Cache-Oblivious Algorithms

Frigo, Matteo; Leiserson, Charles E.; Prokop, Harald; Ramachandran, Sridhar

doi:10.1145/2071379.2071383

Cited by 144 publications

(141 citation statements)

References 25 publications

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“…Hence, this solution improves the time complexity of the previous approach but retains the same important drawback. We conclude by mentioning a solution [13] that solves the weak prefix search problem efficiently in the Cache-Oblivious Model [14], and, thus, makes the above approach suitable for this model. Applying this solution we can search for a pattern P [1, p] in O(log B n + ( p log σ )/B) I/Os in the CacheOblivious Model by using O n log n bits of space, where B is the number of bits that fit in a block.…”

Section: Solutions For the Substring Occurrence Estimation Problemmentioning

confidence: 92%

Space-Efficient Substring Occurrence Estimation

Orlandi¹,

Venturini

2014

Algorithmica

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In this paper we study the problem of estimating the number of occurrences of substrings in textual data: A text T on some alphabet Σ=[σ] of length n is preprocessed and an index I is built. The index is used in lieu of the text to answer queries of the form Count≈(P), returning an approximated number of the occurrences of an arbitrary pattern $$P$$P as a substring of T. The problem has its main application in selectivity estimation related to the LIKE predicate in textual databases. Our focus is on obtaining an algorithmic solution with guaranteed error rates and small footprint. To achieve that, we first enrich previous work in the area of compressed text-indexing providing an optimal data structure that, for a given additive error ℓ≥1, requires Θnℓlogσ bits. We also approach the issue of guaranteeing exact answers for sufficiently frequent patterns, providing a data structure whose size scales with the amount of such patterns. Our theoretical findings are supported by experiments showing the practical impact of our data structures

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Section: Solutions For the Substring Occurrence Estimation Problemmentioning

confidence: 92%

Space-Efficient Substring Occurrence Estimation

Orlandi¹,

Venturini

2014

Algorithmica

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“…The width of the block is called the block size. (This technique is sufficient for our purposes, but see Frigo et al [7] for more clever methods.) Figure 4 shows the relative weight of all these issues.…”

Section: Concurrency and Data In All-pairsmentioning

confidence: 99%

Harnessing parallelism in multicore clusters with the All-Pairs, Wavefront, and Makeflow abstractions

Moretti

Thrasher

et al. 2010

Cluster Comput

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Both distributed systems and multicore systems are difficult programming environments. Although the expert programmer may be able to carefully tune these systems to achieve high performance, the non-expert may struggle. We argue that high level abstractions are an effective way of making parallel computing accessible to the non-expert. An abstraction is a regularly structured framework into which a user may plug in simple sequential programs to create very large parallel programs. By virtue of a regular structure and declarative specification, abstractions may be materialized on distributed, multicore, and distributed multicore systems with robust performance across a wide range of problem sizes. In previous work, we presented the All-Pairs abstraction for computing on distributed systems of single CPUs. In this paper, we extend All-Pairs to multicore systems, and introduce the Wavefront and Makeflow abstractions, which represent a number of problems in economics and bioinformatics. We demonstrate good scaling of both abstractions up to 32 cores on one machine and hundreds of cores in a distributed system.

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“…An online split strategy was previously used by Frigo et al [7] in a cache-oblivious algorithm setting.…”

Section: Lemma 1 (Han and Thorupmentioning

confidence: 99%

RAM-Efficient External Memory Sorting

Arge

Thorup

2015

Algorithmica

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In recent years a large number of problems have been considered in external memory models of computation, where the complexity measure is the number of blocks of data that are moved between slow external memory and fast internal memory (also called I/Os). In practice, however, internal memory time often dominates the total running time once I/O-efficiency has been obtained. In this paper we initiate a study of algorithms for fundamental problems that are simultaneously I/O-efficient and internal memory efficient in the RAM model of computation. For sorting the conventional wisdom is to use merging base algorithms in external memory but we describe how this leads to suboptimal RAM performance. However, by using a splitting based algorithm in combination with existing RAM sorting techniques we obtain a sorting algorithm that is both I/O and RAM model efficient. Furthermore, we design an I/O-and RAM-efficient priority queue. Finally, we prove a sorting lower bound that shows that in most cases our results are optimal both in terms of I/O and internal computation.

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Cache-Oblivious Algorithms

Cited by 144 publications

References 25 publications

Space-Efficient Substring Occurrence Estimation

Space-Efficient Substring Occurrence Estimation

Harnessing parallelism in multicore clusters with the All-Pairs, Wavefront, and Makeflow abstractions

RAM-Efficient External Memory Sorting

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