Exact and approximate membership testers

Carter, Larry; Floyd, Robert W.; Gill, John; Markowsky, George; Wegman, Mark N.

doi:10.1145/800133.804332

Cited by 99 publications

(85 citation statements)

References 9 publications

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“…We choose a hash table based scheme because similar approximate membership testers have proven more space-efficient than Bloom filters [6,19]. Cuckoo hashing supports a fast lookup via two (parallel) probes, but complicates inserts.…”

Section: Cuckoo-bloom Signaturesmentioning

confidence: 99%

“…Counting Bloom filters allow deletions and can represent multisets [10]. Hashtable-based alternatives similar to Bloom filters have been proven to be more space-efficient than Bloom filters themselves [6,19]. Bloom filters have been used in computer architecture for purposes other than conflict detection (e.g., for load-store queues [26] and early miss detection in L2 caches [11]).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Implementing Signatures for Transactional Memory

Sánchez¹,

Yen²,

Hill³

et al. 2007

40th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO 2007)

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Transactional Memory (TM) systems must track the read and write sets-items read and written during a transaction-to detect conflicts among concurrent transactions. Several TMs use signatures, which summarize unbounded read/write sets in bounded hardware at a performance cost of false positives (conflicts detected when none exists).This paper examines different organizations to achieve hardware-efficient and accurate TM signatures. First, we find that implementing each signature with a single k-hashfunction Bloom filter (True Bloom signature) is inefficient, as it requires multi-ported SRAMs. Instead, we advocate using k single-hash-function Bloom filters in parallel (Parallel Bloom signature), using area-efficient single-ported SRAMs. Our formal analysis shows that both organizations perform equally well in theory and our simulationbased evaluation shows this to hold approximately in practice. We also show that by choosing high-quality hash functions we can achieve signature designs noticeably more accurate than the previously proposed implementations. Finally, we adapt Pagh and Rodler's cuckoo hashing to implement Cuckoo-Bloom signatures. While this representation does not support set intersection, it mitigates false positives for the common case of small read/write sets and performs like a Bloom filter for large sets.

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Section: Cuckoo-bloom Signaturesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Implementing Signatures for Transactional Memory

Sánchez¹,

Yen²,

Hill³

et al. 2007

40th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO 2007)

View full text Add to dashboard Cite

show abstract

“…This can be rectified by using a randomization function (1:1 hash), but in this paper, we will use the Cleary table exclusively to store hashes, which are already uniformly distributed. We are using an exact structure to implement an inexact one, a fine and well-understood approach [8,11].…”

Section: Cleary Tablesmentioning

confidence: 99%

Fast, All-Purpose State Storage

Dillinger

Manolios

2009

Lecture Notes in Computer Science

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Abstract. Existing techniques for approximate storage of visited states in a model checker are too special-purpose and too DRAM-intensive. Bitstate hashing, based on Bloom filters, is good for exploring most of very large state spaces, and hash compaction is good for high-assurance verification of more tractable problems. We describe a scheme that is good at both, because it adapts at run time to the number of states visited. It does this within a fixed memory space and with remarkable speed and accuracy. In many cases, it is faster than existing techniques, because it only ever requires one random access to main memory per operation; existing techniques require several to have good accuracy. Adapting to accomodate more states happens in place using streaming access to memory; traditional rehashing would require extra space, random memory accesses, and hash computation. The structure can also incorporate search stack matching for partial-order reductions, saving the need for extra resources dedicated to an additional structure. Our scheme is well-suited for a future in which random accesses to memory are more of a limiting factor than the size of memory.

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“…Bloom filter is a simple and space-efficient probabilistic data structure that belongs to the class of approximate membership testers as given in [6]. It is used to represent a set with much less space requirement than directly representing the entire whole set.…”

Section: Related Workmentioning

confidence: 99%

“…Each monitored node first estimates the required Bloom filter sequence such that the communication cost can be minimized while the searching threshold can be satisfied (line [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Note that this estimation is based on the fact that signature hashing will be applied.…”

Section: Flow Of H 2 CMmentioning

confidence: 99%