Results 1  10
of
42
Approximate distance oracles
 J. ACM
"... Let G = (V, E) be an undirected weighted graph with V  = n and E  = m. Let k ≥ 1 be an integer. We show that G = (V, E) can be preprocessed in O(kmn 1/k) expected time, constructing a data structure of size O(kn 1+1/k), such that any subsequent distance query can be answered, approximately, in ..."
Abstract

Cited by 210 (8 self)
 Add to MetaCart
Let G = (V, E) be an undirected weighted graph with V  = n and E  = m. Let k ≥ 1 be an integer. We show that G = (V, E) can be preprocessed in O(kmn 1/k) expected time, constructing a data structure of size O(kn 1+1/k), such that any subsequent distance query can be answered, approximately, in O(k) time. The approximate distance returned is of stretch at most 2k − 1, i.e., the quotient obtained by dividing the estimated distance by the actual distance lies between 1 and 2k−1. A 1963 girth conjecture of Erdős, implies that Ω(n 1+1/k) space is needed in the worst case for any real stretch strictly smaller than 2k + 1. The space requirement of our algorithm is, therefore, essentially optimal. The most impressive feature of our data structure is its constant query time, hence the name “oracle”. Previously, data structures that used only O(n 1+1/k) space had a query time of Ω(n 1/k). Our algorithms are extremely simple and easy to implement efficiently. They also provide faster constructions of sparse spanners of weighted graphs, and improved tree covers and distance labelings of weighted or unweighted graphs. 1
Closest Pair Queries in Spatial Databases
 In Proceedings of the ACMSIGMOD Conference on Management of Data
, 2000
"... This paper addresses the problem of finding the K closest pairs between two spatial data sets, where each set is stored in a structure belonging in the Rtree family. Five different algorithms (four recursive and one iterative) are presented for solving this problem. The case of 1 closest pair is tr ..."
Abstract

Cited by 65 (9 self)
 Add to MetaCart
This paper addresses the problem of finding the K closest pairs between two spatial data sets, where each set is stored in a structure belonging in the Rtree family. Five different algorithms (four recursive and one iterative) are presented for solving this problem. The case of 1 closest pair is treated as a special case. An extensive study, based on experiments performed with synthetic as well as with real point data sets, is presented. A wide range of values for the basic parameters affecting the performance of the algorithms, especially the effect of overlap between the two data sets, is explored. Moreover, an algorithmic as well as an experimental comparison with existing incremental algorithms addressing the same problem is presented. In most settings, the new algorithms proposed clearly outperform the existing ones. 1
ClosestPoint Problems in Computational Geometry
, 1997
"... This is the preliminary version of a chapter that will appear in the Handbook on Computational Geometry, edited by J.R. Sack and J. Urrutia. A comprehensive overview is given of algorithms and data structures for proximity problems on point sets in IR D . In particular, the closest pair problem, th ..."
Abstract

Cited by 65 (14 self)
 Add to MetaCart
This is the preliminary version of a chapter that will appear in the Handbook on Computational Geometry, edited by J.R. Sack and J. Urrutia. A comprehensive overview is given of algorithms and data structures for proximity problems on point sets in IR D . In particular, the closest pair problem, the exact and approximate postoffice problem, and the problem of constructing spanners are discussed in detail. Contents 1 Introduction 1 2 The static closest pair problem 4 2.1 Preliminary remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Algorithms that are optimal in the algebraic computation tree model . 5 2.2.1 An algorithm based on the Voronoi diagram . . . . . . . . . . . 5 2.2.2 A divideandconquer algorithm . . . . . . . . . . . . . . . . . . 5 2.2.3 A plane sweep algorithm . . . . . . . . . . . . . . . . . . . . . . 6 2.3 A deterministic algorithm that uses indirect addressing . . . . . . . . . 7 2.3.1 The degraded grid . . . . . . . . . . . . . . . . . . ...
Accounting for memory bank contention and delay in highbandwidth multiprocessors
 In Proc. 7th ACM Symp. on Parallel Algorithms and Architectures
, 1997
"... Abstract—For years, the computation rate of processors has been much faster than the access rate of memory banks, and this divergence in speeds has been constantly increasing in recent years. As a result, several sharedmemory multiprocessors consist of more memory banks than processors. The object ..."
Abstract

Cited by 32 (5 self)
 Add to MetaCart
Abstract—For years, the computation rate of processors has been much faster than the access rate of memory banks, and this divergence in speeds has been constantly increasing in recent years. As a result, several sharedmemory multiprocessors consist of more memory banks than processors. The object of this paper is to provide a simple model (with only a few parameters) for the design and analysis of irregular parallel algorithms that will give a reasonable characterization of performance on such machines. For this purpose, we extend Valiant’s bulksynchronous parallel (BSP) model with two parameters: a parameter for memory bank delay, the minimum time for servicing requests at a bank, and a parameter for memory bank expansion, the ratio of the number of banks to the number of processors. We call this model the (d, x)BSP. We show experimentally that the (d, x)BSP captures the impact of bank contention and delay on the CRAY C90 and J90 for irregular access patterns, without modeling machinespecific details of these machines. The model has clarified the performance characteristics of several unstructured algorithms on the CRAY C90 and J90, and allowed us to explore tradeoffs and optimizations for these algorithms. In addition to modeling individual algorithms directly, we also consider the use of the (d, x)BSP as a bridging model for emulating a very highlevel abstract model, the Parallel Random Access Machine (PRAM). We provide matching upper and lower bounds for emulating the EREW and QRQW PRAMs on the (d, x)BSP.
Implementing Database Operations Using SIMD Instructions
, 2002
"... Modern CPUs have instructions that allow basic operations to be performed on several data elements in parallel. These instructions are called SIMD instructions, since they apply a single instruction to multiple data elements. SIMD technology was initially built into commodity processors in order to ..."
Abstract

Cited by 26 (3 self)
 Add to MetaCart
Modern CPUs have instructions that allow basic operations to be performed on several data elements in parallel. These instructions are called SIMD instructions, since they apply a single instruction to multiple data elements. SIMD technology was initially built into commodity processors in order to accelerate the performance of multimedia applications. SIMD instructions provide new opportunities for database engine design and implementation. We study various kinds of operations in a database context, and show how the inner loop of the operations can be accelerated using SIMD instructions. The use of SIMD instructions has two immediate performance benefits: It allows a degree of parallelism, so that many operands can be processed at once. It also often leads to the elimination of conditional branch instructions, reducing branch mispredictions.
Static Dictionaries on AC^0 RAMs: Query time Θ(,/log n / log log n) is necessary and sufficient
, 1996
"... In this paper we consider solutions to the static dictionary problem ���� � on RAMs, i.e. random access machines where the only restriction on the finite instruction set is that all computational instructions are ���� � in. Our main result is a tight upper and lower bound ���� � ���©���������������� ..."
Abstract

Cited by 19 (5 self)
 Add to MetaCart
In this paper we consider solutions to the static dictionary problem ���� � on RAMs, i.e. random access machines where the only restriction on the finite instruction set is that all computational instructions are ���� � in. Our main result is a tight upper and lower bound ���� � ���©��������������������� of on the time for answering membership queries in a set of � size when reasonable space is used for the data structure storing the set; the upper bound can be obtained using space ������ � �� � ���� �. Several variations of this result are also obtained. Among others, we show a tradeoff between time and circuit depth under the unitcost assumption: any RAM instruction set which permits a linear space, constant query time solution to the static dictionary problem must have an instruction of depth �������©���������������©���� � , where � is the word size of the machine (and ���© � the size of the universe). This matches the depth of multiplication and integer division, used in the perfect hashing scheme by Fredman, Komlós and Szemerédi.
Simulationbased Comparison of Hash Functions for Emulated Shared Memory
 In Proc. Parallel Architectures and Languages Europe, LNCS 694
, 1993
"... . The influence of several hash functions on the distribution of a shared address space onto p distributed memory modules is compared by simulations. Both synthetic workloads and address traces of applications are investigated. It turns out that on all workloads linear hash functions, although prove ..."
Abstract

Cited by 17 (4 self)
 Add to MetaCart
. The influence of several hash functions on the distribution of a shared address space onto p distributed memory modules is compared by simulations. Both synthetic workloads and address traces of applications are investigated. It turns out that on all workloads linear hash functions, although proven to be asymptotically worse, perform better than theoretically optimal polynomials of degree O(log p). The latter are also worse than hash functions that use boolean matrices. The performance measurements are done by an expected worst case analysis. Thus linear hash functions provide an efficient and easy to implement way to emulate shared memory. 1 Introduction Users of parallel machines more and more tend to program with the view of a global shared memory. Commercial machines (with more than 16 processors) however usually have distributed memory modules. Therefore the address space has to be mapped onto memory modules, memory access is simulated by packet routing on a network connecting ...
Error Correcting Codes, Perfect Hashing Circuits, and Deterministic Dynamic Dictionaries
, 1997
"... We consider dictionaries of size n over the finite universe U = and introduce a new technique for their implementation: error correcting codes. The use of such codes makes it possible to replace the use of strong forms of hashing, such as universal hashing, with much weaker forms, such as clus ..."
Abstract

Cited by 17 (2 self)
 Add to MetaCart
We consider dictionaries of size n over the finite universe U = and introduce a new technique for their implementation: error correcting codes. The use of such codes makes it possible to replace the use of strong forms of hashing, such as universal hashing, with much weaker forms, such as clustering. We use