design variants and their consequences.On a 16-core machine, with logging enabled and queries arriving over a network, Masstree executes more than six million simple queries per second. This performance is comparable to that of memcached, a non-persistent hash table server, and higher (often much higher) than that of VoltDB, MongoDB, and Redis.

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“…Third, like other systems [28], we do not rebalance the B + -tree upon deletion, which may result in a larger RTM region. There has been both theoretical and empirical work showing deletion without rebalancing works well in practice [34]. Unordered store.…”

Section: Shared Memory Storementioning

confidence: 99%

Using restricted transactional memory to build a scalable in-memory database

Wang

Hao

et al. 2014

Proceedings of the Ninth European Conference on Computer Systems

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The recent availability of Intel Haswell processors marks the transition of hardware transactional memory from research toys to mainstream reality. DBX is an in-memory database that uses Intel's restricted transactional memory (RTM) to achieve high performance and good scalability across multicore machines. The main limitation (and also key to practicality) of RTM is its constrained working set size: an RTM region that reads or writes too much data will always be aborted. The design of DBX addresses this challenge in several ways. First, DBX builds a database transaction layer on top of an underlying shared-memory store. The two layers use separate RTM regions to synchronize shared memory access. Second, DBX uses optimistic concurrency control to separate transaction execution from its commit. Only the commit stage uses RTM for synchronization. As a result, the working set of the RTMs used scales with the meta-data of reads and writes in a database transaction as opposed to the amount of data read/written. Our evaluation using TPC-C workload mix shows that DBX achieves 506,817 transactions per second on a 4-core machine.

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Deletion Without Rebalancing in Balanced Binary Trees

Cited by 8 publications

References 9 publications

A Meet-in-the-Middle Algorithm for Fast Synthesis of Depth-Optimal Quantum Circuits

A Meet-in-the-Middle Algorithm for Fast Synthesis of Depth-Optimal Quantum Circuits

Cache craftiness for fast multicore key-value storage

Using restricted transactional memory to build a scalable in-memory database

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