Efficient Deterministic Sorting on the BSP Model

Gerbessiotis, Alexandros V.; Siniolakis, Constantinos J.

doi:10.1142/s0129626499000098

Cited by 15 publications

(49 citation statements)

References 18 publications

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“…Sorting by deterministic oversampling and splitting into smaller subsets of about equal size is known to be achievable using the following idea [17,29,35]: Lemma 6.1. For numbers N, S, G and t one can find a set of S splitters in any ordered set X of N elements such that in the ordering on X the number of elements between two successive splitters is N/S ± 2tG by using the following procedure: Partition X into G sets of N/G elements each, sort each such set, pick out every tth element from each such sorted list, sort the resulting N/t elements, and finally pick every N/(t S)th element of that.…”

Section: Sortingmentioning

confidence: 99%

A bridging model for multi-core computing

Valiant

2011

Journal of Computer and System Sciences

135

126

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Writing software for one parallel system is a feasible though arduous task. Reusing the substantial intellectual effort so expended for programming a second system has proved much more challenging. In sequential computing algorithms textbooks and portable software are resources that enable software systems to be written that are efficiently portable across changing hardware platforms. These resources are currently lacking in the area of multi-core architectures, where a programmer seeking high performance has no comparable opportunity to build on the intellectual efforts of others. In order to address this problem we propose a bridging model aimed at capturing the most basic resource parameters of multi-core architectures. We suggest that the considerable intellectual effort needed for designing efficient algorithms for such architectures may be most fruitfully expended in designing portable algorithms, once and for all, for such a bridging model. Portable algorithms would contain efficient designs for all reasonable combinations of the basic resource parameters and input sizes, and would form the basis for implementation or compilation for particular machines. Our Multi-BSP model is a multi-level model that has explicit parameters for processor numbers, memory/cache sizes, communication costs, and synchronization costs. The lowest level corresponds to shared memory or the PRAM, acknowledging the relevance of that model for whatever limitations on memory and processor numbers it may be efficacious to emulate it. We propose parameter-aware portable algorithms that run efficiently on all relevant architectures with any number of levels and any combination of parameters. For these algorithms we define a parameter-free notion of opti-mality. We show that for several fundamental problems, including standard matrix multiplication, the Fast Fourier Transform, and comparison sorting, there exist optimal portable algorithms in that sense, for all combinations of machine parameters. Thus some algorithmic generality and elegance can be found in this many parameter setting.

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Section: Sortingmentioning

confidence: 99%

A bridging model for multi-core computing

Valiant

2011

Journal of Computer and System Sciences

135

126

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“…(2) A p-processor BSP algorithm for realizing n disjoint parallel-prefix operations, each of size p, over any associative operator whose application to two arguments take O(1) time, requires time at most C n A generic comparison-based parallel sorting algorithm that can sort any data type will also be utilized. We can either use the one-optimal randomized algorithm in [8] or the oneoptimal deterministic ones in [10,12] or the fully scalable but c-optimal one in [17]. The time required for a BSP sorting algorithm to sort n evenly distributed keys on p processors is denoted by T (n, p).…”

Section: The Bsp Model and Primitive Operationsmentioning

confidence: 99%

“…Its optimality depends on the sorting algorithm that is used. If one of [10,12,13] is used, then one optimality can be claimed for BUILDBSP_FH_STREE. These sorting algorithms however are not fully scalable; they require that n/p = lg 1+α n, where α > 0 or α > 1 depending on the algorithm.…”

Section: Parallel Segment Tree S(t ) For the First-hit Problemmentioning

confidence: 99%

An architecture independent study of parallel segment trees

Gerbessiotis

2006

Journal of Discrete Algorithms

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We study the computation, communication and synchronization requirements related to the construction and search of parallel segment trees in an architecture independent way. Our proposed parallel algorithms are optimal in space and time compared to the corresponding sequential algorithms utilized to solve the introduced problems and are described in the context of the bulk-synchronous parallel (BSP) model of computation. Our methods are more scalable and can thus be made to work for larger values of processor size p relative to problem size n than other segment tree related algorithms that have been described on other realistic distributed-memory parallel models and also provide a natural way to approach searching problems on latency-tolerant models of computation that maintains a balanced query load among the processors.

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“…Note also that in [9,10], Jaja and Helman say that their algorithm "can be easily implemented as a stable sort" and the articles are devoted to cluster systems, more precisely to multilevel hierarchical memory systems. References [11] and [12] are about sorting under the framework of BSP/CGM but authors do not provide any experimental results. A BSP code available at http: //www.…”

Section: Regular Sampling: An Efficient Technique For Sortingmentioning

confidence: 99%

Benchmarking Clusters of Workstations Through Parallel Sorting and BSP Libraries

Cérin

Gaudiot

2001

Parallel Process. Lett.

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We present our experiences in developping and tuning the performance at the user level, of (in core) parallel sorting on homogeneous and non homogeneous clusters with the use of the two available BSP (Bulk Synchronous Parallel model) libraries: BSPLib from Oxford university (UK) and PUB7 from the university of Paderborn (Germany). The paper is mainly about the communication performances of these two libraries and, in more general terms, it compares and summarizes the programming facilities and differences between them.

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Efficient Deterministic Sorting on the BSP Model

Cited by 15 publications

References 18 publications

A bridging model for multi-core computing

A bridging model for multi-core computing

An architecture independent study of parallel segment trees

Benchmarking Clusters of Workstations Through Parallel Sorting and BSP Libraries

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